**Abstract**

The blending or the bilayering of two complementary species are the dominant methods for in-solution-processed thin film devices to get a strong donor-acceptor behavior. They propose opposite strategies for the respective arrangement of the two species, a central point for energy and/or charge transfer. In this work, we propose to engineer at the scale of the exciton diffusion length the organization of a donor (poly(vinyl-carbazole), PVK) and an acceptor (poly(para-phenylene-vinylene), PPV) in a nanowire geometry. A two-step template strategy was used to fabricate coaxial nanowires with PPV and PVK, alternatively as the core or the shell material. Their stationary and time-resolved photoluminescence properties were investigated and compared to the case of PVK-PPV blend. Their respective characteristics are direct evidences of the dominant mechanisms responsible for the emission properties.

**Keywords:** organic nanowires, luminophores, exciton, shell-core


**3.1. Platelet transfusion**

326 Blood Cell – An Overview of Studies in Hematology

matching is necessary [1, 50].

12-24 h and should not be refrozen [43].

monoxide poisoning [54, 55].

acting steroids are given [53, 57].

diseases [53].

red cells. In order to prevent primary sensitization and risk of developing hemolytic disease in breeding females, cross-matching and/or blood typing is important. In general veterinary

To decrease adverse reactions one sould pay attention to blood typing and crossmatching procedures as much as monitoring. There is always risk in blood transfusions. For this reason, they should be performed only when warranted. When taking history, previous transfusion therapy should be asked and in a history of previous transfusion therapy cross-

Depending on availability and indication for transfusion, whole-blood or blood-component therapy may be administered. RBCs, white blood cells (WBCs), platelets, all the coagulation

In cats, fresh whole blood is the most common product used recently. Stored whole blood, packed red blood cells and fresh frozen plasma (FFP) are also given as transfusions [45].

The heavier cellular elements from the supernatant plasma are sedimented by centrifugation of whole blood sediments. Due to separation of blood collection within 8 hours all protein activity and concentration are maintained in the plasma. The obtained supernatant usually frozen. For subsequent transfusion, it is stored as fresh frozen plasma (FFP). In addition it can also processed to provide cryoprecipitate and cryosupernatant. It can also be transfused immediately as fresh plasma [52, 53]. Fresh frozen plasma have to be stored frozen at -30°C before used. Also it should be identified with the donor blood type, name and collection date. Samples thawed and not used sould discarded or stored in a fridge and used within

Recently an ultra-purified polymerised bovine haemoglobin solution is the only commercially available alternative to red cell transfusion (Oxyglobin). It is not licensed in cats but it has been used in treatment of anaemia in cats and also in therapy of carbon

Hemostatic protein deficiencies lead to hemorrhagic disorders and the treatment is done principally by plasma components [56]. In animals with von Willebrand disease (vWD) and hereditary coagulation factor deficiencies active hemorrhage is controlled by plasma components. Plasma components are also used for preoperative prophylaxis in these

For preparation of plasma components sterile plastic bags are used. After that they are stored and transferred as frozen in individual boxes. Products have to be stored at -20°C or lower. Just before transfusion they warmed to 37°C in a water bath or incubator. Preferred route of administration is the intravenous transfusion of plasma components. If attempts at vascular access have failed, intraosseous transfusion can be used in emergency situations. When acut allergic reactions occur transfusion is stopped and antihistamines and/or short-

Cats have antibodies to non-self blood types within the plasma. Because of this only typespecific plasma should be administered to cats in contrast to dogs. Using one of the

factors, albumin and immunoglobulins constitute whole blood (WB) [51].

practise, blood typing for canine DEA 1.1 and for feline types A and B is applied [1].

Correction of coagulation by fresh platelets are shown by in vitro coagulation studies. Freshly collected platelets correct thrombocytopenia, control associated hemorrhage, and prevent death from bleeding. Hemorrhagic diathesis are prevented by platelet replacement for thrombocytopenia [59].

Severe thrombocytopenia or thrombopathia result in bleeding. Platelet transfusion is used for the control of this bleeding. In veterinary medicine platelet transfusion has been used rarely compared to red blood cell (RBC) and plasma transfusion. In dogs, reports related to platelet transfusion are generally associated with experimental hematopoietic stem cell transplantation. Platelet-rich blood products consist of fresh whole blood (FWB), plateletrich plasma (PRP) and platelet concentrate (PC). They are used for aggressive anticancer therapy and treating complex hematologic disorders. Centrifugation of whole blood constitute platelet-rich plasma (PRP) and centrifugation of platelet-rich plasma constitude platelet concentrates (PC). Platelet activation is induced by centrifugation so that the resuspension of the platelet pellet during PC preparation from dogs is difficult. The preparation efficiency of PC from dogs can be improved by addition of PGE1 in PRP before the centrifugation of PRP. Also therapeutic efficacy of the platelets are maintained. In 10-28 kg body weight dogs plateletpheresis has been used successfully. On the canine donor thrombocytopenia and hypocalcemia are the main adverse effects of plateletpheresis [60-62].

At room temperature (RT) (20-24°C), PRP and PC can be stored for 5-7 days with continuous or intermittent agitation. At RT FWB can be stored for up to 8 hours. The interest in freezed (4°C) storage of platelets is increasing because of the increased risk of bacterial proliferation at RT storage. Storage of human PRP and PC are limited to 5 days because of prevention of bacterial proliferation at room temperature [60- 63].

Platelet transfusions as with RBC and plasma components should be performed with 170 µm filters standard blood administration sets. Transfusion sets which can bind platelets should be exempt from latex [60].

The most common reaction to PC are febrile reactions. The frequency is decreased by prestorage leukoreduction. In immunocompetent dogs receiving multiple transfusions, alloimmunization to platelet antigens occurs. Leukocyte reduction and ultraviolet B irradiation are recently accepted methods for preventing the development of platelet alloimmunization [64-66].

Recently platelet cryopreservation are used to provide long-term storage and immediate availability of platelet products for transfusion. When fresh platelets are unavailable cryopreserved platelets can be activated in vitro and provide therapeutic benefit [63].

