shear modulus number (*KGe* ), as recommended in [20] (material models part). As can be seen in Figure 3, there is a good agreement between the simulations (UBCSAND model) and experimental results, regardless of the tailings types used. The index parameters *me*, *ne*, and *np* were assigned the values of 0.5, 0.5, and 0.4, respectively, as suggested in the original UBCSAND report by Beaty and Byrne [27]. The hydraulic conductivity of the tailings was adopted from previous studies [28,31]. In the present study, the hydraulic conductivity values in the horizontal direction were assumed to be 10 times higher than those in the vertical direction due to the layered nature of the tailings [26,31]. All input parameter values of the UBCSAND model used in the numerical analyses are tabulated in Table 2.

**Table 1.** Parameters of Mohr–Coulomb (M-C) model.

#### **Table 2.** Parameters of UBCSAND model.

#### **3. Simulation Results**

*3.1. Stability Analyses*

Stability analysis is an important component in the design of any earth structure, including tailings dams. Stability is usually expressed in terms of the factor of safety (*FoS*), which is defined as the ratio between the available shear strength of the soil and the minimum shear strength required against failure [12]. Figure 4 illustrates the factor of safety of the dam (pond filling rate of 5.0 m/year) during the pond filling, comprising raising phases (R) and consolidation phases (C). As shown, the *FoS* decreased gradually and dropped to less than 1.5 after the construction of three dikes of a height of 20 m from the initial pond filling level, i.e., *FoS* was 1.37 for R3 and 1.48 for C3. These values kept falling and even dropped to around 1.0 after the last construction stage. In addition, it was found that the *FoS* values decreased after every raising phase and then increased again due to the subsequent consolidation phase. This can be explained by the increase in effective stresses at the same rate as the excess pore pressure dissipates. The shear strength

increases gradually during the consolidation phase. In this sense, the lowest stability of the dam is expected immediately after a new dike construction (a raising phase). This observation is similar to that of many previous studies on the stability of an upstream tailings dam [7,12,21,33]. The possible failure mechanism of the dam is shown in Figure 5. The slip surface most likely associated with the lowest *FoS* occurred according to Figure 5. In this case, the *FoS* did not meet the adopted code requirements of the Swedish dam safety guidelines. Therefore, a slope strengthening was introduced in this study to maintain the factor of safety of 1.5 at every stage of construction.

**Figure 4.** Factor of safety (filling rate of 5.0 m/year) during the pond filling comprising both raising phases (R) and consolidation phases (C).

**Figure 5.** Failure mechanism of the upstream tailings dam (filling rate of 5.0 m/year).

Strengthening an upstream tailings dam with rockfill berms at the downstream slope is a common technique that has been introduced and used in many previous works [7,15,21,25,33]. In this study, the width of the rockfill was optimized. The rockfill width was increased in the models (with the rockfill widths of 5 m, 10 m, 15 m, and 20 m) until no *FoS* became smaller than approximately 1.5 at every stage. The factors of safety for all the cases can be observed in Figure 6. These observed factors of safety were higher than those from the upstream tailings dam without rockfill berms. The weight of the rockfill berms placed next to the dikes could provide a resisting moment which increases the slope stability. As expected in all the cases, the *FoS* values decreased after every raising phase and then increased again due to the subsequent consolidation phase, regardless of rockfill widths. This trend is found to be similar to that in Figure 4. In conclusion, the rockfill width of 20 m should be used to keep the dam stable enough (*FoS* ≥ 1.5) for the pond filling rate of 5.0 m/year.

**Figure 6.** Rockfill width optimization based on slope stability analysis (filling rate of 5.0 m/year).

#### *3.2. Excess Pore Water Pressure Analyses*

Figure 7 shows the excess pore water pressure at the last construction stage (filling rate of 2.5 m/year and rockfill width of 20 m). There was a relatively high excess pore pressure in some zones beneath the dikes after the raising phase R5 (Figure 7a). However, this pressure decreased significantly after the consolidation phase C5 (Figure 7b). The remaining high excess pore pressure after the consolidation phase C5 occurred in the lower part of the impoundment but further away from the dam. This is due to the presence of the impermeable base in the finite element model. The trend of the excess pore water pressure distribution observed in this study is similar to that reported by Ormann et al. [7] and Saad and Mitri [14], i.e., larger remaining excess pore pressure after a consolidation phase occurred in the lower part of the impoundment but farther away from the dam.

#### *3.3. Effects of the Pond Filling Rates on the Stability and Excess Pore Water Pressure*

Figure 8 shows the effect of the pond filling rates (*PFR*) on the stability of the upstream tailings dam with the rockfill width of 20 m during the pond filling, comprising raising phases (R) and consolidation phases (C). As shown, the *FoS* decreased very slightly when the *PFR* increased from 2.5 m/year to 5.0 m/year, regardless of construction phases. At the pond filling rate of 5.0 m/year, the factor of safety of the dam at every stage of construction fulfilled the adopted code requirement of the Swedish dam safety guidelines, except for the raising phase R5 (*FoS* at R5 was 1.472). However, when the *PFR* increased from 5.0 m/year to 10 m/year, the *FoS* suddenly dropped to 1.40 at the raising phase R5. Thus, the *FoS* did not satisfy the abovementioned requirement, which might be a sign of potential instability issues for the dam.

Figure 9 shows excess pore water pressure after the last consolidation phase C5 with respect to the pond filling rates. For the ease of interpretation, the shading views of excess pore water pressure in Figure 9 are fixed at the same color intervals, except for the model with the pond filling rate of 10 m/year. As shown, an effect of the pond filling rate on excess pore water pressure was discovered: an increase in pond filling rate can lead to an increase in excess pore water pressure in the model. In particular, the maximum excess pore water pressure in the model with the pond filling rate of 2.5 m/year was 29 kPa. This value rose up to 43 kPa, 68 kPa, and 138 kPa in the models with the pond filling rates of 3.3 m/year, 5.0 m/year, and 10 m/year, respectively. In cases of high pond filling rates, there was less time for the excess pore pressure to dissipate during consolidation.

**Figure 7.** Excess pore water pressure at the last construction stage with the pond filling rate of 2.5 m/year and rockfill width of 20 m: (**a**) after the raising phase R5 and (**b**) after the consolidation phase C5.

**Figure 8.** Effect of the pond filling rates (*PFR*) on the stability of the upstream tailings dam (rockfill width of 20 m).

**Figure 9.** Excess pore water pressure after the last consolidation phase with respect to the pond filling rates: (**a**) 2.5 m/year, (**b**) 3.3 m/year, (**c**) 5.0 m/year, and (**d**) 10 m/year.

In addition, excess pore water pressure which developed along cross sections A–A' and B–B' (defined in Figure 1b) are plotted in Figure 10. Excess pore water pressure increased with an increase in the *PFR*, regardless of the cross sections analyzed. Take the elevation of 5 m from the initial ground level as an example: at the section A–A', excess pore water pressure in the model with the pond filling rate of 2.5 m/year had the lowest value of 4.65 kPa, followed by 7.50 kPa (*PFR* = 3.3 m/year), 12.25 kPa (*PFR* = 5.0 m/year), and 27.25 kPa (*PFR* = 10 m/year). Similarly, at the cross section B–B' at 5 m height, excess pore water pressure in the models with the pond filling rates of 2.5 m/year, 3.3 m/year, 5.0 m/year, and 10 m/year were 7.5 kPa, 12.1 kPa, 19.6 kPa, and 43.1 kPa, respectively. Interestingly, the same trend was observed at both cross sections, even though excess pore water pressure at the cross section B–B' was higher than the corresponding excess pore water pressure at the cross section A–A'. It should be noted that cross section A–A' had a draining distance shorter than cross section B–B'. It was also found from Figure 10 that excess pore water pressure decreased with increased heights from the initial ground level, regardless of the *PFR* and sections. This is because of the presence of the impermeable base in the finite element model.

**Figure 10.** Excess pore water pressure after the last consolidation phase C5 with respect to the pond filling rates (*PFR*) at sections: (**a**) A–A' and (**b**) B–B' (defined in Figure 1b).

Finally, the build-up of excess pore pressure (*EPP*) was also considered in this study. Figure 11 shows the basic concept of excess pore water pressure build-up during the stage of construction at point B and *PFR* = 10 m/year. As shown, the excess pore water pressure increased after every raising phase and then decreased after every consolidation phase which followed. Again, this finding supports the abovementioned stability analyses, that is, the *FoS* values decrease after raising phases and then increase again after consolidation phases. More importantly, it is worth observing that *EPP* remained after every pond filling step (i.e., after consolidation phases C1, C2, C3, C4, and C5). The remaining *EPP* were built up until the last phase of consolidation C5. Based on this concept, the *EPP* build-up during the stage of construction with respect to pond filling rates (*PFR*) is presented in Figure 12. Typical points A and B (in Figure 1b) were selected for the analyses. As shown, the build-up of excess pore water pressure was dependent on the *PFR*. Take point B in Figure 12b as an example: when the *PFR* was small (i.e., *PFR* = 2.5 m/year), the remaining *EPP* were *EPP*C1 = 3.9 kPa, *EPP*C2 = 4.0 kPa, *EPP*C3 = 5.1 kPa, *EPP*C4 = 6.3 kPa, and *EPP*C5 = 7.6 kPa after the consolidation phases C1, C2, C3, C4, and C5, respectively. The remaining *EPP*, in this case, was built up, reaching only a small value of 7.6 kPa (*EPP*C5) after the last consolidation phase C5. However, higher values of *EPP*C5 were found when the *PFR* was higher (i.e., 12.0 kPa, 19.7 kPa, and 43.2 kPa for the cases with *PFR* = 3.3 m/year, *PFR* = 5.0 m/year, and *PFR* = 10.0 m/year). The same effect of the pond filling rates on the *EPP* build-up during the stage of construction can be observed in the results of point A (Figure 12a).

**Figure 11.** Concept of excess pore water pressure build-up during the stage of construction (point B and *PFR* = 10 m/year).

**Figure 12.** Effect of the pond filling rates (*PFR*) on the build-up of *EPP* during the stage of construction at: (**a**) point A and (**b**) point B (defined in Figure 1b).

#### **4. Discussion**

It is concluded by Ormann et al. [7] that the stability of an upstream tailings dam could be improved by utilizing rockfill berms as supports on the downstream side. The stability analyses obtained in this study agree with this conclusion; refer, for example, to Figures 4 and 6. In addition, it was found from this study that, in order to keep the stability of an upstream tailings dam, the exceptionally high pond filling rate is not recommended. However, the recommendation is only applicable in the case of 20 m rockfill width and the tailings type used in this study. In practice, larger rockfill width could be added next to dikes or on the downstream side to enhance the slope stability and hold the production

speed. In this case, key questions remain to be answered: (i) which design of rockfill berms is needed for different production speeds to maintain the stability of a tailings dam, and (ii) how can the volume of rockfill berms be minimized (i.e., for minimum cost) while still maintaining the stability and production speed?

In practice, an upstream tailings dam is typically constructed by placing dikes on top of previously deposited soft saturated tailings, which could lead to the excess pore water pressure gradually increasing during pond filling and dam raising. Therefore, an adequate pond filling rate is required to allow excess pore water pressure to dissipate gradually during the consolidation process. The numerical results from this study showed that excess pore water pressure increased in each raising phase and decreased in the subsequent consolidation phase. However, some of the excess pore water pressure remained after every consolidation phase (i.e., the build-up of excess pore water pressure) (Figure 11). The build-up excess pore water pressure varied depending on the pond filling rates. The dam with a high pond filling rate had a shorter time for the excess pore pressure to dissipate during every consolidation phase than the dam with low ones. Therefore, the highest value was found in the dam with the pond filling rate of 10 m/year in this study (Figure 12).

Even though the numerical results obtained are encouraging, some additional work will need to be performed in the future to simulate other conditions involved in the responses of an upstream tailings dam under cyclic and seismic loading.

#### **5. Conclusions**

In this study, a numerical analysis of an upstream tailings dam subjected to pond filling rates was conducted. A finite element software was used to simulate the timedependent pond filling process and the staged dam construction under various pond filling rates. An advanced constitutive model was adopted in the finite element models to capture the nonlinearity in the elastic deformations of the tailings materials. Based on the results of this study, the following conclusions can be drawn:

**Author Contributions:** Conceptualization, T.M.D.; J.L.; H.M.; methodology, T.M.D.; J.L.; software, T.M.D.; H.M.; validation, T.M.D.; J.L.; H.M.; Q.J.; formal analysis, T.M.D.; J.L.; H.M.; investigation, T.M.D.; J.L.; H.M.; writing—original draft preparation, T.M.D.; writing—review and editing, T.M.D.; J.L.; H.M.; Q.J.; supervision, J.L.; H.M.; Q.J.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Svenskt Vattenkraftcentrum, SVC ("The Swedish Hydropower Center").