Principles of Blood Transfusion 329

The ideal feline blood donors should be healthy, indoor-only cats with an agreeable temperament for easy handling and restraint. Owned pet cats should be donate maximum once every 2 months [43]. The features of feline donor sould be as follows; weigh more than 4.5 kg, have a packed cell volume that is at least 0.35 L/L, have demonstrated a good temperament, and be in good physical condition [3]. Donor cats can donate between 10 and 12 ml/kg. Adult healthy cats can donate 50 ml every weeks. Donors have to be type A. Type B donors may be demanded depending on breed prevalence and geography. Feline leukemia virus, feline immunodeficiency virus (FIV), feline infectious peritonitis, heartworm

For appropriate care of donors some processes needed. These are current vaccinations, if there is contact with new animals every 6 mo fecal floatation, monitorization of hemogram every year, analysing clinical chemistry, screening for infectious diseases and in the dog preventative heartworm therapy in areas where it is necessary. When blood collection is taken the donor's weight, temperature, and packed cell volume have to be analysed [3, 71]. PCV or Hb are measured by taking a blood sample. Preferentially cats with a PCV of 30–

In the cat, blood can be taken by using a 19- to 20 gauge needle or butterfly into a syringe via jugular vein venipuncture. The region over the jugular vein is clipped and prepared aseptically and sedation is administered. It is prefered to use a 1:1 combination of ketamine 100 mg/ml and midazolam 5 mg/ml. It is made up in a small syringe and given intravenously up to a maximum dose of 5 mg/kg ketamine (0.1 ml/kg of combination). Syringe consists of either ACD, CPD, or CPDA- 1 (1 mL/9 mL of blood), or heparin (5 units/mL of blood). Before a preservative solution is used it can be placed in a small blood bag. To access the jugular vein a 19-21G butterfly needle is used. The blood is collected over a total of 10 15 mins. At once a maximum of 10-12 ml/kg blood can be donated. Isotonic crystalloid fluid therapy post-donation at a rate of 60 ml/h for 3 h is given to the cat [3, 43].

Precaution is necessary to prevent damage of the blood product and harm to recipient. Blood typing or crossmatching have to be carried out to provide compatibility before RBC

Transfusions of red blood cell should be administered through a filter. The filter is arranged to remove clots and particles which are potentially harmful to the patient. Blood infusion sets have in-line filters. These filters trap large cells, cellular debris, and coagulated proteins. The pore size range from 170µm to 260µm. A filter may be used to administer 2-4 units of blood to a patient or for a maximum time limit of 4 hours according to human blood banking standards. High protein concentration at the filter surface and room temperature conditions promote proliferation of any contaminating microorganisms. The rate of flow slowed down by accumulated material. After 5 days or more of refrigerated storage constituted microaggregates composed of degenerating platelets, white blood cells (WBCs), and fibrin strands in blood. They are removed by other blood filters with a pore size of 20-40

disease, and Hemobartonella sp have to be excluded in donor cats [1].

35% are used but cats with low–normal PCVs should not be used [43].

**5. Administration**

transfusion [41].

#### **3.2. Granulocyte transfusion**

Granulocyte transfusion can be used as supportive therapy. It is used in patients with lifethreatening neutropenia caused by bone marrow failure or in patients with neutrophil dysfunction. Granulocyte transfusions is shown to be useful in treatment of infections in patients after treatment with high-dose chemotherapy. It is helpful especially in the chemotherapy associated with conditioning for hematopoietic stem cell transplant. By using granulocyte colony-stimulated factors higher doses of granulocytes for transfusion are produced. Thus recently the use of therapeutic granulocyte transfusion has been increased. The outcome of transfusion are effected by the type of infection being treated, the likelihood of recipient marrow recovery, and recipient alloimmunization [67].

In small animals therapeutic granulocyte transfusions have been used especially in experimental models of myelosuppression and neonatal sepsis. In clinical veterinary medicine they have been used rarely. Granulocytes can be used to identify the site of inflammation. Beside leukapheresis, centrifugation of FWB, with or without colloidfacilitated sedimentation, may be used to isolate canine and feline buffy coats. Only sedimentation may also be used in the cat. At RT granulocytes are stored immobil for 24 hours. The dose for beginning is 1 x 1011 granulocytes/kg in a volume of 15mL/kg. It is used once to twice in a day [68-70].


2.3.4. Model Architecture

Determining the network architecture is one of the crucial and challenging tasks in the development of ANN models because it requires the selection of several hidden layers and the number of nodes in each of these.

The number of model inputs and outputs restricts the number of nodes in the input and output layers. The input layer of the ANN model developed in this work had four nodes, one for each of the model inputs (i.e., a rotational speed of cylinder (RS), threshing clearance of concave sieve (TC), separating clearance of concave sieve (SC), feeding quantity (FQ)). On the other hand, the output layer had three nodes (i.e., crushing rate (YP), impurity rate of threshed materials (YZ), and entrainment loss rate (YS)) representing the measured value of threshing performance.

Figure 5 shows the basic elements of an artificial neuron. Artificial neurons mainly comprise weight bias and activation functions. The BP neural network is the most popular and widely used artificial neural network architecture [33]. It involves an input layer, one or more hidden layers, and an output layer. Evidence suggests that a network with a threshold, at least one S-shaped hidden layer, and a linear input layer can approximate any rational number [34]. Mathematical expressions and interpretations of artificial neural networks can be referred to in reference [35].

**Figure 5.** Schematic diagram of an artificial neural network.

The activation function introduces nonlinearity into the neural network, making it more powerful than the linear transformation. The Levenberg–Marquardt algorithm is the most commonly used multi-layer perception training algorithm. It is a gradient descent technique [36] used to reduce the error of specific training patterns. The network was built using the Levenberg–Marquardt backpropagation technique. Tansig is a common nonlinear activation function for nodes in the hidden layer. Figure 6 depicts the architecture of the artificial neural network system described in this paper. *W* is a weight matrix for the hidden and output layers, and *Nij* is a node that computes a weighted sum of its inputs and passes the sum through a soft nonlinearity or activity function.

**Figure 6.** The artificial neural network architecture of threshing performance.


**Chapter 9**

*Landraces - Traditional Variety and Natural Breed*

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conservation of crop genetic resources: declining *de facto* diversity and optimal funding. Natural Resources 6: 196-207.

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[122] Rodringuez LC, Herrero M, Baltenwoek I. (2011). Communitybased intervention for the use and conservation of animal genetic resources: the case of indigenous scavenger chicken production in Benin. Tropical Animal Health and Production

[123] Simianer H. (2005). Decision making in livestock conservation. Ecological Economics 53: 559-572.

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43: 961-966.

**166**


4. Conclusions

and

Pfr ¼ e

probability Po the suitable value of Th is given.

120 From Natural to Artificial Intelligence - Algorithms and Applications

Figure 10. Ranking evaluation of image set AR(B).

Figure 11. ROC of proposed and conventional hLBPI algorithms.