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The research presented in this paper was carried out as a part of the Swedish Hydropower Center (Svenskt Vattenkraftcentrum, SVC). SVC has been established by the Swedish Energy Agency, Energiforsk, and Svenska Kraftnät, together with the Luleå University of Technology, KTH Royal Institute of Technology, Chalmers University of Technology, Uppsala University, and Lund University. The participating companies and industry associations are: Andritz Hydro, Boliden, Fortum Sweden, Holmen Energi, Jämtkraft, Karlstads Energi, LKAB, Mälarenergi, Norconsult, Rainpower, Skellefteå Kraft, Sollefteåforsens, Statkraft Sverige, Sweco Sverige, Tekniska verken i Linköping, Uniper, Vattenfall R&D, Vattenfall Vattenkraft, Voith Hydro, WSP Sverige, Zink-gruvan, and ÅF Industry. This research was funded by SVC and the Luleå University of Technology.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

#### **References**

### *Article* **Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques**

**Mahmood Ahmad 1, Paweł Kami ´nski 2,\*, Piotr Olczak 3, Muhammad Alam 4, Muhammad Junaid Iqbal 1, Feezan Ahmad 5, Sasui Sasui 6 and Beenish Jehan Khan 7**

**Abstract:** Supervised machine learning and its algorithms are a developing trend in the prediction of rockfill material (RFM) mechanical properties. This study investigates supervised learning algorithms—support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) for the prediction of the RFM shear strength. A total of 165 RFM case studies with 13 key material properties for rockfill characterization have been applied to construct and validate the models. The performance of the SVM, RF, AdaBoost, and KNN models are assessed using statistical parameters, including the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data (RSR). The applications for the abovementioned models for predicting the shear strength of RFM are compared and discussed. The analysis of the R2 together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R2 = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R2 = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R2 = 0.6233, NSE = 0.6180, RMSE = 0.3693, and RSR = 0.6181). Furthermore, the sensitivity analysis result shows that normal stress was the key parameter affecting the shear strength of RFM.

**Keywords:** AdaBoost; support vector machine; k-nearest neighbor; random forest; rockfill materials; shear strength

#### **1. Introduction**

Rockfill materials (RFMs) are commonly used in civil engineering projects such as rockfill dams, slopes, and embankments as construction materials for filling. This material is either obtained from a river's alluvial deposits or by blasting available rock [1,2]. RFMs are widely being used in the construction of rockfill dams to trap the river water because of their inherent flexibility, capacity to absorb large seismic energy, and adaptability to various foundation conditions. The behavior of RFMs used in rockfill dams is important for the safe and cost-effective construction of these structures. Generally, rockfill behaves

**Citation:** Ahmad, M.; Kami ´nski, P.; Olczak, P.; Alam, M.; Iqbal, M.J.; Ahmad, F.; Sasui, S.; Khan, B.J. Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques. *Appl. Sci.* **2021**, *11*, 6167. https:// doi.org/10.3390/app11136167

Academic Editors: Paulo José da Venda Oliveira and António Alberto Santos Correia

Received: 24 May 2021 Accepted: 1 July 2021 Published: 2 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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like a Mohr/Coulomb material, albeit without cohesion and with relatively high internal friction angles. Crushed rockfill, loosely layered, can behave like coarse sand. The shear strength of both types of RFM is affected by many factors such as mineral composition, surface structure, particle size, shape, relative density, individual particle strength, etc. [3–5]. Because of the variable jointing, angularity/roundness, and rock particle size distribution, the RFM can be considered the most complex material [6]. In order to know the mechanical properties of RFMs, extensive field and laboratory research is essential for understanding RFM behavior and determining shear strength parameters in order to design safe and cost-effective structures. In situ direct shear system was used to monitor the shear strength of RFM, as well as the variation in the shear strength of rockfill along with the fill lift [7]. Linero [8] carried out some large-scale shear resistance experiments to simulate the material's original grain size distribution and the expected load level. RFM with a large particle size (maximum particle size of 1200 mm) is incompatible in laboratory testing [9]. Owing to restricting the effects of large particle sizes on test apparatus, such behavior makes it much more difficult to design representative/realistic large-scale strength tests. Furthermore, determining the shear strength of RFM directly is considered a costly and difficult process. Large-scale shear tests are often time-consuming and complex, and estimating the nonlinear shear strength function without using an analytical method is difficult. As a result, several researchers have attempted to determine the mechanical properties of RFM using indirect methods based on machine learning (ML) techniques.

In recent years, several researchers used ML algorithms and achieved efficient successes in different civil engineering and other sectors such as environmental [10], geotechnical [11–18], and other fields of science [19–28]. Numerous researchers have documented the behavior of the RFM. Marsal [3], Mirachi et al. [4], Venkatachalam [5], Gupta [29], Abbas [30], and Honkanadavar and Sharma [31] carried out laboratory experiments on different rockfill materials and concluded that the behavior of stress-strain is nonlinear, inelastic and based on the level of stress. They also noted that with an increase in maximum particle size for riverbed rockfill material, the angle of internal friction increases, and a reverse pattern for quarried rockfill material is observed. Frossard et al. [32] proposed a rational approach for assessing rockfill shear strength on the basis of size effects; Honkanadavar and Gupta [9] developed power law to relate the shear strength parameter to some index properties of riverbed RFM. Describing the mechanical behavior of rockfill materials and challenges in large-scale strength tests have incited several approaches in modeling the respective behavior of such soils. In this context, the artificial neural network (ANN) approach used by Kaunda [33] needs fewer rockfill parameters and was found to be more efficient in predicting RFM shear strength. Zhou et al. [34] have recently used cubist and random forest regression algorithms and have found that both can deliver better predictive RFM shear strength results than ANN and conventional regression models. This field, however, continues to be further explored. Considering that large-scale strength tests to characterize the shear strength are challenging, ML algorithms based on support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) models are proposed. Furthermore, the ML algorithms SVM, RF, AdaBoost, and KNN have demonstrated excellent prediction efficiency in a variety of fields [35–39] because of their generalization capability. The application in civil engineering field more significantly in prediction of RFM shear strength is limited based on literature surveys.

The main intention of the present study is to explore the capability of using SVM, RF, AdaBoost, and KNN algorithms to establish a more precise and parsimonious behavioral model for predicting the RFM shear strength. A critical review of existing literature suggests that despite the successful implementation of these techniques in various domains, their implementation in the prediction of RFM shear strength is scarcely explored. One of the primary significances of this study is that the data division in the training and testing data sets has been made with due regard to statistical aspects such as maximum, minimum, mean, and standard deviation. The splitting of the data sets is made to determine the predictive capability and generalization performance of established models and later helps

to better evaluate them. Additionally, sensitivity analysis is carried out to find the main parameter influencing RFM shear strength. Concisely, the present study investigated and expanded the scope of machine learning algorithms for the development of the RFM shear strength model, which will provide theoretical support for researchers to establish a basis in selecting optimal machine learning algorithms in improving the predictive performance of RFM shear strength.

The rest of this article is structured as follows: The next section introduces the description of the used database and preliminaries of the algorithms used in the proposed approach and discusses the model evaluation metrics. Development of SVM, RF, AdaBoost, and KNN models are described in Section 3. Section 4 is dedicated to the performances and comparison of proposed models. Finally, Section 5 draws conclusions and outlines promising directions for future work.

#### **2. Materials and Methods**

#### *2.1. Data Set*

In this study, 165 samples of rockfill material (RFM) shear strength case history acquired by Kaunda [33] presented in Table A1 in the Appendix A were used to develop and evaluate the effectiveness of the proposed models. The RFM shear strength case history data are summarized in Table 1, where *D*10, *D*30, *D*60, and *D*90 correspond to the 10%, 30%, 60%, and 90% passing sieve sizes, *Cc* and *Cu* refer to coefficients of uniformity and curvature (*Cc*), respectively, *FM* and *GM* describe fineness modulus and gradation modulus, respectively, *R* represents ISRM hardness rating, *UCSmin* and *UCSmax* (MPa) indicate the minimum and maximum uniaxial compression strengths (MPa), *γ* is the dry unit weight (kN/m3), *σn* is normal stress (MPa), and *τ* is the shear strength of RFM (MPa) as the output variable. In this study, the output parameter selected to determine shear strength was the shear stress value at the failure of test samples and was the single output variable. The database was divided into two different sets, consisting of 80 percent (132 cases) and 20 percent (33 cases) of data, respectively, represented as training and testing sets. The testing set was accustomed to determine when training should be stopped in order to avoid overfitting. In order to achieve a consistent data splitting, different combinations of training and testing sets were experienced. The abovementioned selection was in such a way that the maximum (Max), minimum (Min), mean, and standard deviation of the parameters were consistent in the training and testing data sets (Table 2).

**Table 1.** Rockfill materials shear strength case history data.

**Table 2.** Statistical parameters of the training and testing data sets.

#### *2.2. Support Vector Machine*

Boser, Guyon, and Vapnik were the first to formulate and introduce the support vector machine (SVM) [40]. In the case of non-separable data, to accommodate errors for certain objects *i*, the "ideal boundary" must be introduced:

$$\begin{cases} \text{minimize} \left( \frac{1}{2} |\boldsymbol{\delta}|^2 + \mathbb{C} \sum\_{i=1}^n \boldsymbol{\xi}\_i \right) \\ \text{under the constraints } y\_i (\boldsymbol{b} + \boldsymbol{\delta} \cdot \boldsymbol{x}\_i) + \boldsymbol{\xi}\_i \ge 1 \text{ and } \boldsymbol{\xi}\_i \ge 0 \end{cases} \tag{1}$$

where *C* is the penetrating parameter; *δ* and *b* are, respectively, the normal vector and the bias of the hyperplane; and each *ξi* refers to the distance within object *i* and the respective margin hyperplane [41,42].

Data are implicitly mapped to a higher-dimensional space through mercer kernels, which can be broken down into a dot product to learn nonlinearly separable functions

*K xi*, *xj* = *ϕ*(*xi*)·*ϕ xj* [42]. The kernel of the radial basis function (RBF) that is used widely is listed below:

$$K(\mathbf{x}\_i, \mathbf{x}\_j) = \exp\left(-\sigma \|\mathbf{x}\_i - \mathbf{x}\_j\|^2\right) \tag{2}$$

where *σ* is the kernel parameter.

#### *2.3. Random Forest*

The use of a large series of low-dimensional regression trees is the basis of the random forest (RF). The theoretical development of RF is described by Breiman [43]. RF is an example of ensemble learning, which requires the development of a large number of decision trees to be implemented. In general, there are two types of decision trees: regression trees and classification trees. Regression trees were designed in the RF model since the main goal of this analysis was to predict the shear strength of RFM. Figure 1 depicts a general architecture for RF analysis. The protocol for analysis can be divided into two stages:

Stage 1: To create a sequence of sub-data sets, the bootstrap statistical technique is used to randomly sample from the initial data set (training data). The forest is then built using regression trees based on these sub-data sets. Each tree is trained by choosing a set of variables at random (a fixed number of descriptive variables selected from the random subset). Two important parameters that can be adjusted during the training stage are the number of trees (ntree) and the number of variables (mtry).

Stage 2: Once the model has been trained, a prediction can be made. In an ensemble approach, input variables are evaluated for all regression trees first, and then the final output is calculated by measuring the average value of each individual tree's prediction.

**Figure 1.** Schematic representation of RF analysis.

#### *2.4. AdaBoost Algorithm*

The sequential ensemble technique AdaBoost, or adaptive boosting, is based on the concept of developing many poor learners using different training sub-sets drawn at random from the original training data set. Weights are allocated during each training session, and these are used to learn each hypothesis. The weights are used to calculate the hypothesis error on the data set and are a measure of the relative importance of each

instance. After each iteration, the weights are recalculated so that instances classified wrongly by the previous hypothesis obtain higher weights. This allows the algorithm to concentrate on instances that are more difficult to understand. The algorithm's most important task is to assign updated weights to instances that were wrongly labeled. In regression, the instances represent a real-value error. The AdaBoost technique can be used to mark the calculated error as an error or not an error by comparing it to a predefined threshold prediction error. Instances that have made a greater mistake on previous learners are more likely (i.e., have a higher probability) to be chosen for training the next base learner. Finally, an ensemble estimate of the individual base learner predictions is made using a weighted average or median [44].

#### *2.5. k-Nearest Neighbor*

The supervised ML algorithm k-nearest neighbor (KNN) can be used to solve both classification and regression problems. It is, however, most commonly used in classification problems [45]. In regression problems, the input data set consists of *k* that is nearest to the training data sets deployed in the featured set. The output is dependent if KNN is deployed to function as a regression algorithm. For KNN regression, the ensuing result is the characteristic value for the object, which is the mean figure of *k*'s nearest neighbors. To locate the *k* of a data point, a parameter such as Euclidean, Mahalanobis can be used as the distance metric [46,47].

#### *2.6. Performance Metric*

The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and the ratio of the RMSE to the standard deviation of measured data (RSR) were taken into account to examine the predictive capacity of the models, as shown in Equations (3)–(6) [48–50]:

$$R^2 = \left[ \frac{\sum\_{i=1}^{n} \left( O\_i - \overline{O} \right) \left( P\_i - \overline{P} \right)}{\sqrt{\sum\_{i=1}^{n} \left( O\_i - \overline{O} \right)^2} \sqrt{\sum\_{i=1}^{n} \left( P\_i - \overline{P} \right)^2}} \right] \tag{3}$$

$$MSE = \frac{\sum\_{i=1}^{n} \left(O\_i - \overline{O}\right)^2 - \sum\_{i=1}^{n} \left(P\_i - O\_i\right)^2}{\sum\_{i=1}^{n} \left(O\_i - \overline{O}\right)^2} \tag{4}$$

$$RMSE = \sqrt{\frac{1}{n} \sum\_{i=1}^{n} (P\_i - O\_i)^2} \tag{5}$$

$$RSR = \frac{\sum\_{i=1}^{n} \left(O\_i - P\_i\right)^2}{\sqrt{\sum\_{i=1}^{n} \left(O\_i - \overline{O}\right)^2}}\tag{6}$$

where *n* is the number of observations under consideration, *Oi* is the *i*th observed value, *O* is the mean observed value, *Pi* is the *i*th model-predicted value, and *P* is the mean model-predicted value.

R-squared, also called the determination coefficient, describes the change in data as the degree of fit. The normal "determination coefficient" range is (0–1). The model is considered to be efficient if the R2 value is greater than 0.8 and is close to 1 [51]. The NSE is a normalized statistic that controls the relative extent of the residual variance relative to the variance of the data measured [52]. The NSE varies between −∞ and 1. When NSE = 1, it presents a flawless match among observed and predicted values. Model predictive output with a range of 0.75 < NSE ≤ 1.00, 0.65 < NSE ≤ 0.75, 0.50 < NSE ≤ 0.65, 0.40 < NSE ≤ 0.50, or NSE ≤ 0.4 is graded as very good, good, acceptable, or unacceptable, respectively [53,54]. The RMSE is the square root of the ratio of the square of the deviation between the observed value and the true value of the number of observations n. The RMSE has a value greater than or equal to 0, where 0 is a statistically perfect fit for the data observed [55–57]. The

RSR is interpreted as the ratio of the measured data's RMSE and standard deviation. The RSR varies between an optimal value of 0 and a large positive value. A lower RSR presents a lower RMSE, which indicates the model's greater predictive efficiency. RSR classification ranges are described as very good, good, acceptable, and unacceptable with ranges of 0.00 ≤ RSR ≤ 0.50, 0.50 ≤ RSR ≤ 0.60, 0.60 ≤ RSR ≤ 0.70, and RSR > 0.70, respectively [53].

#### **3. Model Development to Predict RFM Shear Strength**

The models for RFM shear strength prediction were developed using Orange software, which is a popular open-source environment for statistical computing and data visualization. All data processing is carried out using Orange software (version 3.13). The most prevalent supervised learning classification algorithms are given by Orange. In the package documentation manuals, one can find more information about input parameters, implementation, and references.

The structure of the model was based on an input matrix identified by predictor variables, *x* = {*D*10, *D*30, *D*60, *D*90, *Cc*, *Cu*, *GM*, *FM*, *R*, *UCS*min, *UCSmax*, *γ*, and *σn*} and output, also called target variable (*y*), was the RFM shear strength. In every modeling process, achieving a consistent data division and the appropriate size of the training and testing data sets is the most important task. The statistical features, such as the minimum, maximum, mean, and standard deviation of the data sets, have therefore been taken into account in the splitting process. The statistical accuracy of the training and testing data sets optimizes the performance of the models and ultimately helps to evaluate them better. On the remaining data set, the proposed models were tested. In other words, to build and test the models, 132 and 33 data sets were used, respectively. To fairly assess the predictive performance of the models, the data set used for the testing of all models was kept the same.

In order to optimize the RFM shear strength prediction, all the models (AdaBoost, RF, SVM, and KNN) were tuned based on the trial and error process. Initially, the values were chosen for model tuning parameters and gradually varied in experiments until the best fitness measurements were achieved. Figure 2 shows the schematic diagram of the proposed methodology. The optimization method aims to find the best parameters for AdaBoost, RF, SVM, and KNN in order to achieve the best prediction accuracy. Some critical hyperparameters in the AdaBoost, RF, SVM, and KNN algorithms are tuned in this study, as shown in Table 3. The definitions of these hyperparameters are also clarified in Table 3. The values for the tuning parameters of the models were first chosen and then varied in the trials until the best fitness measures mentioned in Table 3 were achieved.

**Table 3.** Hyperparameter optimization results.

**Table 3.** *Cont*.

**Figure 2.** The flowchart of the methodology.

#### **4. Results and Discussion**

In this study, R2, NSE coefficient, RMSE, and RMSE to standard deviation of measured data are chosen as the criterion for defining the model's output. The database is split into a training data set and a testing data set to evaluate the performance of the presented models. To make a fair comparison, all the models are developed by applying them to the same RFM shear strength training and testing data sets. Figure 3, displays the scatter plot of the actual and the predicted RFM shear strength for the training phase. The analysis of the R2 together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R2 = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R2 = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R2 = 0.6233, NSE = 0.6180, RMSE = 0.3693, and RSR = 0.6181).

Figure 4, presenting the predicted RFM shear strength, is plotted with the actual RFM shear strength data. According to the test data set, all models demonstrated very good predictive potential (R2 > 0.8) with the exception of KNN, which displayed slightly worse results (i.e., R2 = 0.6304) for the test data set. The result of R2 demonstrated that all SVM, RF, and AdaBoost models except KNN are appropriate, but the SVM model performed better because it had the highest R2 value (0.9656), and after that, the RF (0.9181) and AdaBoost (0.8951) models. In comparison to the other models, the KNN model presented the worst estimates with maximum dispersion (Figure 4).

**Figure 3.** Scatter plots of actual vs. predicted RFM shear strength in training stage: (**a**) SVM, (**b**) RF, (**c**) AdaBoost, and (**d**) KNN.

In addition, the NSE measure was ranked from highest to lowest predictive strength, following the way: SVM (0.9654) > RF (0.9164) > AdaBoost (0.8835) > KNN (0.6076), which is similar to R2. With regard to RMSE score, the SVM model also had the maximum predictive ability by having the lowest RMSE (0.0153), succeeded by the models RF (0.0797), AdaBoost (0.0941), and KNN (0.1727).

Finally, the reliability of all applied models was divided into four groups based on RSR values: unsatisfactory, satisfactory, good, and very good with ranges of RSR > 0.70, 0.60 ≤ RSR ≤ 0.70, 0.50 ≤ RSR ≤ 0.60, and 0.00 ≤ RSR ≤ 0.50, respectively. The RSR value therefore demonstrates very good results throughout all our established models except the KNN model, whose performance is considered to be satisfactory. Figure 5 depicts the bar graphs comparing the R2, NSE, RMSE, and RSR for the training and testing data sets of all the models. The R2 defines the degree of co-linearity between our predicted and actual data. The value of RMSE is more focused on large errors than on small errors. A lower RSR indicates a lower RMSE, indicating the model's better predictive efficiency. The SVM model has high R2 and NSE while lower RMSE and RSR values, revealing that the SVM model is preferable for predicting the RFM shear strength for the testing data. The SVM achieved a better prediction performance with (R2 = 0.9655, RMSE = 0.0513 and mean absolute error (MAE) = 0.0184) in comparison to the cubist method (R2 = 0.9645, RMSE = 0.0975, and MAE = 0.0644) and ANN method (R2 = 0.9386, RMSE = 0.1320 and MAE = 0.0841) reported by Zhou et al. [34] and Kaunda [33], respectively, for the test data. Additionally, the accuracy of modeling determined by the linear regression method reported by Andjelkovic et al. [58]

**Figure 4.** Scatter plots of actual vs. predicted RFM shear strength in testing stage: (**a**) SVM, (**b**) RF, (**c**) AdaBoost, and (**d**) KNN.

In the present research, a sensitivity analysis was also conducted using Yang and Zang's [59] method to evaluate the influence of input parameters on RFM shear strength. This approach has been used in several studies [60–63] and is formulated as:

$$r\_{ij} = \frac{\sum\_{m=1}^{n} (y\_{im} \times y\_{om})}{\sqrt{\sum\_{m=1}^{n} y\_{im} \sum\_{m=1}^{n} y\_{om}}} \tag{7}$$

where *n* is the number of data values (this study used 132 data values) and *yim* and *yom* are the input and output parameters. The *rij* value ranged from zero to one for each input parameter, and the highest *rij* values suggested the most efficient output parameter (which was RFM shear strength in this study). The *rij* values for all input parameters are presented in Figure 6. It can be seen from Figure 6 that the *σn* with *rij* is 0.990. Similar research of sensitivity analyses on RFM shear strength was also implemented by Kaunda [33] and Zhou et al. [34]. The findings demonstrated that normal stress is the most sensitive factor, which shows agreement with the present mentioned results.

Despite the fact that the proposed model produces desirable prediction results, certain limitations should be addressed in the future.

**Figure 5.** Comparison of R2, NSE, RMSE, and RSR values from the SVM, RF, AdaBoost, and KNN models in (**a**) training phase and (**b**) testing phase.

**Figure 6.** Sensitivity analysis results.

#### **5. Conclusions**

This study employed and examined the SVM, RF, AdaBoost, and KNN algorithms in the RFM shear strength prediction problem. To construct and validate a new model on the basis of the aforementioned algorithms, a comprehensive database containing 165 RFM case studies was collected from the available literature. Thirteen different predictive variables for rockfill characterization were selected as the input variables: *D*10 (mm), *D*30 (mm), *D*60 (mm), *D*90 (mm), *Cc*, *Cu*, *GM*, *FM*, *R*, *UCSmin* (MPa), *γ* (kN/m3), *UCSmax* (MPa), and *σn* (MPa). The predictive performance of the proposed models is verified and compared. The conclusions can be outlined as follows:

The findings show that the SVM model is a useful and accurate artificial intelligence technique for predicting RFM shear strength and can be used in various fields. Further, the generalization of the proposed approach for achieving improved performance results, more experimental data should be collected in future research. Finally, RFM shear strength prediction using advanced machine learning algorithms (i.e., deep learning) is left as a future research topic.

**Author Contributions:** Conceptualization, M.A. (Mahmood Ahmad); P.K. and P.O.; methodology, M.A. (Mahmood Ahmad), P.K.; software, M.A. (Mahmood Ahmad) and F.A.; validation, P.O., F.A., M.A. (Mahmood Ahmad), M.A. (Muhammad Alam) and F.A.; formal analysis, M.A. (Mahmood Ahmad); investigation, M.A. (Mahmood Ahmad), F.A., P.K., M.J.I. and S.S.; resources, P.K. and P.O.; data curation, M.A. (Mahmood Ahmad), M.A. (Muhammad Alam) and M.J.I.; writing—original draft preparation, M.A. (Mahmood Ahmad); writing—review and editing, M.A. (Mahmood Ahmad), B.J.K., F.A. and S.S.; supervision, P.K. and P.O.; project administration, P.K.; funding acquisition, P.K. and P.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data used to support the findings of this study are included within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Notation**

*Appl. Sci.* **2021** , *11*, 6167

**Table A1.** *Cont*.

**Table A1.** *Cont*.

#### **References**

### *Article* **Mechanical Properties of Cement-Treated Soil Mixed with Cellulose Nanofibre**

**Hidenori Takahashi 1,\*, Shinya Omori 2, Hideyuki Asada 2, Hirofumi Fukawa 3, Yusuke Gotoh 1 and Yoshiyuki Morikawa 1**

**Abstract:** Cellulose nanofibre (CNF), a material composed of ultrafine fibres of wood cellulose fibrillated to nano-order level, is expected to be widely used because of its excellent properties. However, in the field of geotechnical engineering, almost no progress has been made in the development of techniques for using CNFs. The authors have focused on the use of CNF as an additive in cement treatment for soft ground, where cement is added to solidify the ground, because CNF can reduce the problems associated with cement-treated soil. This paper presents the results of a study on the method of mixing CNF, the strength and its variation obtained by adding CNF, and the change in permeability. CNF had the effect of mixing the cement evenly and reducing the variation in the strength of the treated soil. The CNF mixture increased the strength at the initial age but reduced the strength development in the long term. The addition of CNF also increased the flexural strength, although it hardly changed the permeability.

**Keywords:** cement-treated soil; cellulose nanofibre; strength; permeability

87

#### **1. Introduction**

In recent years, it has become possible to produce cellulose nanofibre (CNF), which is a material composed of ultrafine fibres of wood cellulose fibrillated to nano-order level [1–3]. CNF is expected to be widely used because its strength is five times and its mass is one-fifth that of steel, and its thermal expansion is one-fiftieth that of glass. The characteristics of CNFs, such as high specific surface area, edibility, light weight, high strength, low thermal expansion, biodegradability, and biocompatibility, have been utilised to develop various applications. For example, transparent resins such as acrylics and epoxy resins are reinforced without much loss of their transparency (transparency reinforcement). The characteristics of light-weight and high-strength fibres are used to produce moulded products that are as strong as steel but one-fifth their mass by injecting phenolic resin into nanofibre sheets and then laminating and curing them. In addition, the thixotropy (the property that the fluidity increases when an external force is applied) of CNF solutions has already been used to commercialise ball-point pen inks, which prevent dripping and ease writing. In the field of concrete, the addition of CNFs to improve the strength of concrete has been studied [4–6]. This is attributed to its ability to accelerate the cement hydration reaction as well as to the high strength of CNF itself. The use of CNF as a stabiliser to prevent the separation of concrete compositions and bleeding before solidification, and as an additive to increase the flowability required in the pressure injection method using thixotropic properties, are being investigated and have been partly implemented.

**Citation:** Takahashi, H.; Omori, S.; Asada, H.; Fukawa, H.; Gotoh, Y.; Morikawa, Y. Mechanical Properties of Cement-Treated Soil Mixed with Cellulose Nanofibre. *Appl. Sci.* **2021**, *11*, 6425. https://doi.org/10.3390/ app11146425

Academic Editors: Paulo José da Venda Oliveira and António Alberto Santos Correia

Received: 17 June 2021 Accepted: 10 July 2021 Published: 12 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

In the field of geotechnical engineering, very little progress has been made in the development of techniques for using CNFs. The authors have focused on the use of CNF as an additive in cement treatment for soft ground, where cement is added to solidify the ground, for example, by the deep mixing method [7] to improve the ground and to construct geotechnical structures. This is because cement treatment is a relatively inexpensive and highly reliable ground improvement method. Although it is widely used, there are still some problems such as brittleness, low tensile strength, strength variability, and low shortterm strength. If more cement is added to solve these problems, the environmental impact will increase. If the long-term strength of the treated soil is too high, the treated soil could make rehabilitation of the geotechnical structure difficult. The possibility of reducing these problems using the CNF effect can be considered as follows: (1) The high strength of fibres increases the strength of the treated soil and reduces the risk of brittle or tensile failure. (2) As in concrete, CNFs accelerate the cement hydration reaction. (3) Owing to its water absorption and thickening properties, shear strength can be expected to develop immediately after stirring. (4) Thixotropy improves uniformity when stirring and mixing cement, and it ensures fluidity when pumping the treated soil in the slurry state before solidification. (5) Dispersibility increases uniformity during the stirring and mixing process. Previous studies have shown that fibrous materials of a few millimetres in length can be used for mixing with the treated soil [8–15]. The mixture of long fibres can have the effect described in (1) above, although not many other effects can be expected. When using existing ground improvement machines, such as deep mixing machines, it is difficult to stir long fibres into the treated soil, which presents a number of challenges for on-site use. Carbon nanotubes (CNTs) are similar to CNF, and research has been conducted on mixing them into the treated soil [16,17]. In these studies, the addition of these materials was found to increase the initial strength of the treated soil, even at the nano level. However, CNTs are poorly dispersible and require the addition of dispersants. By contrast, CNF is hydrophilic and highly dispersible in its own right, making it easy to mix directly with water or soil. This suggests that the use of CNFs in cement treatment is useful.