Then from Eqs. (4) and (5) it follows that α ¼ �Ln Pfað Þ Th =Th where Pfað Þ Th is the false acceptance percentage, calculated from a threshold Th. Hence, for a desired false acceptance

Figure 13 shows the results of an exponential function and some experimental results of the

relationship between the false acceptance rate and false rejection rate in this chapter.

�βPfa (5)

Th ¼ �Ln Pð Þo =α (6)

This chapter presented the application of different texture descriptors for tasks ranging from feature extraction to face recognition, which are based on the LBP algorithm, specifically the hLBPI and WBP. The evaluation results demonstrate that these algorithms provide good recognition rates. In most situations, the accuracy of recognition with the WBP is slightly lower than the accuracy with the hLBPI because the feature vector estimation of the WBP does not require PCA and uses non-overlapping blocks. This fact results in an important computational complexity reduction of approximately 2NML=9 relative to hLBPI, where L is the feature vector size. If an L with a big value is used, it produces a more exact feature vector, although this causes the computational complexity to increase. Also, the evaluation results were compared with other methods, such as the Eigenfaces, Laplacianfaces, and Fisherfaces. Another point that can be observed is that, as in all recognition systems, the accuracy percentage increases when a greater number of training images are used, so one could look for ways to generate training images based on an original image to increase the number of images, and then the system may be able to recognize face images in other types of environments, such as with a lot of lighting or with partial face occlusions. The results obtained with the set A shown in Figure 8 where the system has a good performance both with images with facial expressions and with lighting, as well as when there are images with a partial occlusion of the face such as sunglasses as shown in Figure 9. In both cases, a recognition rate higher than 90% is obtained.

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[8] Benitez-Garcia G, Olivares-Mercado J, Aguilar-Torres G, Sanchez-Perez G, Perez-Meana H. Face identification based on contrast limited adaptive histogram equalization (CLAHE). In: Proc. of International Conference on Image Processing, Computer Vision and Pattern Rec-

[9] Zaeri N. Eigenphases for corrupted images. In: Proc. of the International Conference on Advances in Computational Tools for Engineering Applications; 2009. pp. 537-540

[10] Olivares-Mercado J, Hotta K, Takahashi H, Nakano-Miyatake M, Toscano-Medina K. Improving the eigenphase method for face recognition. IEICE Electronics Express. 2009;

[11] Benitez-Garcia G, Olivares-Mercado J, Sanchez-Perez G, Nakano-Miyatake M, Perez-Meana H. A sub-block-based eigenphases algorithm with optimum sub-block size.

[12] Sharkas M. Application of DCT blocks with principal component analysis for face recognition; In: Proc. of the WSEAS International Conference on Signal, Speech and Image

[13] Dabbaghchian S, Ghaemmaghami MP, Aghagolzadeh A. Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology.

[14] Ajit Krisshna NL, Deepak VK, Manikantan K, Ramachandran S. Face recognition using transform domain feature extraction and PSO-based feature selection. Applied Soft Com-

[15] Aguilar-Torres G, Toscano-Medina K, Sanchez-Perez G, Nakano-Miyatake M, Perez-Meana H. Eigenface-Gabor algorithm for feature extraction in face recognition. Interna-

[16] Owusu E, Zhan Y, Mao RQ. An SVM-AdaBoost facial expression recognition system.

[17] Qin H, Qin L, Xue L, Yu C. Gabor-based weighted region covariance matrix for face

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6:1112-1117

The evaluation results using the AR databases demonstrate that these algorithms provide good results also when it performs identity verification tasks, providing a theoretical criterion which allows selecting the threshold such that the system be able to provide a previously specified false acceptance or false rejection rate.


*4.1.6 Factors predictive of metastasis*

Prognosis is better than ciliary body or choroidal melanoma with a 10-year metastasis of 7% as compared to 25% in choroidal melanoma and 34% for ciliary body melanoma.


*Article* **Spin Polarization of Electrons in Two-Color XUV + Optical Photoionization of Atoms**

**Nikolay M. Kabachnik 1,2,\* and Irina P. Sazhina 2**

**Abstract:** The spin polarization of photoelectrons in two-color XUV + optical multiphoton ionization is theoretically considered using strong field approximation. We assume that both the XUV and the optical radiation are circularly polarized. It is shown that the spin polarization is basically determined by the XUV photoabsorption and that the sidebands are spin polarized as well. Their polarization may be larger or smaller than that of the central photoelectron line depending on the helicity of the dressing field.

**Keywords:** multiphoton ionization; two-color; photoelectrons; spin polarization; femtosecond pulses; xenon atom


*7.4.5 Semisolid product*

Semisolid grade is a compact stick design in a semisolid state just like a glue stick. It can be used mainly for repair application where worker can carry and apply easily to vertical or ceiling area of applications. This product has a medium strength and high strength.


*4.1. NMR Sample Preparation*

Reference 1 and Reference 2 were prepared as previously described [15]. Human plasma extraction: metabolites were extracted from 1 mL of human plasma via a methanol and chloroform liquid–liquid extraction. The aqueous phase was transferred to a 15 mL Falcon tube and freeze-dried. The powder was reconstituted in 180 μL of 50 mM phosphate buffer at pH 7.4 in D2O, and then immediately transferred to a 3 mm NMR tube for NMR data collection. The NMR standard, DSS-d6, was added to each sample for chemical shift referencing.

## *4.2. NMR Experiments and Data Processing*

All NMR spectra were acquired on a Bruker AVANCE III solution-state NMR spectrometer equipped with a liquid helium-cooled TCI (H/F, C, N), deuterium lock, and a cryoprobe operating at a frequency of 599.773010 MHz for proton and 150.822998 MHz for carbon. NUS schedules were generated using a Poisson gap distribution with a sinusoidal weight of two and random seed generator [51]. The same 25% NUS schedule and seed were used for all experiments. All NMR data were collected at 298 K.

The spectral widths along the direct and the indirect dimensions were set at 9578.544 and 24,132.982 Hz, respectively. The number of complex points in the direct dimension was set at 512, and in the indirect dimension set at 32 with a 25% NUS sampling schedule. The number of scans for the 1D 1H experiments was set to 64 (d1 = 1.5 s), 72 (d1 = 1.2 s), 84 (d1 = 0.8 s), and 92 (d1 = 0.6 s). The number of scans for the 2D 1H-13C HSQC experiments was set to 36 (d1 = 1.5 s), 48 (d1 = 1.2 s), 72 (d1 = 0.8 s) and 84 (d1 = 0.6 s), respectively. The scan numbers were selected such that the total acquisition time for each 1D- and 2Dexperiment was on average 246 s, and 69 min., respectively. The transmitter frequency offset was set to 75 ppm in the 13C dimension and 4.7 ppm in the 1H dimension.