As mentioned above, the effect of mixing CNF can be predicted, but it is not simply a matter of mixing CNF. The method of mixing CNF and cement needs to be studied, because adding CNF in gel form requires more cement to solidify the soil, as water is also added. It is also not clear whether CNF of increasing plasticity can be stirred evenly. In contrast to concrete, the properties of soils are diverse and need to be carefully investigated to ensure that CNFs are effective. There are different types of CNFs as well as soil, and it is necessary to know which CNF should be combined with which soils. In the first place, it has not been demonstrated that mixing CNFs can solve the problems of cement-treated soil. Therefore, the authors' group has been studying the method of adding CNF in the cement treatment method, understanding the effect of mixing CNF, and investigating its use in an actual ground improvement site. This paper presents the results of a study on the method of mixing CNF, the strength and its variation obtained by adding CNF, and the change in permeability. Strength and permeability are important parameters because cement treatments are used to increase the strength of soft ground and impervious materials. If the study finds the mixing method and the improved properties in terms of strength and permeability, CNF will clearly be of great use in the field. Incidentally, the purpose of this study is not to clarify the mechanism of the effect of CNF but to investigate how to mix CNF and whether the effect of CNF can be observed in the solidification process.

#### **2. Materials and the Mixture Method**

#### *2.1. Characteristics of CNF Used*

There are many types of CNFs in terms of raw materials and manufacturing processes, and the ones used in this study were two typical commercially available CNFs (TEMPO-CNF and ACC-CNF). Two types of ACC-CNFs with different raw materials were used, which are referred to as ACC-CNF-S and ACC-CNF-B. The tests used these three types of CNFs and two states of TEMPO-CNF: a gel containing 2% CNF and a powder containing

90% CNF. The CNFs used are listed in Table 1. CNF is generally supplied in gel form mixed with water, although when mixed with soil to produce cement-treated soil, the soil includes a large quantity of water. The addition of water, which is included in the cement slurry, and CNF gel to the soil causes the soil to soften, and more cement is required to solidify the soft ground. Powdered CNF was also used in this study to investigate whether it might be possible to mix it with soil.

The production methods for the CNFs are different, and their degrees of fibrillation are also different. TEMPO-CNF is produced from wood pulp by a chemical defibration technique called TEMPO-mediated oxidation [18]. It is characterised by a very fine and uniform fibre width, which is as thin as approximately 3 nm. On the other hand, ACC-CNF is produced from bleached pulps of softwood or bamboo by a physical defibration technique called the aqueous counter collision (ACC) method [19]. CNF derived from soft woods is referred to as ACC-CNF-S, and that derived from bamboo is called ACC-CNF-B. These CNFs are characterised by their low chemical environmental impact as they are fibrillated using only water and raw materials, their amphiphilicity, and the concise control of the fibre width due to the gradual refinement of the fibre by repeated processing. The fibre width ranges from a few nanometres to several millimetres, depending on the number of treatments.

#### *2.2. Characteristics of Soil and Cement*

Two types of soil were used as base materials: Kasaoka clay, which is powdered clay manufactured from a mudstone in Okayama Prefecture, Japan, and Kawasaki clay, which is marine clay collected from the seabed of the Port of Kawasaki. The properties of each sample are listed in Table 2. Kasaoka clay is classified as high-liquid clay with a liquid limit of 62.1%. Kasaoka clay has few impurities and is uniform. Kawasaki clay is classified as low-liquid clay with a liquid limit of 41.0%. Kawasaki clay is a natural marine clay collected by dredging and contains impurities (e.g., small amounts of shells and gravel). A small amount of water was added as a pre-treatment to liquefy it, and it was passed through a 2 mm sieve to remove impurities before the cement treatment. Artificial seawater was added until the liquid limit ratio became 1.55.

**Table 2.** Physical properties of soils.

Three types of cement were used for the solidification process: ordinary Portland cement, high-early-strength Portland cement, and blast-furnace cement type B. Portland cement was used to determine the basic properties of the treated soil mixed with CNF. In Section 4, the results of the study on the strength variability of the treated soil are discussed by making an assumption about the treated soil in the field. Here, blast-furnace cement type B was used, assuming the soil in the field was treated. In Japan, this type of cement is frequently used for ground improvement in port and in harbour engineering. Blast-furnace cement type B is a relatively inexpensive cement that is prepared by adding blast-furnace slag powder to cement. It also has high durability against seawater and chemical substances, and low leaching of hexavalent chromium.

#### *2.3. Mixture Procedure of Soil, Cement, and CNF*

#### 2.3.1. Method 1

An attempt was made to add and mix CNF uniformly into the soil–cement mixture in the laboratory. This was performed to reduce the variation between the specimens and to confirm the effect of CNF in the treated soil mixed with CNF. Figure 1 shows the procedure used to prepare the specimens. Three types of CNFs in gel form (TEMPO-CNF, ACC-CNF-S, and ACC-CNF-B) were prepared and watered. Considering the possibility that the gelatinous material might not be mixed uniformly into the soil, water was added until it was no longer gelatinous to ensure uniform mixing. Cement slurry was also prepared by adding water to ordinary Portland cement. As shown in the mixture procedure (Figure 1), the CNF solution and cement slurry were mixed first and then added to and mixed with dry and powdered Kasaoka clay. The reason for mixing the powdered clay with the CNF solution and cement slurry is that if they were mixed with the wet soil, too much water would be present in the treated soil, and a large quantity of cement would be needed to solidify the soil. As this is not practical, the mixing of CNF and cement into wet clay was attempted using Method 2.

The soil mixer used for mixing was of 5 L capacity in accordance with JIS R5201 [20], with a mixing time of 5 min. Figure 2 shows the soil treated immediately after mixing. As shown in the figure, it can be observed that the flowability of the mixture without CNF is high, whereas that of the mixture with CNF is low. This was due to the water absorption and thickening properties of the CNFs. However, cement was considered to have been well mixed because of its high fluidity at a high strain rate near the agitator blades, which was induced by the thixotropic nature of CNF. The mixed soil was packed in unconfined compression test moulds (50 mm diameter × 100 mm height cylindrical) and cured in a constant-temperature curing room at 20 ± 2 ◦C and a humidity of more than 95% for a specified period.

**Figure 1.** Mixture procedure for preparing specimens (Method 1).

**Figure 2.** Appearance immediately after mixture by Method 1.

Table 3 shows the mixing proportions of cement, CNF, water, and soil for the treated soil prepared by Method 1. When CNF was added, the ratio of the dry weight of CNF to the dry weight of soil was defined as the CNF addition rate, *α*, which was set to *α* = 1.0%. The reason for setting *α* = 1.0% is that in a previous study, the effect of CNF was not clearly visible unless it was mixed with *α* = 1.0%. Cement was added in the ratio of approximately 100 kg dry weight per unit volume of soil mixed with all other materials except cement. The dry weight ratio of cement to soil was 13.8%. This was slightly less than the amount added in the deep mixing method, which is one of the most common ground improvement methods used on site. Cement was not added as a powder but as a cement slurry. The water–cement ratio was set to *w*/*c* = 7.0. Considering the water contained in all soil mixtures, the water content ratio was approximately 85%. The treated soil mixed using Method 1 was subjected to unconfined compression tests, and the test results are discussed in Section 3.

**Table 3.** Mix proportion of cement, CNF, water, and soil in Method 1: (**a**) Treated soil without CNF; (**b**) Treated soil with TEMPO-CNF (Gel); (**c**) Treated soil with ACC-CNF-S; (**d**) Treated soil with ACC-CNF-B.

#### 2.3.2. Method 2

As mentioned above, CNF in gel form was used in Method 1 with the aim of ensuring uniform mixing. However, water in the gel is added to the soil to increase the amount of CNF, and consequently, more cement is required to solidify the soil. To overcome this problem, dry powdered CNF was added to wet clay in Method 2 instead of the gel form of CNF. This is a more practical method for addition in the field. It is also practical to use high concentrations of CNF, even if it is difficult to produce powdered CNF. Figure 3 shows the mixture procedure for specimen preparation using Method 2. Powdered CNF (TEMPO-CNF) and cement (blast-furnace cement type B or high-early-strength cement) were mixed well in advance. Separately, Kawasaki clay was water-adjusted to obtain a liquid limit ratio of 1.55 or 1.95; then, they were mixed and stirred. For the specimens used in the permeability tests, ACC-CNF-S mixed with high-early-strength cement was also prepared, because the powdered ACC-CNF-S was not available. The gel form was used in this type of specimen.

**Figure 3.** Mixture procedure for preparing specimens (Method 2).

The soil mixer used for mixing was the same as that used in Method 1, and the mixing time was set to 1 or 5 min. A 5 min agitation makes the cement-treated soil uniform both with and without CNF. For this reason, the mixing time was shortened to 1 min for some specimens, which deliberately created a condition of insufficient mixing. It was easy to predict that without CNF mixing, stirring would be inadequate, and this test series was used to investigate whether the addition of CNF would promote uniform mixing of the cement. The specimens used in the permeability tests required uniformly treated soil. This was why a sufficient mixing time of 5 min was provided. Figure 4 shows the immediate aftermath of a short mixing time of 1 min for the specimen. In the figure, it was easily possible to visually distinguish between cemented and uncemented areas in the soil without the CNF mixture. On the other hand, in the soil mixed with CNF, there was no unevenness of mixing as far as could be visually observed, indicating the effect of the addition of CNF. This was attributed to the effects of the thixotropy, which facilitated mixing of the soil, and the homogeneous dispersion, which mixed cement well, although the water absorption and thickening properties of CNF reduced the flowability. The mixed soil was packed in unconfined compression test moulds (50 mm diameter × 100 mm height cylindrical) and cured in a constant-temperature curing room at 20 ± 2 ◦C and a humidity of over 95% for a specified period.

**Figure 4.** Appearance immediately after mixture by Method 2.

Tables 4 and 5 show the mixing proportions of cement, CNF, water, and soil for the treated soil prepared by Method 2. The tables show the proportions of the specimens for the tests to examine the strength variability and permeability tests, respectively. The ratio of CNF addition was set to *α* = 1.0%, which showed the effect of CNF, except for the soil treated with ACC-CNF-S, for which the ratio was set to *α* = 0.5%. This was because in this CNF, the powdered form was not available, and the gel form was used. The larger the amount added, the more water was added to the treated soil. Cement was added in the ratio of approximately 50 or 80 kg dry weight per unit volume of soil mixed with all other materials except cement. The dry weight ratio of cement to soil was 4.9% or 10.0%. These values are lower than the amounts added in the deep mixing method, especially the 4.9% cement addition, which was so small that the specimen could be easily broken by hand. By reducing the amount of cement in this way, the effect of CNF on cement mixing was more clearly visible. Considering the water contained in all the soil mixtures, the water content ratio was approximately 60% or 80%. Thirty specimens were prepared for each

proportion with and without CNF, because the variability was expected to be large in the specimens prepared with 1 min of mixing. The treated soil prepared with these proportions was subjected to unconfined compression tests and permeability tests, and the test results are discussed in Sections 4 and 5.

**Table 4.** Mix proportion of cement, CNF, water, and soil in Method 2 for evaluating variability: (**a**) Treated soil without CNF; (**b**) Treated soil with TEMPO-CNF (Powder).

**Table 5.** Mix proportion of cement, CNF, water, and soil in Method 2 for evaluating permeability: (**a**) Treated soil without CNF; (**b**) Treated soil with TEMPO-CNF (Powder); (**c**) Treated soil with ACC-CNF-S (Gel).

#### **3. Effect of CNF on Strength Increase**

*3.1. Unconfined Compressive Strength*

Unconfined compression tests were conducted on the specimens prepared by Method 1 to examine the strength of the treated soil. The method was in accordance with JIS A 1216 and JGS 0511 [21,22]. In the tests, a cylindrical specimen of 50 mm diameter × 100 mm height was compressed in the one-dimensional direction at a strain rate of 1%/min to investigate the axial compressive stress and strain. The strength of the soil was determined by taking the peak value of the relationship as the unconfined compressive strength. The tests were carried out 7, 28, and 112 days after preparing the specimens. The strength of treated soil is known to increase linearly with the logarithmic axis of time, and the specimens were cured for four times as many days each. The stress–strain relationship for each test case is shown in Figure 5. Figure 6 shows the time change of the unconfined compressive strength, together with the values normalised to the strength of the treated soil without CNF.

**Figure 5.** Relation between stress and strain in unconfined compression tests (Method 1).

**Figure 6.** Time change of unconfined compressive strength (Method 1).

The strength at the age of 7 days was 1.4–1.5 times higher in the soil treated with CNF than in the soil without CNF treatment, indicating the effect of CNF. The strength of the soil at 28 days of age was 1.1–1.3 times higher than that of the soil without CNF treatment, whereas the ratio of these strengths was smaller than that of the soil at the age of 7 days. At the age of 112 days, the trend was more pronounced, and the strength of the treated soil with TEMPO-CNF was lower than that of the treated soil without CNF. The strength of the soil treated with ACC-CNF was similar to that of the treated soil without CNF. This tendency can be clearly observed in Figure 6b, where the strength ratios of the treated soil without CNF are shown. It was found that the CNF mixture increased the strength at the initial age of the treated soil but reduced the strength development in the long term. This trend is more evident for the TEMPO-CNF. Increasing the initial strength and reducing the long-term strength is advantageous in cement treatment. As mentioned in Section 1, one of the challenges of cement treatment is the low short-term strength, and if the long-term strength is too high, the treated soil can hinder the rehabilitation of geotechnical structures. CNFs have the potential to solve these problems. It is not clear how the strength development was suppressed with age, although it was possible that in TEMPO-CNF, the carboxylic groups of CNF reacted with the calcium ions of cement and aggregated, suppressing the cement hydration reaction and reducing the increasing rate of long-term strength. On the other hand, the failure strains (axial strains at the peak value) of the soil treated with TEMPO-CNF were not very large, whereas those of the soil treated with ACC-CNF were large. The addition of ACC-CNF increased the failure strain by a factor of 2, indicating that some types of CNFs could mitigate the brittleness, which is a major problem in cement-treated soil.

The soil treated with CNF was photographed using an optical microscope. Figure 7 shows micrographs of the sheared surfaces (peeled and sliding surfaces) produced by the unconfined compression tests. In the photographs of the soil treated with CNF, no fibrous material could be observed in the TEMPO-CNF specimen, whereas it could be observed in the ACC-CNF specimen. The fibrous material was considered to be a CNF. TEMPO-CNF has a high degree of friability, which cannot be captured by optical microscopy, but a bunch of CNFs was seen in the void. Fibrous material could be observed at any given position in the ACC-CNF specimen, suggesting that the CNF was uniformly agitated. It was assumed that the uniformly mixed CNFs contributed to the strength increase. In addition, a number of small voids appeared in the soil treated with CNF, especially in the case of the TEMPO-CNF mixture. This was attributed to the reduced fluidity of the CNF mixture, which allowed air to enter the soil. A few small voids did not contribute significantly to the strength loss, and therefore would not affect the strength development much.

**Figure 7.** Appearance of sheared surface (Method 1).

#### *3.2. Flexural Strength*

One of the problems of cement-treated soil is its low tensile and flexural strengths. As it was anticipated that the CNF mixture would improve these properties, some tests were conducted to investigate the flexural strength. Since the flexural failure of treated

soil depends on its tensile strength, its flexural strength can indirectly indicate the tensile strength of the treated soil. The method was implemented in accordance with JIS R 5201 [20]. In this test, a prismatic specimen (40 mm high × 40 mm deep × 160 mm long) was placed on support rolls placed at a distance of 100 mm, and the roll was loaded in one direction at the centre of the specimen at a loading rate of 50 N/s. Then, the relationship between bending stress and deflection was investigated, and the peak value was taken as the flexural strength. Figures 8 and 9 show the relationship between the bending stress and deflection for each test case and the micrographs of the tensile fracture surfaces, respectively.

**Figure 8.** Relation between bending stress and deflection in bending strength tests (Method 1).

**Figure 9.** Appearance of tensile fracture surface (Method 1).

The flexural strength of the specimens without CNF was approximately 400–470 kN/m2, which was 25–50% of the unconfined compressive strength, regardless of the age of the specimens. Previous studies [23–25] showed that the flexural strength was approximately 20–50% of the unconfined compressive strength (on average around 30%), and our results were consistent with these findings. The flexural strength of the specimens with TEMPO-CNF

was 590 kN/m2 (at 28 days of age) and 810 kN/m2 (at 112 days of age), indicating that the flexural strength increased with the addition of CNF. The ratio of flexural strength to unconfined compressive strength was as large as 50–65%, which was not the case for normal cement-treated soil. The increase in flexural strength can be attributed to the increase in the tensile strength. The flexural strength of the specimens with ACC-CNF was 560 kN/m2 (at 28 days of age) and 660 kN/m2 (at 112 days of age), and the ratio to the unconfined compressive strength was 33–56%. The addition of ACC-CNF also increased the flexural strength, although not as much as that of TEMPO-CNF. The low flexural and tensile strength of the treated soil is frequently a problem in the field, and the addition of CNF was found to be a possible solution to this problem. The micrographs showed fibrous material in the ACC-CNF specimen, as well as in the sheared surfaces from the unconfined compression test, while the TEMPO-CNF specimen showed voids and material that could be CNF in the void.

#### **4. Effect of CNF on Strength Variability**

#### *4.1. Dispersion of Unconfined Compressive Strength*

CNF has dispersive properties that make substances in liquids homogeneous, and this property has been considered for use in a variety of applications. CNF is also expected to improve the dispersibility of cement when it is mixed with the soil, and its thixotropy property increases the uniformity of cement. The uniform mixing of cement with soil could reduce the variation in strength. This section presents the test results regarding the effect of CNF mixing on the strength variability. It is important to consider variability, as a reduction in the strength variability can reduce the design strength. Figure 10 shows the relationship between the frequency distribution of intensity occurrences and the design strength. During design, the percent defective is determined at the beginning, and the mean strength (design strength) is determined so that it is satisfied. As shown in the figure, the smaller the variance in strength, the lower the mean strength for the same percent defect. The advantages of reducing the strength and dispersion are significant. This reduces the risk that large dispersals will result in treated soil of extremely high strength as well as high cost of cement and high environmental impact of cement production. As mentioned before, large-strength treated soil can hinder the rehabilitation of geotechnical structures.

**Figure 10.** Conceptual diagram of frequency distribution of strength and design strength.

Unconfined compression tests were conducted at 28 days of age on a total of 60 specimens prepared by Method 2 with a short mixing time of 1 min. The axial compressive stress–strain relationships obtained from the tests are shown in Figure 11a,b. Figure 11c shows the results of the unconfined compression test of the treated soil used for the permeability test (preparation method: Method 2, mixing time: 5 min), which is discussed later in Section 5. Most of the peak strengths (unconfined compressive strengths) of the specimens without CNF were below

50 kN/m2, which was quite low. By contrast, the peak strengths of the specimens with CNF were relatively higher, with unconfined compressive strengths ranging from 100 to 350 kN/m2. Comparing the mean values, the unconfined compressive strength was 43 kN/m2 (without CNF) and 219 kN/m2 (with CNF); the strength ratio was significantly larger: 5.1 times. As the strength ratio of the well-mixed specimens with and without CNF at 28 days of age was 1.1–1.3 times, as mentioned in Section 3, the large strength ratio of 5.1 indicates that the addition of CNF caused the cement to mix evenly with the soil. The failure strains of the specimens with CNF were relatively low owing to the higher strength of the specimens. The average failure strains were 3.9% (without CNFs) and 1.4% (with CNFs).

**Figure 11.** Relation between stress and strain in unconfined compression tests (Method 2).

Figure 12 shows photographs of the sheared surfaces generated by the unconfined compression tests. The sheared surface of the specimen without CNF showed that the solidified portions were scattered within the poorly solidified soil. The unconfined compressive strength was extremely low, which was probably because the solidified portions were not continuous. On the other hand, the sheared surface of the specimen with CNF appeared to be homogeneous, suggesting that the cement spread over the whole specimen, and that this condition led to the development of a relatively high unconfined compressive strength.

**Figure 12.** Appearance of sheared and trimmed surfaces (Method 2).

#### *4.2. Evaluation of Variability*

Here, variations in the unconfined compressive strength and failure strain were discussed. Figures 13 and 14 show the frequency distributions around the mean values of the unconfined compressive strength and failure strain of the specimens, respectively. Although the strengths of the specimens without CNF were generally low, some specimens developed significant strength, resulting in a strength coefficient of variation of 0.56. On the other hand, the variation coefficient of the strength of the specimens with CNF was

0.33, and the frequency distribution was in the range of 2*σ*, as shown in the figure. Thus, it was confirmed that the addition of CNF reduced the variation in strength in terms of the coefficient of variation of strength. The coefficients of variation of failure strain for the specimens without and with CNF were 0.51 and 0.26, respectively. For failure strain too, the addition of CNFs reduced the variability. The density and water content of the specimens were also measured, and no differences were observed between the specimens with and without CNF. This suggests that there was no difference in the properties of the base soil or the amount of cement added between the specimens with and without CNF, indicating that CNF had an effect on the strength of the specimens.

**Figure 13.** Frequency of appearance of unconfined compressive strength.

**Figure 14.** Frequency of appearance of failure strain.

In this study, the amount of cement added was reduced so that the effect of CNF on cement mixing could be more clearly expressed. Considering the strength of the soil treated by the deep mixing method, the amount of cement was small. It will be important in the future to compare the variability of the specimens with increasing cement content. However, there are some methods that produce treated soil with lower strength than the deep mixing method. Examples include the pre-mixing method [26] and the pneumatic flow mixing method [27]. In these methods, the soil is mixed with cement in a mixer or in a pneumatic flow in a pipe and poured in situ before solidification occurs. Strengths of 100–400 kN/m2 at 28 days of age are generally expected with cement additions of approximately 50–100 kg/m3. The amount of cement added and the expected strength were similar to those in this study. If the mixing time is short for some reason, the soil could be extremely weak, as described above. Mixing CNFs could prevent such problems.

#### **5. Effect of CNF on Permeability**

#### *5.1. Test Method*

In addition to the strength of the treated soil, its permeability was also investigated. Previous studies showed that the permeability of clayey soils decreases when they are mixed with cement [28–30]. The treated soil is occasionally used as an impervious material, and permeability is one of the most important aspects of the treated soil. In addition, a high permeability indicates that the calcium ions in the treated soil can easily diffuse to the outside, and therefore, permeability is an important parameter for evaluating the deterioration of the treated soil. It is known that treated soils start degrading from the exposed surface [31–36], which is one of the points that must be understood when using cement treatment in the field. This section discusses how the permeability changes with the addition of CNFs.

The specimens prepared using Method 2 were cured for more than 200 days. The unconfined compression test results of the specimens are shown in Figure 11c. As part of the specimen was used for the permeability test, the unconfined compression tests in this study did not use specimens of the size specified in JIS A 1216 and JGS 0511 [21,22]. It should be noted that because the compression tests were conducted on small prismatic specimens (15 mm wide × 15 mm deep × 30 mm high), the test results could contain errors. All other methods were implemented in accordance with the standards. The unconfined compression test results showed that the strength of the specimens with CNF was lower than that without CNF. This was in agreement with the trend of the results described in Section 3, where it was found that the addition of CNF reduced the long-term strength.

The remainder of the treated soil in the unconfined compression test was subjected to a permeability test. As there is no standard method for permeability testing of treated soil, the tests were conducted using the method utilised in the past study [37]. Figure 15 shows a conceptual diagram of the test apparatus. In this test method, a cylindrical specimen with a diameter of 50 mm and height of 30 mm was permeated by percolating water above the specimen, applying confining pressure from the side through a membrane. The lateral confining pressure prevented the upper pressurised water from leaking at the side of the specimen. The water pressure above the specimen was 400 kN/m2, and the average hydraulic gradient inside the specimen increased to 1361. This was because the permeability of the treated clayey soil was low, and the hydraulic gradient had to be increased to increase the drainage rate. The drainage water that passed through the specimen was collected to determine the permeability per unit time. The pH, calcium ion concentration, and sodium ion concentration were measured periodically in the collected effluent. The permeability period depended on the specimen: 114 days for 'no CNF', 114 days for TEMPO-CNF, and 49 and 97 days for ACC-CNF-S, respectively. The reason for the long-term measurements was to check whether the addition of CNF would change the condition of the effluent, as it is known that continued permeability degrades the treated soil.

**Figure 15.** Schematic view of test apparatus.

#### *5.2. Test Results*

Figure 16 shows the drainage rate per unit time and the chemically measured values for each day. Note that these values indicate the effluent collected at the time of measurement and not the accumulated effluent. The drainage rate per unit time, which indicates the permeability, showed that the permeability of the specimens with TEMPO-CNF was approximately 35% lower and that of the specimens with ACC-CNF was approximately 25% higher, depending on the type of CNF. It was not simply that the CNF itself clogged the pores and reduced the permeability. Figure 17 shows scanning electron microscopy (SEM) images of the permeable and non-permeable specimens. As shown in the images, the specimen with ACC-CNF-S contained fibrous material that could be considered as CNF, and the fibrous material formed a network. On the other hand, CNF did not appear in the specimen with TEMPO-CNF, because TEMPO-CNF has a high degree of fibrillation and cannot be observed at a magnification of 500 times. In any case, the amount of CNF added was found to be small and insufficient to close the pore space. The pore conditions of the soil particles were similar in all cases, and the reason for the change in permeability could not be determined from the SEM photographs. The reason for the change in permeability is not understood, although CNF may have influenced the cement hydration reaction, changing the amount of hydrate formed, or the hydrophilicity of the CNF itself may also have affected the permeability. Incidentally, a difference of 25–35% is not generally considered to be significant, as the permeability frequently varies by orders of magnitude depending on the pore conditions and the amount of cement added. It is necessary to conduct more tests on more specimens to improve the accuracy of the results. In other words, it could be said that the addition of CNF did not change the permeability significantly, at least on a per-digit basis.