The spectral data were processed using the NMRPipe software package, as previously described [52]. The NUS data were reconstructed using iterative soft thresholding according to the hmsIST algorithm [51] to generate the same number of direct dimension data points and twice the number of indirect dimension data points, 512 (N2) × 256 (N1). Both the NUS and US NMR data were zero-filled, Fourier-transformed and manually phasecorrected to yield a final digital resolution of 2048 (N2) × 2048 (N1) points. Chemical shift queries, metabolite identifications and quantifications were performed using the COLMARm NMR webserver (http://spin.ccic.ohio-state.edu/index.php/colmar (accessed on 4 February 2021) [35]. The metabolite list is presented in the Supplementary Materials section for Reference 1 (Table S1) and Reference 2 (Table S2). The resonance assignments were used as previously reported [15].

#### **5. Conclusions**

In this study, we demonstrated the ability of Gd to improve the sensitivity of 2D NUS NMR spectra for the analysis of metabolomics samples. The addition of Gd led to an overall improvement in S/N and mean signal intensity for metabolites in both a model mixture and plasma samples. In the model mixture, the addition of Gd led to a 1.25-fold improvement in NMR signal intensities, which resulted in 1.7- and 1.6-fold improvements in LOD and LOQ, respectively. Interestingly, a significant improvement (>2-fold increase) was observed for metabolites with the lowest peak intensities, which suggests that the combination of Gd with NUS may improve the coverage of the plasma metabolome. The addition of Gd also maintained the highly linear intensity measurements that were correlated with a wide range of metabolite concentrations (50 μM to 2 mM). The reproducibility of intensity measurements, as noted by a decrease in %CV for both the model mixture (8% to 7%) and the plasma samples (15% to 10%), was similarly improved with the addition of Gd. Collectively, our results suggest that supplementing metabolomics samples with 0.25 mM Gd can improve the sensitivity of 2D NUS 1H-13C HSQC spectra and enhance the overall quality of the resulting data analysis. The routine adoption of PRE by the metabolomics community may expand the utility of multidimensional NMR to empower future biomarker discoveries.

**Supplementary Materials:** The following are available online. Figure S1. Chemical structure of Gadobutrol (Gd), Figure S2. Effect of addition of relaxation agents on 1H-13C HSQC 1D 1H spectral intensity for a model mixture (Reference 1) of metabolites in time equivalent experiments, Figure S3. Plots showing (a) mean peak intensity and (b) mean S/N for Reference 1 with and without addition of Gadobutrol, Figure S4. Linear regression curve of Reference 2 metabolites, Table S1. The list of

metabolites in Reference 1 model mixture, Table S2. The list of metabolites in Reference 2 model mixture, Table S3. Comparison of LOD and LOQ at 0.8 D1 and at 1.5 D1 with and without Gd.

**Author Contributions:** Conceptualization, R.P. and E.M.O.; investigation, C.H., N.T. and B.Z.; data curation, C.H.; writing—original draft preparation, C.H. and E.M.O.; writing—review and editing, R.P. and E.M.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Olaris, Inc., and, in part, by funding from the National Science Foundation under Grant Number (1660921) to R.P. and the National Institutes of Health Grant Number (P20 GM113126, NIGMS, Nebraska Center for Integrated Biomolecular Communication) to R.P. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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

**Informed Consent Statement:** Not Applicable.

**Data Availability Statement:** Data available on request due to proprietary restrictions. The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The authors would like to thank Leonardo O. Rodrigues, Chen Dong, Srihari Raghavendra Rao, Tan Chui-Mae, Josephine J. Wolf, Kanchan Sonkar, Monty Mahantesh Kothiwale, Charles Sang, Gerhard Wagner, Amber Balazs and David Emmons, for their many valuable discussions that motivated the writing of this communication.

**Conflicts of Interest:** The authors report that this work (design of the study, collection, analysis, and interpretation of data) was supported by Olaris, Inc. Chandrashekhar Honrao, Nathalie Teissier and Elizabeth O'Day are employees at Olaris, Inc. Bo Zhang is a former employee. Robert Powers is a member of the Scientific Advisory Board at Olaris, Inc.

**Sample Availability:** Samples are or not available from the authors.

#### **References**


**1. Introduction**

The degree to which human actions have an effect on changes in the environment has been a key subject of study for land management researchers. One can measure these changes, which often have a spatial dimension, both qualitatively and quantitatively. Nevertheless, mapping of temporal and spatial changes of urban and rural land still remains a challenging task because the technological tools and instruments have so far not been adequate to support the daily practice and the spatio-temporal needs of planners and decision makers. The traditional methods used for land use planning such as field surveys and participatory mapping are time consuming, costly and labor intensive. Advancements in the data acquisition technologies and the availability of improved computational power has made it possible to make practical use of the algorithms which earlier could only be called theoretical solutions but could not be practiced [1]. The methods of imaging land

**Citation:** Chaturvedi, V.; de Vries, W.T. Machine Learning Algorithms for Urban Land Use Planning: A Review. *Urban Sci.* **2021**, *5*, 68. https://doi.org/10.3390/ urbansci5030068

Academic Editors: Pierfrancesco De Paola, Francesco Tajani, Marco LocurcioandFelicia DiLiddo

Received: 2 August 2021 Accepted: 12 September 2021 Published: 14 September 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/).

use fall in the domain of remote sensing (RS) and processes by which they are utilized for their analysis is through Geographic Information Systems (GIS) tools. RS datasets are a vital source for assessing the land-use and land-cover processes. RS datasets provide large scale coverages right from the regional to the global scale. One of the ways to understand the status and changes in both natural and built environment is through interpretation of RS datasets. In recent decades, RS sensors and techniques have become increasingly sophisticated. They can provide a large volume of data with superior quality and high spatial resolution [2]. The availability of high-resolution data like LIDAR, RADAR, MSS, Hyperspectral, UAV borne data and other commercially available satellite data and data from other airborne platforms has improved the capabilities and understanding of land use planning. While classical methods compartmentalize graphic and non-graphic data for analysis, a combined use of advanced RS and GIS applications can integrate both spatial and socio-economic factors which may support a better understanding of the dynamics of the society needed to improve land use planning.