The pH, calcium ion concentration, and sodium ion concentration of the drainage water were also measured and are shown in Figure 16. The drainage was the result of the pore water in the treated soil being pushed out, which indicates the state of the pore water. The pH values were similar, indicating that the addition of CNF did not change the alkaline atmosphere of pore water, and that the environment in all specimens was conducive to cement solidification. The concentration of calcium ions was higher in the TEMPO-CNF specimens. The low permeability of the specimens may have resulted in a higher supply of calcium from the cement hydrate to pore water, or the calcium bound to the carboxylic groups may have been more easily leached than the cement hydrate. Sodium ions were detected early, but not thereafter. The fact that the base soil was marine clay and that the water content ratio was adjusted with artificial seawater suggested that the treated soil contained sodium. The concentration of sodium ions in the specimens with TEMPO-CNF was higher than that in the specimens without TEMPO-CNF, suggesting that the sodium bound to the carboxylic groups may have leached out. The effect of CNF was not apparent in terms of the chemical properties of the effluent.

**Figure 16.** Chemical properties of drained water.

**Figure 17.** SEM microphotograph of specimens.

#### **6. Conclusions**

This study investigated methods of mixing CNF, the increase in strength and the reduction of variability by adding CNF, and the change in permeability. The results can be summarised as follows:

Thus, the addition of CNF to cement treatment had beneficial effects, depending on how it was used. This result is a quantum leap forward in encouraging the use of CNF in cement-treated soil. However, at present, the production volume of CNFs is limited, and their unit price is high. When CNFs are used in large quantities for civil engineering, the production volume will increase, and the price will decrease significantly. In practice, the price must be reduced before CNFs can be utilised. In addition, it is important to conduct trial mixing tests before the actual addition of CNF in the field, as the degree of effectiveness and properties may vary depending on the type of soil and the type of CNF. Moreover, if the chemical mechanism, although complex, can be clarified, it will be possible to add CNF more reliably to achieve the required effect. More detailed investigations of properties other than strength and permeability will be conducted in the future.

#### **7. Patents**

This paper includes the contents of the following patents applied in Japan: Manufacturing method of ground improvement soil and ground improvement method (JP2021011728A, JP2021011729A).

**Author Contributions:** Project administration and writing—original draft preparation, H.T.; data curation, S.O.; validation, H.A.; investigation, H.F.; investigation, Y.G.; validation, Y.M.; writing—review and editing, All authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank DKS Co. for the supply of TEMPO-CNFs and Marubeni Corporation and Chuetsu Pulp & Paper Co. for the supply of ACC-CNFs. In addition, the authors wish to thank Yosuke Tanaka (Toa Corporation) for his supports during the tests.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

### *Article* **Novel Approach for Suppression of Ettringite Formation in Sulfate-Bearing Soil Using Blends of Nano-Magnesium Oxide, Ground Granulated Blast-Furnace Slag and Rice Husk Ash**

**Khaled Ibrahim Azarroug Ehwailat, Mohd Ashraf Mohamad Ismail \* and Ali Muftah Abdussalam Ezreig**

School of Civil Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Seberang Prai Selatan, Pulau Pinang, Malaysia<EMAIL_ADDRESS>(K.I.A.E<EMAIL_ADDRESS>(A.M.A.E.) **\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** The treatment of sulfate-bearing soil with calcium-based stabilizers such as cement or lime often results in ettringite formation, consequently leading to swelling and strength deterioration. Ettringite formation has negative environmental and economic effects on various civil engineering structures. This study was conducted to investigate the use of different materials (nano–magnesium oxide (M), ground granulated blast-furnace slag (GGBS), and rice husk ash (RHA)) for gypseous soil stabilization to prevent ettringite formation. Various tests were performed, including flexural strength, unconfined compression strength, linear expansion, and microstructure analysis (SEM/EDX), on lime (L)-, (M)-, (M-RHA)-, (M-GGBS)-, and (M-GGBS-RHA)-stabilized gypseous soil samples to determine their properties. The results indicated that the swelling rates of the soil samples mixed with 20% M-RHA, M-GGBS, and M-GGBS-RHA binders were much lower (less than 0.01% of volume change) than those of the soil samples mixed with 10% and 20% lime-stabilized binders after a curing period of 90 days. Meanwhile, the strengths of the soil samples mixed with 20% of M-RHA, M-GGBS, and M-GGBS-RHA soil specimens after soaking of 90 days were obviously higher (with a range from 2.7–12.8 MPa) than those of the soil samples mixed with 20% of lime-stabilized binder. The SEM and EDX results showed no ettringite formation in the M-RHA-, M-GGBS-, and M-GGBS-RHA-stabilized soils. Overall, the test results proved the potential of M-RHA, M-GGBS, and M-GGBS-RHA as effective soil stabilizers.

**Keywords:** sulfate-bearing soil; ettringite; soil stabilization; gypseous soil; calcium-based stabilizer; non-calcium-based stabilizer

#### **1. Introduction**

Gypseous soils are soils containing significant amounts of calcium sulfates (CaSO4 × 2H2O). Such soils are widely distributed in many countries, such as the US, UK, India, Africa, and Australia [1,2]. Gypseous soils are common in highway pavement construction, but they often pose many problems, such as cracking, collapsing, and pavement layer settlement, thereby making pavement and highway construction on such soil a challenge to geotechnical engineers [1,3,4]. One of the proven economical ways to improve the engineering properties of soil samples is via chemical soil stabilization [5–7]. The most common soil stabilization method is the addition of cement or lime to increase the soil's durability and strength [1,3–5,8–11]. However, adverse effects of ground heave have been reported when using cement and lime to stabilize sulfate-rich soil. Sulfate-induced heave is well known to have serious negative impacts on the general performance of runways, highways, and other structures that are constructed on lime- or cement-stabilized sulfaterich soil layers [1,4,11]. The application of lime or cement on sulfate-rich soils frequently elicits a series of reactions that result in the formation of ettringite [Ca6Al2(SO4)3(OH)12 × 26H2O], which is an expansive sulfate-mineral-based compound [2,4,12]. Ettringite contains 26 water molecules and can resist expansive increases in volume when exposed to

**Citation:** Ehwailat, K.I.A.; Ismail, M.A.M.; Ezreig, A.M.A. Novel Approach for Suppression of Ettringite Formation in Sulfate-Bearing Soil Using Blends of Nano-Magnesium Oxide, Ground Granulated Blast-Furnace Slag and Rice Husk Ash. *Appl. Sci.* **2021**, *11*, 6618. https://doi.org/10.3390/ app11146618

Academic Editors: Paulo José da Venda Oliveira and António Alberto Santos Correia

Received: 8 June 2021 Accepted: 11 July 2021 Published: 19 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

hydration, thereby leading to a significant heave effect, swelling, and decline in compressive strength [13]. Many types of equipment and tests have been utilized to determine the negative impact of sulfate on soil stabilization. The most common equipment and tests are unconfined compressive strength tests used to reveal the influence of ettringite on the strength of soil samples containing sulfate, and linear expansion to observe the magnitude of swelling induced by ettringite formation. Adeleke et al. [4] demonstrated understanding of the mechanisms of sulfate-bearing soil when stabilized with a calcium-based stabilizer. Unconfined compressive strength and linear expansion tests were conducted on gypseous soil containing a high level of calcium sulfate (10%, 15%, and 20%) treated with 7%, 8%, 9%, and 10% calcium-based stabilizer (cement). The outcomes illustrated that the strength reduced by a factor range of 6–47% at 28 days of curing, while the swelling magnitude depended mainly on the sulfate concentration, with the highest rate of swelling observed in the presence of sulfate at 10%.

Treatment of sulfate-containing soils (SO4) with calcium-based stabilizers encourages a reaction between the calcium content of the stabilizer (lime or cement) and the sulfate/aluminum content of the soil. With the presence of water, this generates large quantities of ettringite (a crystalline mineral), causing further changes in volume and damage to the structures built on such soil (see Equation (1)) [2,14,15].

$$2\text{6Ca}^{2+} + 12\text{OH}^- + 2\text{Al}^{3+} + 3\text{SO}\_4^{2-} + 2\text{6H}\_2\text{O} \rightarrow \text{Ca}\_6\text{Al}\_2(\text{SO}\_4)\_3(\text{OH})\_{12} \times 2\text{6H}\_2\text{O} \tag{1}$$

Ettringite can transform into another expanding mineral: thaumasite[Ca6Si2(CO3)2(SO4)2(OH)12 × 24H2O]. The transformation occurs due to the replacement of aluminum and sulfate with silica and carbonate at temperatures < 15 ◦C (see Equation (2)) [2,12].

$$\begin{array}{ll} \text{Ca}\_6[\text{Al(OH)}\_6]\_2 \times & (\text{SO}\_4)\_3 26 \text{H}\_2\text{O} + 2 \text{H}\_2 \text{SiO}\_4^{2-} + 2 \text{CO}\_3^{2-} + \text{O}\_2\\ & \rightarrow \text{Ca}\_6[\text{Si(OH)}\_6]\_2 \times (\text{SO}\_4)\_2 \times (\text{CO}\_3)\_2 \times 24 \text{H}\_2\text{O} + 2 \text{Al(OH)}\_4^- + \text{SO}\_4^{2-} + 4 \text{OH}^- + 2 \text{HO}\_2 \end{array} \tag{2}$$

Chemical modification remains the most effective method of soil stabilization. Among the available chemical admixtures, the most extensively used for soil stabilization is lime [16]. Aldawood et al. [1] performed unconfined compressive strength, X-ray diffraction, and scanning electron microscopy tests on gypseous soil (with different gypsum amounts: 5%, 15%, and 25%) to investigate the influence of lime applied at different dosages (0%, 3%, 5%, and 10%) to stabilize sulfate-bearing soil. The results noted that the mechanical properties of soil specimens were not only affected by the curing time, but also depended on the lime dosage. The optimum reduction in strength was detected to be 5%, and the results of micro tests confirmed the ettringite formation and the pozzolanic products. Researchers and engineers are now progressively exploring novel materials that can serve as soil stabilizers to overcome the lime-induced heave problems often encountered in sulfate-bearing soils during the construction of civil structures. Novel methods should be able to repress heave and ettringite formation in sulfate-rich soils. Materials that exhibit pozzolanic properties, such as rice husk ash (RHA) and ground granulated blast-furnace slag (GGBS), are believed to be suitable sulfate soil stabilizers because such materials generally consume lime [17], thereby reducing its availability for the formation of expansive products while improving the strength of the soil [18].

Annually, millions of tons of industrial and agricultural waste are produced globally, creating serious problems such as environmental pollution and the deterioration of the health of many inhabitants of the world [10,19]. Due to this fact, studies are needed to find alternative uses for waste materials for economic purposes. Concurrently, the sources of cement, lime, and other raw materials, especially near large urban centers, are depleting and thus becoming more expensive, in addition to existing transportation costs and environmental restrictions [10]. Considering these facts, waste materials such as RHA and GGBS can be used as alternative raw materials in the production of building materials to reduce the cost of building projects while protecting the environment [9,10,17,19–21].

The processes involved in the production of lime and Portland cement (PC) contribute significantly to environmental pollution; for instance, the production of one ton of (PC) leads to the emission of almost 0.95 tons of CO2 and requires almost 5000 MJ of energy to complete [9,22–24]; for lime production, these values are ~0.79 tons of CO2 and ~3200 MJ of energy [9]. This calls for the use of waste materials as a complete or partial replacement of conventional binders, as proposed by various researchers [6,10,12,20,25–29]. GGBS is a waste product from the iron industry and has been considered a suitable material for soil stabilization [19]. In terms of CO2 emission and energy consumption, only 1300 MJ of energy is required to produce one ton of GGBS, and the process emits only 0.07 tons of CO2 [2,26]. GGBS can be activated via lime for soil stabilization, as it is a latent hydraulic material [16,30]. This process involves three basic reactions: the lime–soil reaction, lime– GGBS reaction, and GGBS–soil reaction [30]. Calcium silicate hydrates (CSH), calcium aluminosilicate hydrates (CASH), and calcium aluminate hydrates (CAH) are the major hydration products of lime–GGBS-stabilized soil [6,24,26].

GGBS can serve as a partial or complete replacement for cement or lime to prevent or reduce ettringite formation in stabilized sulfate soils. The alumina and silica contents of GGBS can quickly react with the calcium content in soil to form a cementitious gel, thereby reducing or preventing ettringite formation [6,15]. GGBS can also reduce water availability and permeability through the formation of a denser cementitious matrix, which improves resistance to both external and internal sulfate attacks [17,21,31,32].

One agricultural waste material is the husk separated from rice. Due to its limited applications, this material is usually burnt and destroyed. The product remaining after the burning phase, called RHA, has unique characteristics, such as a high silica content [10,33]. Bazyar [10] indicated that RHA has positive impacts on the improvement of gypsum clays' mechanical properties with lime. The author also recommended 6–8% lime and 8–10% RHA as the optimum amounts for stabilization of gypseous soil. RHA has an adequate amount of silica with a high specific surface area, which is very suitable for activating the soil and lime reaction.

Reactive magnesia (M) is a more effective substance in GGBS activation compared to lime, as it facilitates a higher rate of strength development [2,6,20,25,26,28]. Reactive M is commonly obtained from the calcination of magnesite in the temperature range of 700–800 ◦C (below the temperature for dead-burned M (>1400 ◦C)). About 2400 MJ of energy is required to produce one ton of reactive M, and the process emits about 1.4 tons of CO2 [23,24,27]. Reactive M is used at low concentrations during GGBS activation; hence, the overall CO2 emission during the reaction of M with GGBS is considered relatively lower than that of PC. For instance, the production of one ton of M-GGBS with M at the optimum M:GGBS ratio of 1:9–1:4 only emits about 0.20–0.34 tons of CO2 [9,26]. The study by Li et al. [2] also showed that M-GGBS binder protected gypseous soil samples from swelling and contributed to better strength after soaking compared to PC.

This work aimed to establish a new method to stabilize soils subjected to internal sulfate attacks by gypsum (CaSO4) using a combination of nano–magnesium oxide (M), RHA, and GGBS. For this purpose, unconfined compressive strength (UCS), linear expansion (LP), and flexural strength (FS) tests were performed before and after exposure to water. In this study, two steps were taken to achieve the objectives:

Step 1: Running compaction characteristics for each mixture.

Step 2: Performing UCS before and after sulfate exposure, LP, and FS tests.

The curing times applied were 7, 28, and 90 days. In addition to the UCS, LP, and FS tests, SEM and EDS were performed to study the stabilized soil samples to determine their mineralogical and microstructural performance.

#### **2. Materials and Methods**

#### *2.1. Materials*

The materials utilized for this research were kaolin clay (K), hydrated lime (L), calcium sulfate (gypsum) (G), (M), (RHA), and (GGBS). Kaolin clay was supplied by KAOLIN (Malaysia) SDN BHD under the brand name MK40 as a white, finely ground, odorless powder. Kaolin clay was used due to the following considerations: (1) it is one of the major compositions of natural clay minerals; (2) it has a uniform composition, minimal organic content, and consistent and uniform mineralogy; (3) it has a low cation exchange capacity [4,20]; (4) it has a higher alumina content than most other costly minerals and can thus release more alumina in high pH environments, thereby participating in ettringite mineral formation and increasing susceptibility of sulfate attack [12]. For these reasons, kaolin clay is a suitable control soil in the soil stabilization process. Table 1 summarizes the major properties of kaolin clay. A hydrometer test of clay was conducted according to BS EN ISO 17892–4:2016 [34] with the grading curve shown in Figure 1.

**Table 1.** Characteristics of the kaolin clay used in this study.

**Figure 1.** Particle size distribution (PSD) of the kaolin clay used in this study.

L, G, and RHA were collected from Sungai Jawi, 14200 Penang, Malaysia. However, G (CaSO4 × 2H2O) was selected in this research due to its much lower solubility compared to other sulfate types (potassium sulfate, magnesium sulfate, and sodium sulfate), as presented in Table 2. Moreover, it is one of the sulfates that contain calcium, and it is logical that it would form ettringite if a non-calcium-based stabilizer had been used, as presented in Table 2.

**Table 2.** Most common types of sulfate found in soil.

RHA is a byproduct of the rice milling industry that is produced from the burning of rice husk. The processing of 1000 kg of rice grain is estimated to generate about 200 kg of RHA, which, when burnt, should produce about 40 kg of RHA [35]. Currently, RHA is commercially used in the extraction of silica and as a pozzolanic material [36]. The phase of the silica content of RHA (amorphous or crystalline) is a function of the duration and temperature of the burning process. Fine particle-sized amorphous silica is more reactive, and many researchers [36,37] have shown that burning RHA at a temperature range of 500–650 ◦C results mostly in the crystalline form of silica, as it can be effectively transformed from its active state into a crystalline form. In this research, RHA was prepared by burning RHA at a temperature range of 350 to 550 ◦C for three hours to retain the amorphous silica content.

GGBS is a byproduct of the pig-iron manufacturing process and is formed through the rapid cooling of molten iron slag to retain its amorphous structure, followed by grinding to increase its specific surface area. The GGBS used in this study was collected from MDC Sungai Pentani Company, Malaysia. It was chosen to reduce the rate of ettringite formation through the provision of more Al and Si, which react with Ca2+ to form complex cementing gels [12,31,32]. The denser structure and lower calcium ion content of GGBS should contribute to superior sulfate resistance [6,38].

In this research, M was chosen, as it is a green and low-carbon stabilizer for clay soil. It was collected from Hang Zhou Jiu Peng New Material Co., Ltd., China as a white, fine, crystalline powder. The production of reactive M requires low temperatures, consumes less fuel, and emits less CO2 than that of Portland cement (PC) [2]. Nanostructured binders have been receiving more attention recently because the main hydrate CSH gel of cement is also a natural nanostructured material [5,22]. Furthermore, it reduces sulfate-induced expansion and has a lower crystallinity and higher reactivity [2,15], thus reacting significantly more quickly with water [25,39].

An X-ray fluorescence test (XRF) was carried out to determine the chemical properties of all materials (K, L, M, GGBS, RHA, and G), which are summarized in Table 3.

**Table 3.** Chemical compositions of kaolin clay, L, M, G, GGBS, and RHA.

#### *2.2. Sample Preparation*

Sulfate-bearing soil or gypseous soil was prepared artificially by mixing kaolin clay with 10% gypsum (by dry weight of soil). The concentration of sulfate was determined as the worst case according to AASHTO [40–42]. The risk of different sulfate levels is summarized in Table 4.

**Table 4.** Severity of sulfate levels.

Cylinder samples were 50 mm in diameter and 100 mm in height and were prepared as reported by [2,4,8] for tests of linear expansion (LP), flexural strength (FS), and unconfined compressive strength (UCS). Each mixture system was compacted with an optimum moisture content (OMC) and maximum dry density (MMD) following the BSEN 13286–2:2010 standard [43]. After compaction of samples, each sample was covered using cling film to reduce moisture loss.

The total binder contents were fixed at 10% and 20% based on the weight of the soil (see Table 5) for each system (unary, binary, and ternary). This was achieved using activator (L) calcium-based stabilizer and (M) non-calcium-based stabilizer at dosages of 10% and 20%, with (G) calcium sulfate dosed at 10% (as a worst case) into kaolin clay. The ratios of M stabilized with (RHA and GGBS) (Figure S1 in supplementary materials shows a comparison of particle size distribution between RHA and GGBS) were set as 1:3, 1:1, and 3:1 in a binary system, and 1:05:05, 1:1:2, 1:2:1, and 1:2.5:0.5 in a ternary system. Figure 2 summarizes the experimental process. In total, 462 cylinder samples were prepared: 207 for testing UCS, 138 for LP, and 117 for FS.

**Figure 2.** Schematic representation of the experimental procedure.

**Table 5.** Mixture designs of stabilizer agents.

(K) Kaolin, (G) gypsum, (L) hydrated lime, (M) nano–magnesium oxide, (RHA) Rice husk ash, and (GGBS) ground granulated blast-furnace slag.

#### *2.3. Experiments*

#### 2.3.1. Linear Expansion Test (Swelling) (LP) Test

Two cylindrical samples were prepared for each mix proportion, and were cured for 7, 28, and 90 days to determine their vertical swelling ratio (%). Swelling readings were recorded every 24 h until no significant swelling ratio was observed after partial soaking in water. The LP test was conducted following the BS EN 13286–49:2004 standard [44].

#### 2.3.2. Unconfined Compressive Strength (UCS) Test

The UCS test was conducted according to BS EN ISO 17892–7:2018 [45] for each mix proportion; three cylindrical samples were tested for compressive strength before and after soaking. "Before soaking" implies that the samples had been cured for 7, 28, and 90 days without having been immersed in water, while "after soaking" implies after the linear expansion test, which was 52 days after the preparation of the samples for all soil stabilization systems. A constant rate of compression strain of 1 mm/min was applied until sample failure occurred.

#### 2.3.3. Flexural Strength (FS) Test

For this test, specimens similar to those described for the UCS and LP tests were used. A three-point bending test was performed to determine the flexural strength for kaolin clay and the optimum mixing of sulfate-bearing soil mixed with the calcium-based stabilizer (lime) and non-calcium-based stabilizer. For each test, three samples were prepared using a compaction mold at the maximum dry density (MMD) and optimum moisture content (OMC) and cured for 7 and 28 days. The flexural strength test was performed according to ASTM D1635 at a constant strain rate of 0.1 mm/min. The flexural stress for the circular specimens of the outer layer of each specimen was calculated as follows:

$$\text{FS} = \frac{\text{PL}}{\pi \text{r}^3} \tag{3}$$

where FS = flexural strength, P = maximum applied load, r = radius of the sample, and L = support span.

#### 2.3.4. Microstructure Analysis (SEM/EDX)

Scanning electron microscopy (SEM) was performed to inspect the surface morphology of the specimens, which was followed by energy dispersive spectroscopy (EDS) to reveal the presence and composition of various elements within. Small portions of soil specimens were collected from carefully hand-broken samples after UCS and then dried in the oven at 105 ◦C for 24 h prior to testing.

#### **3. Results and Discussion**

*3.1. Unconfined Compressive Strength (UCS) Test*

3.1.1. Effects of M-RHA on UCS

Figures 3 and 4 depict the effects of L and M additions on the UCS values after 7, 28, and 90 days of curing for the gypseous soil samples. The UCS values for M-treated samples increased from 885.33 to 1108.67 kPa when the M content was increased from 10% to 20% without the presence of sulfate at 90 days of curing. Samples treated with L and M (10% and 20%) showed a decrease in UCS values in the presence of sulfate (see Figures 3 and 4). Furthermore, after soaking, the UCS values deteriorated from 885.33 to 635 kPa for M10%, (reducing by 28.27%) and 1108.67 to 1035.33 kPa with 20% M content in the presence of sulfate (reducing by 6.62%). However, this deterioration, as compared to that of the lime stabilizer, was much less. The observed deterioration in the UCS results can be attributed to ettringite production, the growth of which between the particles of the sample soil would lead to the destruction of the soil structure. In addition, the residual sulfate chelates formed calcium in CSH through a decalcification process, which accounted for the lower strengths of the soaked and unsoaked samples. However, the higher strength of the M-treated samples might have been due to the hydration process and formation of pozzolanic products that might have improved the strength of the bonds between the soil particles.

**Figure 3.** UCS of soils stabilized with 10% of L, M, and M-RHA before and after soaking in the presence of sulfate: (**a**) after 7 days of curing, (**b**) after 28 days of curing, (**c**) after 90 days of curing.

**Figure 4.** UCS of soils stabilized with 10% of L, M, and M-GGBS before and after soaking in the presence of sulfate: (**a**) after 7 days of curing, (**b**) after 28 days of curing, (**c**) after 90 days of curing.

Figures 3 and 4 show the effects of the RHA content on enhancing the USC values for the M-treated samples (soaked and unsoaked) after 7, 28, and 90 days of curing in the presence of sulfate. For example, Figure 4b indicates that the UCS values of the samples treated with 7.5M-12.5RHA increased from 2510 kPa to 2753 kPa when immersed in water after 28 days of curing, where UCS increased by 9.68%. This impact was also observed for 5M-15RHA (increased from 1830 to 2148 kPa), which was improved by 17.37%, and 5M-5RHA (increased from 884 to 929 kPa), which was enhanced by 5.09%. These improvements could be due to pozzolanic product (CSH) formation, which would have improved the USC value for the samples with higher M-RHA content.

#### 3.1.2. Effects of M-GGBS on UCS

For the M-GGBS stabilizer, all specimens showed enhancement of their UCS values. Figures 5 and 6 presented the maximum UCS of the M-GGBS-stabilized soils at the M:GGBS ratio of 1:3 when a 20% binder was used to stabilize the sulfate-bearing soil: 3327.33, 7125, and 12,115 kPa at 7, 28 and 90 days of curing, respectively. This improvement may have been because of the high content of highly reactive M, and because an excessive residual

can negatively impact the strength of stabilized soils. Nevertheless, the UCS values of the stabilized samples before and after soaking indicated enhancement in the mechanical performance of the samples after wetting, except ratios of 3:1 and 1:1 when used to treat gypseous soil samples with 10% M-GGBS-stabilizer (see Figure 5); for example, the samples treated with 5M-5GGBS deteriorated by 13.02%, 33.11% and 29.76% at 7, 28 and 90 days of curing, respectively. The UCS of the treated soil sample with 2.5M:17.5GGBS was 11,275 kPa after soaking, which was significantly higher than that before soaking (914.23%) after 7 days of curing. After soaking, the 5M-15GGBS ratio of 1:3 exhibited an optimum UCS value of 12,831 kPa, which was increased by 5.91% after 90 days of curing. However, the UCS values showed a decline at higher M-GGBS ratios. These findings agree with the report by Li et al. [2], where highly reactive M was found to have better activation efficiency with GGBS compared to low-reactive M; therefore, lower M-GGBS ratios are recommended to achieve higher UCS values.

**Figure 5.** UCS of soils stabilized with 20% of L, M, and M-RHA before and after soaking in the presence of sulfate: (**a**) after 7 days of curing, (**b**) after 28 days of curing, (**c**) after 90 days of curing.

**Figure 6.** UCS of soils stabilized with 20% of L, M, and M-GGBS before and after soaking in the presence of sulfate: (**a**) after 7 days of curing, (**b**) after 28 days of curing, (**c**) after 90 days of curing.

#### 3.1.3. Effects of M-GGBS-RHA on UCS

As shown in Figures 7 and 8, all ternary binder compositions improved the UCS values in the stabilized gypseous soil. The highest values of UCS obtained by the M-GGBS-RHA stabilizer ratio of 1:2.5:0.5 were 2050, 5232, and 9142 kPa after 7, 28, and 90 days of curing, respectively. However, the lowest enhancements after 7, 28, and 90 days of curing were 563.67, 758.67, and 975 kPa, respectively, when a stabilizer–binder ratio of 1:0.5:0.5 was used.