The spatial analysis underlying land use planning encompasses land use classification, growth, zoning, restrictions, allocation and change. There are various ML algorithms that make it possible to model aspects of land use planning. However, the algorithms differ in their performances, in terms of spatial accuracies and output possibilities. According to Hagenauer et al. (2019) [3] "ML comprises a set of inductive models that recognize patterns and/or minimize the prediction error of complex regression functions, by means of a repeated learning strategy from training data, linking an output such as land-use change to several underlying drivers. Once learned, the model can then be used to estimate previously unseen cases and predict future land-use change. There are many simulation models to model land use change and growth using ML techniques". In other words, the benefits of ML models are numerous, and have the ability of dealing with large amounts of data and a large number of variables, assigning relative importance to the variables, alongside the ability to model complex nonlinear relationships as well as interactions between drivers, while not being grounded in restrictive distributional assumptions of the input data that are hard to achieve in practice [3]. Despite these benefits, there is still limited literature which systematically compares the currently used ML algorithms in light of the specific needs and requirements of land use planning, classification, change, transition and growth.

The aim of this article therefore is to present the results of a comparative study of the commonly used ML algorithms against the background of the needs and requirements of land use planning and land use planning related decision making. The article identifies ML algorithms which give optimum results in terms of computation cost and accuracy levels.

The sequence of the article is as follows. We first present a theoretical grounding of the land use classification algorithms. Followed by discussion of the methodology adopted for reviewing the various ML algorithms for land use planning and finally conclude the findings after reviewing the functionalities, applications and computational power of the ML algorithms.

#### **2. Theoretical Perspective**

Land use planning is a process that ensures the judicious use of land by group of people who benefit from it. In order to make the best use of the limited available land and its resources, land use planners and policy makers need to intervene by incorporating guidelines/regulations on the use of land and to sustain the natural resources. The governments and administrative bodies involved in land use planning impose regulations on the use of land which include but are not limited to zoning, land use control, land use restrictions and allocation [4]. Rapid urbanization leads to substantial unplanned growth which is not healthy for the environment. It leads to degradation, air pollution and contamination of water resources. A major problem faced by urban planner and decision makers is to channelize this growth. In order to ensure the growth is planned and systematic spatial, non-spatial and temporal data has to be made available. To monitor and

solve the problems of unplanned growth, EO and GIS data along with socio-economic data provide vital source of information for updating of land use maps.

In remotely sensed data, spatial resolution and temporal frequencies are the two important factors to study land use change. Proprietary and open-source data provided by various international and national agencies are custodians of EO and GIS data. The land administrators and land use planners need information on built-up area, non-built-up area, water bodies, green area, forests (urban), land use patterns, road network, drainage systems, etc. Most of this data can be obtained and/or extracted from images. Some types of data, such as open street map, USGS earth explorer, Sentinel hub, Copernicus open access hub, GHSL, etc., are repositories of open-source data and are utilized for spatial data requirements. Apart from open-source data, commercially available satellite images, aerial photographs, LIDAR data are used for various land use planning application such as cadastral boundary extraction, 3D feature extraction, 3D modeling, etc. The first step in the various stages of land use planning is to study the past data which is available in the form of master plan, census data and other statistical data available with various government and non-government agencies, the second stage is to study how far the plan has been fulfilled by studying with the help of EO based data against the master plan projections and the third stage will be projecting the land use plan for future, depending on the projection of the statistical data being generated as future projection, i.e., it will be geared towards fulfillment of those projections which will also be dependent on the government policies. A comprehensive geospatial database has to be developed to assess the existing land use and model the future changes. The type of data requirement depends on the type of land use planning problem in hand which needs to be addressed. Depending on the purpose, imagery could be of high resolution and in multi band or it may suffice to have imagery of low resolution as can be seen in Table 1 depicting the data requirements and the applications to measure the indicators.

**Table 1.** Land use planning indicators with measurements, data required and applications.

#### *2.1. Machine Learning Based Algorithms*

Several ML algorithms have been tested for their performance on different kinds of datasets for land use classification and simulation of land use planning processes. The more popular algorithms are support vector machine, neural network, Markov random field, GANS and random forest. These algorithms are experimented on different data sets individually and in combination. The article is a review of the functionalities of these algorithms and their application in land use planning.

There is ongoing research for new methods of ML to take land use mapping to a higher plane. Support vector machines (SVMs) have been applied in a number of research papers and have been compared for their performance in land use classification with other ML algorithms such as random forest (RF), neural network. SVMs is a group of non-parametric ML algorithms. The core operation of SVMs is to construct a separating hyperplane (i.e., a decision boundary) on the basis of the properties of the training samples, specifically their distribution in feature space. In many instances, classification in high dimension feature spaces results in over-fitting in the input space, however, in SVMs over-fitting is controlled through the principle of structural risk minimization. The empirical risk of misclassification is minimized by maximizing the margin between the data points and the decision boundary [5]. In case of computational requirements SVMs work well with small data sets with fewer outliers [6]. Among the decision tree algorithms like CART (classification and regression tree), ID3 (iterative dichotomizer 3) more commonly used algorithm for land use classification is RF. One of the benefits of RF algorithm is that it can be used for both classification and regression. RF works as an ensemble learning algorithm based on decision tree classifiers, bagging, and bootstrapping. Each tree is trained by bootstrapping, using different samples from the training data. Additionally, each tree is trained using a random subset of the predicting variables. RF may use thousands of decision trees, where each tree casts a vote and the prediction of the class is decided by the majority vote. A big committee of randomly created decision tree determines the classification, hence the name, random forest [7]. RF can handle large number of variables without need of deleting any and bringing out the relative importance of each of the variables. As compared to SVMs, RF do not have hyperparameters to tune like choosing the right kernel, regularization, penalty, the slack variable, however, the complexity and computational cost increase with the increase in the number of trees in the forest. A list of some of the most commonly used variables is given in Table 2.

**Table 2.** *Cont.*

Apart from SVM and RF, another ML algorithm which has been widely applied for land use classification is deep learning methods. In 2006, deep learning was introduced by Hinton et al. (2015) [10]. Deep learning methods are representation learning methods composed of multiple layers and each layer computes a new data representation from the representation in the previous layers of artificial neurons creating a hierarchy of data abstractions [10]. Among the group of deep learning methods is convolutional neural network (CNN) composed of convolution and pooling that are concluded by a fully connected neural network layer and a proper activation function, i.e., in models that directly reconstruct an output image prediction, such as U-Net and generative models, the fully connected network and activation function is not needed [11]. In deep learning, artificial neural network (ANN) has been gaining importance in land use planning studies. ANN is a computational ML model based on multilayer perception composed of processing elements forming three kinds of layer (input, hidden, output) which are called perceptrons [12]. Deep learning algorithms work well with relatively large datasets with supporting infrastructure to train them in reasonable time. There has been increasing interest in the Markov random field-based methods for land use classification and land use change as it helps in generating a smooth classification pattern. Markov random field (MRF) is a statistical model based on probability theory which efficiently represents dependency between pixels in a spatial domain. MRF is useful for characterizing spatial-contextual information and has been commonly used for image segmentation, texture analysis, edge detection and image restoration. MRF has been used for linear feature detection with satisfactory results. MRF for modeling spatial context relies on its relationship to Gibbs random field which is a useful way to apply MRF to deal with context [13]. In the paper "Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality" [14], six types of image features perimeter, line segment detector (LSD), Hough transform, gray-level co-occurrence matrix GLCM, HoG, and local binary patterns (LBP) were extracted to identify urban poverty in Wuhan, China. The paper utilizes four machine learning regression approaches random forest (RF), Gaussian process regression (GPR), support vector regression (SVR), and neural network (NN) to study whether the features derived are helpful in differentiating urban poverty. It was concluded in the paper that textural features are important in identifying urban poverty in the study area.

In addition to the above-mentioned ML algorithms, there are several simulation models for the purpose of mapping and growth projections of land use. There are mainly two groups according to the key mechanisms to simulate the process of land use change rule-based/process-based models and empirical-statistic models [15].

#### *2.2. Urban Land Use Models*

Cellular Automata (CA)have been defined as discrete spatio-temporal dynamic systems based on local rules. In cellular models, geographic space is represented in the form of a geographic grid, such as the cells in a raster Geographic Information System. They are preferred when model states and the probabilities of transitions among those states are known and stable. They are most suitable for measuring, detecting and predicting change processes such as land use change and urban growth [16].

Cellular automata have capacity to handle temporal dynamics. Cellular automata have the following basic features:

The transition function is applied to each cell of the grid across several iterations. Therefore, cellular automata have an evolution process because some cells are changing their states across the different iterations [17]. The most commonly used cellular automata model is the Slope, Land cover, Exclusion, Urban growth, Transport and Hill shade (SLEUTH) model which has been in application for a long time. SLEUTH model has been widely used for simulating urban growth and land use change. SLEUTH is open source and has been developed in C programming language. As described by Berbero ˘glu et al. (2016) [18].

*"The program involves as a series of nested loops: the outer control loop repeatedly executes each growth "history", retaining cumulative statistical data, while the inner loop executes the growth rules for a single iteration, assumed to be a "-year-." The rules apply to one cell at a time and the whole grid is updated as the iterations complete".*

Statistical modeling methods are widely used for modeling, assessing, qualifying, quantifying and predicting the degree/extent/direction of land use change and growth. An example of a statistical model is the logistic regression model. Logistic regression is a predictive statistical modeling technique which applies multivariate regression to predict future land use based on historical land use changes, their spatial (change) characteristics and other potential drivers [15]. It is easier to model land use change using statistical modeling methods as their calibration is not so computationally intensive compared to rule-based models like cellular automata [19]. In logistic regression, social and economic factors like population density, accessibility to services, distance to commercial and industrial area, mean incomes, etc., can be incorporated in the model. Logistic regression analysis has been one of the most widely used approaches in the past two decades for predictive land use modeling by means of variation of inductive modeling [19].

Agent-based modeling (ABM), which is a forward-looking simulation technique which calculates "agents," each of which represents an actor and how they interact with their "environment" or the total system. The models represent real and imagined scenarios, which allow for the discovery of potentially emergent issues or phenomena. Such models have increasingly been used to analyze complex issues like land use change. Agents are independent entities which have set goals to achieve. The agents can be countries, landowners, land tenants, citizens, etc. ABM is used to simulate human behavior in cities, for example whereby policy makers, planners or citizens are entities (agents) which interact with the city environment and are capable of making urban planning decisions [16]. In a rule-based approach, their behavior is fixed, meaning that decision-making functions and algorithms remain unchanged (i.e., they always react in the same way when confronted with a particular situation). While agents react to changes in their spatial and social environment, they neither adapt their rules in response nor intelligently learn from previous

experiences. ABM can be useful to study the changes in the land use and to evaluate the projections.

The various development environment used for agent-based modeling are Anylogic, Cormas, Cougaar (via OpenMap) Framsticks, Janus (using JaSIM), MASON, Repast, SeSAm, VisualBots, and NetLogo. These environments provide tools to develop an agentbased model and provide a platform to represent model components, control model function, and evaluate and visualize model output [20].

Hybrid approaches have been developed by integrating different ML methods which has resulted in better performance and assessment. An example of a hybrid method is the integration of logistic regression, Markov chain and cellular automata [21] to model urban expansion in the metropolitan area of Tehran, Iran. The results of the simulation were compared with the actual land use map and the result matched 89% between the simulated and the actual. Another example is integration of cellular automata Markov chain (CA-MC) with artificial neural network [22] to enhance the simulation capacity in predicting the changes in land use. The study integrates ANN and CA-MC to incorporate several driving forces (economic, spatial and environmental variables) that impact land use change. The integration and the influence of the driving forces improved the model prediction. Among the hybrid approaches Kamusoko and Gamba (2015) [23] tested random forest–cellular automata to study urban land change in Harare metropolitan province, Zimbabwe. Cellular automata was used to calculate multiple-step transition rates from land use/land cover maps (1984, 2002 and 2008). RF model was used to compute transition potential maps. The study then compared this model with SVM-CA and logistic regression (LR) and CA. The result showed that RF-CA outperformed SVM-CA and LR-CA models. Hybrid Urban Expansion Model (HUEM) was used by Mustafa and Cools (2018) [24] that integrates LR, CA, AB to simulate future urban development in Wallonia, Belgium. The urban expansion is simulated between 1990 and 2000. The calibration results are analyzed by comparing the projection for the 2000 simulated map with the actual 2000 land use map. The HEUM model uses three agent sets, developer agent, farmer agent and planning permission authority agent. The performance of HEUM is compared with other spatial expansion models, i.e., Logit model, CA model, CA-Logit model. The comparison shows that the performance of HEUM model is better than other models in terms of allocation ability.

#### **3. Materials and Methods**

#### *3.1. Research Approach*

The review article is based on an integrative approach relying on secondary data. It is a concept-centric qualitative approach drawing on deductive logical reasoning to create new scientific knowledge from the existing facts in literature and inform future research, policies and decision making. The researchers use their own knowledge about the topic in order to critically analyze and synthesize the existing knowledge about different concepts, theories and principles and deduct their own conceptualization from the reviewed general facts. Webster and Watson [25] and Torraco [26] argue that this approach adopts critical review, analysis and synthesis of existing knowledge about the topic under research, with the object of devising possible relationships among various research variables, identifying knowledge gaps and contradictions, and seeking opportunities for future research. The aim of the research is the potential to reconceptualize the expanding and diversified knowledge base of the topic as it develops over time [25]. Spatial and temporal limitations (boundaries) were not considered throughout the review process for internal validity purposes. This led to review of both old and new geographically unlimited available literature material on the topic as an appropriate method for this type of research approach which requires comprehensive and broad literature. For the purpose of avoiding various conceptual deviations, misuse and linguistic bias, both empirical (primary) and review (secondary) literature were limited to English language. The following subsection explains the process and method for identification of literature (search, selection criteria and its sources, database, repositories) screening, eligibility and inclusion (Figure 1).

**Figure 1.** PRISMA showing the steps of methodology.

#### *3.2. Data Sources and Research Method*

Once the limits of the literature search were defined, we conducted the literature identification. The search strategy involved keywords and their combination. The list of keywords and their structure is given in Table 3.

**Table 3.** *Cont.*

The methodology adopted consisted of selecting books, articles, journals, review papers and research papers dealing with land use planning and the various ML applications adopted for studying and analyzing the various components of land use planning. The objective of literature search and selection was not to derive an all-encompassing list of author-centric publications referring to specific ML algorithms, but to derive a contentspecific review of publications which most adequately describe specific ML algorithms, specifically the procedures, popularity (i.e., frequency of use, frequency of citation, etc.), advantages and disadvantages, applicability, functional and interoperability requirements. The framework of the paper is designed on the basis of both test case and use case approach [27]. Research papers where the algorithm is a function of the commercial software and also where it has been independently developed by the user wherein functional requirements are taken care by the developer were examined.

Land use planning is a flexible, adaptive and iterative process and is based on the needs, capacities and the institutional guidelines in place. There are two approaches to land use planning, one is systematic or top-down approach which is institutionalized and is based on technical surveys and the other is collaborative/participatory planning also known as the bottom-up approach which is based on people's participation and is in accordance with the socio-cultural, economic, technological and environmental conditions.

There is limited interoperability among the ML methods. The data types are generally incompatible. Some of the programming languages in which ML tools are written are java, R and python. ML tools are supported on third party environments and can be run in the background without the user having to write a code. The selected ML algorithms can be reviewed for their functionality, interoperability requirements and their applications in land use planning. The emphasis is given on land use growth, land use zoning, land use classification, land use restrictions, land use allocation. These ML algorithms were chosen because of their popularity (i.e., frequency of use, frequency of citation, etc.), in land use planning phases, processes and/or decision making. There is awareness of the chosen algorithms among researchers and they have wide range of application. For the purpose of reviewing the algorithms, relevant literature was selected where the algorithm is used as an application and the functionalities of the algorithm are used and analyzed for their performance. For the comparison of the algorithms/models the advantages and disadvantages are discussed. The papers range from classification of data to simulation models for land use change and growth.

A comparison of various ML algorithms/models for land use planning which includes land use classification, land use change and land use growth indicates that each of the algorithms have their own advantages and disadvantages. Selection of the appropriate algorithm/model depends largely on the objective and the quality/type of data sets available (both graphic and non-graphic).

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

Machine learning algorithms are listed in Table 4 with their advantages and their applications.

**Table 4.** Machine learning algorithms and their advantages.

Support vector machine (SVM) is popular because of its ability to use nonlinear boundaries and locate boundaries of training data. Classifying algorithm SVM does not require very large training samples. Sensitivity of SVM algorithm is experimented on rapid eye images in [28] for land use classification. For comparison, the traditional maximum likelihood classification was also performed in the same set of images. The results of the research indicated that model parameters and kernel type play an important role in the accuracy of the classification using the SVM algorithm. However, it can be deduced from various research papers that the results of the SVM depend on the choice of the kernel and the regularization parameters.

ANN approaches have a distinct advantage over statistical classification methods in that they are non-parametric and require little or no a priori knowledge of the distribution model of input data. Additional superior advantages of ANNs include parallel computation, the ability to estimate the nonlinear relationship between the input data and desired outputs, and fast generalization capability. Many previous studies on the classification of multispectral images have confirmed that ANNs perform better than traditional classification methods in terms of classification accuracy, such as maximum likelihood classifiers [29].

Random forest algorithm is used for satellite and aerial image classification. Its ability is to handle large numbers of features like texture, contextual, spatial, spectral and structural without affecting the overall accuracy of the classification. In an experimental study, the authors [7] integrate the random forest method with spatial metrics and texture analysis and find that the use of this method seems to be limited to flat cities with a small number of high-rise constructions. In the research by Belgiu et al. (2014) [30], building types are classified by means of domain ontology and machine learning using airborne laser scanner data. In the study, RF classifier has been applied to select the relevant features for predicting the classes of interest. RF classification model has been used to study the urbanity index between Germany and France [31]. The study involved defining urbanity on the basis of travel behavior. The aim of the study is the identification and comparison of different urban structures at zip code level across Germany and France. It was concluded that the adaptation of the RF model provided an effective methodology for the automated assessment of level of urbanity of different areas.

Cellular automata models are sometimes described as a type of agent-based model. The key difference is that, for cellular models, spatial entities are the basic units of simulation and the topology (or connection) between those units remains fixed, whereas agent-based models described in Table 5 represent decision making units that have a flexible and dynamic relationship with land units [20]. Specific advantages of agent-based models include their ability to represent individual decision makers and their interactions, to incorporate social processes, non-monetary influences on decision making, and to dynamically link social and environmental processes [32]. A relevant example of an agentbased model is simulation of informal settlement growth in Dar es Salam, Tanzania [32] where a vector based, micro scale housing model is developed to simulate the growth of informal settlements.

#### **Table 5.** Statistical modeling approaches.

Some of the research work carried out using different methods of ML and modeling by the researchers is given in Table 5 with the advantages, disadvantages and their applications.

#### *4.1. Machine Learning Models and Their Applications*

4.1.1. Algorithms/Models to Study Urban Growth, Patterns and Land Use Change

Several studies have been carried out using empirical data sets to compare the performance of machine learning/statistical models to study the aspects of land use planning. Most of the studies focus on urban growth/expansion, land use change and patterns of land use. The most commonly used models to study urban growth and change are logistic regression and cellular automata. The performance of some of the machine learning algorithms and statistical approaches for the studies reviewed is shown in Table 6.

**Table 6.** Machine learning algorithms and their performance.

**Table 6.** *Cont.*

In addition to the above commonly used ML algorithms and statistical models, several other published papers have applied not so common ML algorithms to obtain their results. To study the factors contributing to urban land use patterns, Decraene et al. (2013) [38] developed a dynamic urban growth model that aims to construct artificial cities from the bottom-up using the measure of spatial entropy and index of dissimilarity. The study applies a CA model which relies on the range of influence of different land use sectors (residential, business and industrial). Four statistical modeling approaches are compared for modeling land use change; they are Markov chain, logistic regression, generalized additive models and survival analysis [39]. The methods were compared on the basis of accuracy (overall and by land use type, sample size and spatial independence by means of conventional and spatial cross-validation). Logistic regression and survival analysis were more accurate for specific land use type. It was concluded that hybrid approaches outperformed individual statistical approaches. SVMs have been used in a variety of application domains including land use change modeling. SVM based land use modeling technique is applied to study the change in land use classes for the municipality of Zemun, Republic of Siberia for a 10 year time period [40]. The SVM modeling procedure entails finding a function which maps attributes of a grid cell at a time point to its land use class at time + 1. The population change index (PCI) was used as an attribute and it was calculated as the ratio of change in the number of inhabitants per each cell between two censuses: 2001, 2011. Correlation based feature subset reduces the number of initial attributes to an informative subset for a more efficient model less sensitive to the SVP parameter changes. It is concluded that the SVM based LUC model is sensitive to the choice of datasets used for training and validation, the selection of attributes and optimal learning parameters.

#### 4.1.2. Commonly Used Algorithms for Land Use Classification

RF algorithm is evaluated for its performance in land use classification (Rodriguez-Galiano, Ghimire, Rogan, Chica-Olmo, & Rigol-Sanchez, 2012) [41] using multitemporal Landsat-5 thematic mapper data of a large heterogeneous area. The study area selected for the project is the province of Granada in Southern Spain. The study also compares the performance of the RF algorithm with conventional classification trees (CT). It was concluded that RF provides more significant differentiation of land cover categories. A hybrid approach of classification has been adopted by [42]. In the modified method, the object-oriented approach is combined with deep convolutional neural network (COCNN). The object-oriented method is used to construct a multiscale sample set to provide high precision training data. Ten land use types are classified using remote sensing images covering an area around Fuxian lake. Convolution kernels is regarded as the most sensitive element of the CNN and is responsible for directly extracting the lowest level feature on the original image. The result of this approach has been compared with the outputs obtained from CNN and the modified approach has shown to improve the results. It is concluded in the paper that the result is dependent on the kernel size and for the object-oriented training sets a kernel size of 3 × 3 has been used.

## 4.1.3. Limitation

The research cannot be exhaustive as the number of papers which have been published on urban land use planning using ML algorithms and statistical models majorly focus on two aspects of urban sprawl/growth and land use transition/change. The author was interested in other aspects of urban land use planning like zoning, land allocation, land restrictions but sufficient published literature was not available to cover these aspects. There are numerous published research papers on use of classification algorithm to classify land use but the majority of them are covering algorithms like deep learning, SVM, random forest and very few papers on algorithms such as logistic regression, MRF, k-nearest neighbors, naive Bayes, etc.


*3.2. Effect of Crack Geometry on the Chloride Diffusion Process in Cracked Concrete*

The non-steady-state chloride diffusion process in cracked concrete was simulated using the finite difference method. Rectangular, V-shaped and real cracks were adopted in the simulations. The detailed geometries of these cracks are shown in Figure 7. The rectangular cracks had different crack depths (i.e., 25 mm, 35 mm and 45 mm). The depth of the V-shaped and real cracks was set to be 45 mm. The geometry of real crack was adopted from [33]. All of the cracks had a crack width of 0.6 mm at the left surface of the specimen.

**Figure 7.** The crack geometries used in non-steady-state diffusion simulation (crack width at the left surface: 0.6 mm).

The simulated chloride concentration distributions in concrete with rectangular cracks after one year of immersion in NaCl solution are shown in Figure 8. The chloride penetrated the concrete specimen quickly through the crack, and the chloride penetration depth was largely dependent on the crack depth. A higher crack depth led to a deeper chloride penetration depth. Hence, the crack depth is the key factor that influences the service life of concrete structures in chloride environments. The chlorides also penetrated concrete through the crack surfaces; hence, the chloride concentration in concrete near the crack surfaces was also increased due to the presence of cracks.

**Figure 8.** The simulated chloride concentration in cracked concrete with rectangular cracks after one year of immersion in NaCl solution.

The chloride concentration distributions in cracked concrete with different crack geometries are shown in Figure 9. At the immersion age of 0.01 years, it was quite obvious that the chloride concentration in the real tortuous crack was lower than that in the rectangular and V-shaped cracks. The tortuous crack inhibited the fast chlorides' diffusion in concrete cracks. When the immersion age increased, the chloride concentration distributions in different cracked concretes were close to each other. The chloride concentrations at the height of 25 mm in cracked concrete (i.e., at the center of the cracked concrete in the vertical direction) are shown in Figure 10. At the immersion age of 0.01 years, the

chloride concentration at the real crack tip was much lower than that in the rectangular and V-shaped cracks, and for the real crack, the chloride concentration beyond the crack tip was also lower than that in the other two. At the immersion age of one year, the chloride concentration distributions in cracked concrete with rectangular and V-shaped cracks were almost the same. The chloride concentration in the real crack was only slightly lower than that in the rectangular and V-shaped cracks. This suggested that the crack geometry influenced the chloride diffusion process in cracked concrete soon after the chloride penetrated the crack. However, for a long-term immersion with a given crack depth, the crack geometry did not show much influence on the chloride penetration depth in cracked concrete. In comparison, the influence of crack depth was more significant than that of crack geometry on the chloride penetration depth in cracked concrete in the long term.

**Figure 9.** The chloride concentration distribution in concrete with different crack geometries.

**Figure 10.** The chloride concentrations at the height of 25 mm in cracked concrete: (**a**) at the immersion age of 0.01 years; (**b**) at the immersion age of one year.

Generally, in cracked concrete, the crack width influences the short-term chloride diffusion process, but for long-term chloride diffusion, the influence of crack depth is more significant. That is, the crack depth is the main factor that influences the residual service life of a cracked concrete structure in chloride environments. Moreover, when predicting the residual service life of cracked concrete structures with numerical methods, the real crack could be simplified as rectangular (or other shaped) crack in the simulations.