**Figure 7.** UCS of soils stabilized with 10% of L, M, and M-GGBS-RHA before and after soaking in the presence of sulfate: (**a**) after 7 days of curing, (**b**) after 28 days of curing, (**c**) after 90 days of curing.

After soaking, the M-GGBS-RHA ratio of 1:2.5:0.5 produced the highest UCS of 9721 kPa among all the M-GGBS-RHA-stabilized soils, an increase of 6.33%; the UCS decreased with the reduction in the GGBS ratio. This enhancement in the UCS values might have been due to the hydration process following the prolonging of the curing time from 7 days to 90 days; this should ensure proper hydration, improved strength, and resistance against sulfate attacks [8]. Furthermore, the production of more cementitious gels, such as CSH, CAH, and CSAH, contributed to consumption of the available calcium, leading to the inhibition of ettringite formation [4].

**Figure 8.** UCS of soils stabilized with 20% of L, M and M-GGBS-RHA before and after soaking in the presence of sulfate: (**a**) after 7 days of curing, (**b**) after 28 days of curing, (**c**) after 90 days of curing.

#### *3.2. Linear Expansion Test (Swelling) (LP) Test*

Figures 9–14 illustrate the typical swelling plots for K-M clay and K-L-G and K-M-G clay systems dosed with 10% wt of gypsum and stabilizer with 10% and 20% wt of L and M for observatory periods of 7, 28, and 90 days, respectively. Swelling was observed immediately after soaking the cylinder samples in water, which was sustained throughout monitoring until the cessation of swelling. A higher rate of swelling was observed compared to the reported expansion in lime-stabilized sulfate-bearing soils [4,10,15].

**Figure 9.** Vertical swelling strain of soils stabilized with 10% of L, M, M-RHA, and M-GGBS after 7 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.

**Figure 10.** Vertical swelling strain of soils stabilized with 10% of L, M, M-RHA, and M-GGBS after 28 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.

**Figure 11.** Vertical swelling strain of soils stabilized with 10% of L, M, M-RHA, and M-GGBS after 90 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.

**Figure 12.** Vertical swelling strain of soils stabilized with 20% of L, M, M-RHA, and M-GGBS after 7 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.

**Figure 13.** Vertical swelling strain of soils stabilized with 20% of L, M, M-RHA, and M-GGBS after 28 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.

**Figure 14.** Vertical swelling strain of soils stabilized with 20% of L, M, M-RHA, and M-GGBS after 90 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.

Figures 9 and 12 show the highest degree of swelling obtained with a calcium-based stabilizer (10% wt): which was 25.226% with 10% lime after 7 days curing; meanwhile, the lowest swelling was observed with 20% lime: 18.313% after seven days of curing. Nevertheless, the comparison of the swelling values of the treated soils between the calcium-based stabilizer and non-calcium-based stabilizer clearly showed that the noncalcium-based stabilizer (M) had a significant effect on restricting the swelling values. For example, when 10% and 20% M were used, the swelling values were 0.18% and 0.466%; hence, the swelling was restricted to 99.28% and 97.45%. This result was due to the absence of calcium in non-calcium-based stabilizer (M), which led to the suppression of ettringite formation.

#### 3.2.1. Effects of M-RHA on Swelling

Utilizing the M-RHA stabilizer resulted in a restriction in the volume change (swelling) for all gypseous soil specimens, as presented in Figures 9–14. The lowest minimum values of swelling, achieved with a 5M-5RHA ratio of 1:1, were 0.047%, 0.04%, and 0.01% after 7, 28, and 90 days of curing, respectively, with the swelling inhibited 99.81%, 99.78%, and 99.67%, respectively. Furthermore, the 20% M-RHA-stabilizer led to a low volumetric change after 90 days of curing at 0.011%, 0.026%, and 0.026%, with the swelling suppressed to 99.5%, 98.82%, and 98.82%, respectively. This reduction in swelling can be attributed to the restriction of ettringite formation and the production of more CSH compounds in the absence of calcium.

#### 3.2.2. Effects of M-GGBS on Swelling

Figures 9–14 present the vertical volume change (swelling) of gypseous soil treated with the M-GGBS stabilizer. The maximum vertical volume change was 7.293%, obtained after applying a 7.5M-2.5GGBS ratio of 3:1 over 7 days of curing. However, all other specimens exhibited low swelling. For example, the swelling of soil samples stabilized with 5M-15GGBS and 2.5M-17.5GGBS was 0.001% and 0.005% after 90 days of curing, with swelling magnitude almost wholly suppressed to 99.95% and 99.77%. The reduction in the swelling values was because of the use of GGBS, which exhibited superior sulfate resistance along with its denser structure and lower presence of calcium ions [6,38].

#### 3.2.3. Effects of M-GGBS-RHA on Swelling

Figures 15–17 reveal that the use of the M-GGBS-RHA stabilizer (10 and 20 wt. %) modified the behavior of the volumetric change and resulted in a significant decrease in the swelling magnitude of the gypseous soil. After 28 days of observation, the swelling magnitudes for 5M-2.5GGBS-2.5RHA, 5M-5GGBS-10RHA, 5M-10GGBS-5RHA, and 5M-12.5GGBS-2.5RHA were 0.04%, 0.089%, 0.044%, and 0.001%, respectively, with swelling values suppressed to 99.78%, 98.98%, 99.5%, and 99.98% respectively. Nevertheless, the longer curing period (90 days) produced further declines in the swelling rate, thereby achieving minimum swellings of 0.01%, 0.058%, 0.017%, and 0.001%, where the swelling was roughly inhibited to 99.67%, 97.38%, 99.23%, and 99.95% respectively.

**Figure 15.** Vertical swelling strain of soils stabilized with 10% and 20% of L, M, and M-GGBS-RHA after 7 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.

**Figure 16.** Vertical swelling strain of soils stabilized with 10% and 20% of L, M, and M-GGBS-RHA after 28 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.

**Figure 17.** Vertical swelling strain of soils stabilized with 10% and 20% of L, M, and M-GGBS-RHA after 90 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.

Results from the linear expansion test indicated that swelling behavior disappeared in the high-sulfate soil, which was treated with the M-GGBS-RHA-stabilizer. The volume changes of the treated soils were less than those of the lime-treated soils, indicating that the swelling characteristics were enhanced in the treated high-sulfate soils.

These results indicate that the M-GGBS-RHA-stabilized soil resisted sulfate attacks better than the lime-stabilized soil. Moreover, the results showed that the swelling of the lime-stabilized soil was higher than that of the M-GGBS-RHA-stabilized soil with an increase in the soaking time. The phenomenon can be explained by two factors: (1) a larger amount of calcium in the lime-stabilized soil contributed to ettringite formation in the presence of sulfate and (2) the consumption of calcium by sulfate, which led to a reduction in the formation of cementitious material such as CSH, CAH, and CSAH. Adeleke et al. [4] reported that there are various levels of risk in relation to swelling, as

shown in Table 6. The risk level was "very strong" in comparison with the maximum expansions of 25.226% and 18.313% obtained in the current study from the mixing of K-10L-10G and K-20L-10G, respectively.

**Table 6.** Risk levels for swelling of clay soil.

#### *3.3. Flexural Strength Flexural (FS)*

Flexural load curves for the kaolin clay and sulfate-bearing soil along with the calciumbased stabilizer (L) and non-calcium-based stabilizer (M) with the inclusion of RHA, GGBS, and GGBS-RHA ratios of 10% and 20% after curing for 7 and 28 days, are shown in Figures 18 and 19. The kaolin clay curve exhibited a flexural strength of 121.44 kPa after curing for 28 days. In contrast, the curves produced by the M stabilizer mixtures showed a slight improvement with and without the presence of sulfate, which is a direct measure of the flexural strength of the specimens. When compared to specimens treated with the L stabilizer, the flexural strength was less with the presence of sulfate. Furthermore, using 10% and 20% of the M-RHA, M-GGBS, and M-GGBS-RHA stabilizers resulted in a significant increase in the peak flexural load. This effect was especially relevant regarding the 20% M-GGBS content. The flexural strength of the M-GGBS mixture with 10% and 20% content reached 1270.37 and 1922.55 kPa after curing for 28 days, respectively, gaining values 303.4% and 302.9% higher than those of the L stabilizer (314.89 and 477.11 kPa) using 10% and 20% mixtures, respectively, after 28 days of curing.

**Figure 18.** Flexural load curves of test gypseous soils treated with 10% mixture content after 7 and 28 days of curing.

**Figure 19.** Flexural load curves of test gypseous soils treated with 20% mixture content after 7 and 28 days of curing.

#### *3.4. SEM and EDX Analysis*

The SEM images of the gypseous soil–lime mixture after 90 days of curing showed that a significant amount of crystalline mineral needles (ettringite) had formed, as shown in Figures 20a and 21a. This resulted in a decrease in strength, a high capacity for swelling, and the destruction of the soil specimen's structure. The improvement in the gypseous soil samples was determined by measuring the Si/Al ratio (%) utilizing EDX to explain the strength development. Generally, an increase in the Si/Al ratio refers to the forming of a high number of Si–O–Si bonds. Consequently, the mechanical strength of the soil sample structural matrix was enhanced [45]. The Si/Al ratio in the (10% and 20%) L-G specimens obtained from the EDX pattern ranged from 1.06 to 1.12, which was low due to the ettringite formation and high porosity in the microstructure of the samples. The EDX pattern of the gypseous soil–lime mixture and its elemental composition is represented in Figures 20b and 21b.

**Figure 20.** SEM photo and EDX spectrograph of stabilized gypseous soils after 90 days of curing: (**a**) SEM photo of 10L:10G and (**b**) EDX spectrograph of 10L:10G.

**Figure 21.** SEM photo and EDX spectrograph of stabilized gypseous soils after 90 days of curing: (**a**) SEM photo of 20L:10G and (**b**) EDX spectrograph of 20L:10G.

Figures 22a and 23a show SEM observation images of the sulfate soil–M-G mixture, revealing spherical and irregular particles of magnesium silicate hydrate (MCH). The EDX information showed a slight increase in the Si/Al ratio (1.1 and 1.14) for M-G-stabilizer specimens with 10% and 20% when compared to the L-G specimens (1.06 and 1.12), which was probably due to the absence of ettringite and the formation of MSH product. Figures 22b and 23b, respectively, present the EDS spectrum and elemental composition of the gypseous soil–M-G mixture.

**Figure 22.** SEM photo and EDX spectrograph of stabilized gypseous soils after 90 days of curing: (**a**) SEM photo of 10M:10G and (**b**) EDX spectrograph of 10M:10G.

**Figure 23.** SEM photo and EDX spectrograph of stabilized gypseous soils after 90 days of curing: (**a**) SEM photo of 20M:10G and (**b**) EDX spectrograph of 20M:10G.

Figures 24a, 25a and 26a show the SEM observation images of M-GGBS, M-RHA, and M-GGBS-RHA, respectively. In terms of particle shape and surface features, M-GGBS was quite different from M-RHA and M-GGBS-RHA. M-RHA and M-GGBS-RHA particles were spherical, as shown in Figures 25a and 26a. The particles had shiny surfaces without any dust covering them. The M-GGBS particles were rather angular, irregular in shape, and the presence of plate-shaped particles was evident (Figure 24a), which had rough, gritty surface textures. Figures 24a, 25a and 26a show the surface morphologies of the soil– M-GGBS, –M-RHA, and –M-GGBS-RHA specimens after 90 days of curing. As indicated, no ettringite formation was observed. This proves that the addition of M with GGBS and RHA as a stabilizing agent contributed to the formation of CSH gel to a significant degree. The presence of some crystalline structures, such as MSH (magnesium silicate hydrate), was also observed. The pozzolanic activity occurring between M, GGBA, and RHA as a stabilizer and clay was attributed to the formation of CSH gel. Based on the results derived from the EDX spectrum, the highest Si/Al ratios (1.23, 1.59, and 1.41) were observed in the 7.5M-12.5RHA, 5M-15GGBS, and 5M-12.5GGBS-2.5RHA samples, respectively. This improvement was due to GGBS and RHA dissolving in an alkaline activator to generate extra soluble alumina and silica, which increased the Si/Al ratio of the specimen, leading to the complete activation of the particles and producing calcium– alumino–silicate hydrate (CASH) in the matrix. Figures 24b, 25b and 26b show the EDS spectrum and elemental composition of the gypseous soil–M-GGBS, –M-RHA, and –M-GGBS-RHA mixtures, respectively.

**Figure 24.** SEM photo and EDX spectrograph of stabilized gypseous soils after 90 days of curing: (**a**) SEM photo of 5M:15GGBS and (**b**) EDX spectrograph of 5M:15GGBS.

**Figure 25.** SEM photo and EDX spectrograph of stabilized gypseous soils after 90 days of curing: (**a**) SEM photo of 7.5M:12.5RHA and (**b**) EDX spectrograph of 7.5M:12.5RHA.

**Figure 26.** SEM photo and EDX spectrograph of stabilized gypseous soils after 90 days of curing: (**a**) SEM photo of 5M:12.5GGBS:2.5RHA and (**b**) EDX spectrograph of 5M-12.5GGBS:2.5RHA.

The increase in UCS value with prolonged curing periods could have been due to cementing gel formation from the interaction of the soil with the nanoparticles, which improved the UCS of the treated samples as a result of the improved soil particle bonding [46,47].

#### **4. Conclusions**

Sulfate-bearing soils cause major issues in pavement and various civil engineering infrastructures due to their causing significant swelling and strength damage. The results of this study portrayed the impacts of (M) as a non-calcium-based stabilizer, rice husk ash, and GGBS as abundant byproducts in treating gypseous soil with a high level of sulfate. Based on the results of the study, the following conclusions were made: