*2.3. Risk Factors Leading to Cost and Time Overruns*

Studies have identified various critical success factors for construction projects. These included time, cost, quality, safety, client's satisfaction, employees' satisfaction, cash-flow management, profitability, environment performance, learning and development, etc. [40]. However, the majority of the past research have extensively focused on the three major factors for success in construction industry, which included cost, time, and quality [41]. It has been elicited that over 70% of public construction projects in Saudi Arabia have experience delays [42]. Various risk factors and challenges explaining the time and cost overruns for these projects have been uncovered. Baghdadi and Kishk [43] identified 54 risk factors in the context of external, internal, and force majeure in aviation construction projects, which were causing duration delays as well as cost overruns. Mahamid [44], focusing on the factors affecting performance in construction projects, identified various risks, including poor communication among project participants, poor labour productivity, poor planning and scheduling, payment delays, escalation of material prices, poor labour productivity, and poor site management. Regarding the causes of disputes, Mahamid [44] identified 29 direct and 32 indirect dispute causes, of which major direct dispute causes included delay in progress payment by the owner, unrealistic contract duration times, change orders, poor quality of completed work, and labour inefficiencies. Major indirect dispute causes included inadequate contractor experience, lack of communication between the construction parties, ineffective planning and scheduling of the project by the contractor, cash problems during construction, and poor estimation practices.

Focusing on the design risks, Sha'ar et al. [45] identified unstable client requirements, lack of proper coordination between the various disciplines of the design team, awarding the contract to the lowest price regardless of the quality of services, lack of skilled and experienced human resources in the design firms, lack of skilled human resources at the construction site, delaying of due payments, lack of a specialised quality-control team, lack of professional construction management, delaying the approval of completed tasks, and deficient drawings and specifications. Various other challenges, such as those related to subcontractors, labour, machinery, availability of materials, and quality; and client-related risks such as financial issues, issues related to design documents, change in codes and regulations, scope of work, accidents on site, lack of expertise, re-designing, unqualified workforce, organisational culture, and poor contract management were identified from various studies conducted on Saudi Arabian construction industry [46–48].

Furthermore, the causes of the cost and time overruns factors differ between various projects/buildings. For instance, when comparing the delay factors between road infrastructure and building projects, a recent study [49] found that the major critical delay factors for road infrastructure projects included inadequate contractor experience and payment delays to the contractor, while the shortage of materials and financial difficulties of contractor were most salient for building projects. For tall building projects, the major causes of delay and cost overruns identified in [50] included "client's cash flow problems/delays in contractor's payment", "contractor's financial difficulties", and "poor site organization and coordination between various parties". Another study focusing on regular manufacturing and building construction [17] identified delays in progress payments, difficulties in financing the project by contractor/manufacturer, slowness in decision making, late procurement of materials, and delay in approving design documents as the major causes of cost and time overruns. In specialised construction projects, such as railways, the causes were found to be related to "Client's decision-making process and changes in control procedures", "Design errors (including ambiguities and discrepancies of details/specifications)", "Labor skills level", "Design changes by client or consultant", and "Issues regarding permissions/approvals from other stakeholders" [51]. In addition, Allahaim [14] emphasised causes and classifications as differing by project type and stakeholder, with overall cost overrun depending on the type of project: power and health projects (60% cost overruns), transport and water projects (40% cost overruns), and education projects (30% cost overruns). Aljohani et al. [52] carried out a review of the literature and identified 173 causes of cost overrun in seventeen contexts, with the main ones being frequent design change, contractors' financing, payment delay for completed work, lack of contractor experience, poor cost estimation, poor tendering documentation, and poor materials management. The authors concluded that the main causes differed from country to country, and that it would be an inaccurate method to use only the global literature to identify the causes for a specific country [14]. In contrast, Ahady et al. [53] found that most of causes of cost overruns in construction industries of development countries are similar, and the causes are different for every project. The most significant causes of cost overruns were fluctuations and increases in material price. Appendix A shows that various risk factors associated with construction projects from 17 studies [2,4,14,17,44,51,52,52–65].

Hence, the factors causing cost and time overruns may change by the types of construction projects. Therefore, there is a need to focus the research on specific building projects in the context of Saudi Arabia. Furthermore, most studies in the literature probed the causes of either cost or time overruns for the construction industry, but very few considered both. Given these gaps, it is essential that risk factors and risk management techniques in Saudi Arabia have to be studied from time to time in order to prevent any damage/losses and avoid cost and duration overruns in construction projects. Accordingly, the purpose of this study is to identify the influential risk factors that lead to completion delays and cost overruns of government-funded building construction projects in Saudi Arabia, all of which have been subjected to a traditional type of procurement method Standard Public Works Contract (SPWC).

#### **3. Research Methodology**

For this study, the researchers adopted a cross-sectional questionnaire-based survey to identify risk factors related to government-funded building construction projects in Saudi Arabia. Figure 1 illustrates the adapted research methodology phases used to achieve the study objectives. This methodology includes four phases: the identification of initial risk factors from the literature, questionnaire design, data collection, and then data analysis.

**Figure 1.** Research methodology process.

The first phase was the identification of initial risk factors from previous literature. A comprehensive literature review was carried out to uncover the various risk factors associated with construction projects. Then, the researchers identified the risk factors that were applicable in the context of Saudi Arabia. A final list of 83 risk factors, classified into nine different groups (client-related, designer-related, consultant-related, contractor-related, labour-related, material-related, equipment-related, external risks, and force majeure), was identified to be relevant for investigation in the context of this study, as shown in Appendix B.

The second phase was the questionnaire's design. The initial questionnaire was developed based on the findings in the previous phase. All these applicable risk factors were included in the questionnaire, which was divided into three sections. The first section of the questionnaire included the participants' demographic information, while the second focused on the level to which project delays and cost overruns affect construction projects. The third section pertained to identifying which risk factors caused project delays and cost overruns by asking three sub-questions for each risk. These included the probability of occurrence (*P*) in projects based on the respondents' perspective and experience, the negative impact of the risk on project's time (*IT*), and the negative impact of the risk on project's cost (*IC*). The questionnaire used a Likert scale of five ratings (1: very low; 2: low; 3: moderate; 4: high; 5: very high) and was designed in both English and Arabic to improve the participants' ease of accessibility and understandability. The researchers conducted a pilot study to validate the prepared questionnaire by distributing it to a set of experts in the construction field. The collected comments were reviewed to develop the final questionnaire.

The third phase of study was data collection. The questionnaire link was forwarded to the experts in construction industry who have been working in relevant building construction projects using various online networks. The researchers adopted snowball sampling [66], requesting the participants to forward the survey link to their colleagues and other relevant professionals. The survey was initially forwarded to 38 experts. Snowball sampling is a more conducive and practical technique for the research scope and to overcome the obstacle of the questionnaire's length, finding the target audience, and providing high-quality information. However, because of snowball sampling, 63 responses were received. After removing eight incomplete responses, the responses from 55 participants were included in the data analysis.

The fourth phase of study was the data analysis of the survey results using MS Excel. The relative importance index (*RII*) calculated the probability of occurrence (*P*) of each risk, the impact of the risk on project's time (*IT*), and the impact of the risk on project's cost (*IC*). Risk Importance (*RI*) was used to determine the level of importance of each identified risk associated with building construction projects by multiplying the probability and impact for each in terms of project time and cost. In addition, the reliability of factor analysis was used to measure the strength of the internal consistency of the identified risk factors, and an agreement analysis test (Cronbach's alpha) was conducted to measure the strength and direction of relationship between the parties involved in this study (client, contractor, and consultant).

#### *3.1. Ranking of Risks*

To carry out data analysis, the relative importance index (*RII*) for each risk was calculated by Equation (1) for the probability of occurrence (*P*) in projects based on the respondents' perspective and experience, and for negative impact (*I*) of the risk on project's time (*IT*) and for negative impact on project's cost (*IC*), using five point Likert scales:

$$RII = \sum\_{i=0}^{n} \frac{\mathcal{W}\_i}{A \times N} = \sum\_{i=0}^{n} \frac{5n\_5 + 4n\_4 + 3n\_3 + 2n\_2 + n\_1}{5N} \tag{1}$$

where

*RII*—is the Relative Importance Index; *Wi*—is the weight given to each factor by the respondents from 1, 2, 3, 4, and 5 for very low, low, moderate, high, and very high, respectively;

*A*—is the highest weight (i.e., 5 in five-point Likert scale); *N*—is the total number of respondents for every variable.

To prioritise risks, the formula of Risk Importance (*RI*) was calculated by multiplying the probability and impact for each in terms of project time and project cost (see Equation (2)). Based on the calculations, risks were classified as "high", "moderate", or "low" importance. Risks that have an (*RI*) value equal to or greater than (0.6) were classified as "high" and were significantly important, and those between 0.6 and 0.4 were classified as "moderate" importance and less than 0.4 as "low" importance:

$$\text{Risk Impactance}; RI = P \times I \tag{2}$$

where

*RI*—is the Risk Importance to determine the level of importance of each identified risk; *P*—is the probability of risk occurrence;

*I*—is the impact of risk on time or cost.


*3.2. Gasification Results*

The results obtained are summarized in Table 3.

**Table 3.** Results of lignite gasification tests.

The length of tests #5 and #6 was shorter compared to the previous tests; however, the gas composition analysed online during the tests reached a steady state after around 10 min, giving stable values for the entire length of the test; a 60 min period was therefore considered sufficient for an evaluation of the results. The amounts of char and ash produced during gasification were evaluated in relation to the total amount of lignite fed during the experimental run. Consequently, the evaluation of their content was not affected by the length of the test.

From the comparison of the results obtained in the first three tests, it is possible to observe that, in general, at higher temperatures, the product gas has a higher quality in terms of the H2O and C conversions and gas yield. Moreover, the gas composition in the tests carried out at 850 ◦C displays an increase in CO and decrease in CO2, probably caused by the higher extent of steam gasification reactions that takes place for higher temperatures, and the lower extent of the WGS reaction, enhanced at lower temperatures (~600 ◦C). Starting from the syngas compositions obtained in the six experimental runs, the equilibrium contents of CO and CO2 were calculated with the software Aspen Plus, in order to compare the experimental and equilibrium compositions. The equilibrium compositions calculated were approximately equal to the gas compositions obtained in the experimental tests, meaning that the WGS reaction had reached equilibrium. For higher temperatures, the char yield is also lower, which is proof of the higher conversion of carbon and thus lower amount of solid un-reacted residual char. Furthermore, the amount of tar produced at higher temperatures is lower compared to the cases with lower temperatures. In the tests conducted at 750 ◦C, the tar content is around 2.5 g/Nm3, while at 850 ◦C, it reduces to 0.5 g/Nm3. Regarding the tar content in general, it was observed that in tests with lignite, the amount of tar produced is lower compared to in similar tests carried out with biomass in the same experimental reactor with olivine as bed material [27,29,30]. Biomass gasification tests in similar conditions carried out at 800 ◦C produced tar contents ≥3300 mg/Nm3, while lignite gasification at the same temperature (test #2) produced a tar content <950 mg/Nm3. This phenomenon could be related to the lower volatile content of lignite compared to biomass (~50% versus ~70%, respectively [27]). In fact, volatile matter has been reported to make organic feedstocks more susceptible to tar formation [31].

The tests with and without air injections in the freeboard were evaluated in a comparison. As expected, in the tests with air injections, there is a higher content of CO2 in the product gas. Furthermore, in the tests with air injections, the H2O conversion was lower, probably because, being a product of combustion, it increases during the reaction. The H2 content and its production in terms of Nl/min are lower compared to the case without air injection. It is possible that some of the produced H2 was consumed in the combustion reactions with the injected O2. The difference between the H2 produced in test #2 (without air injections) and test #5 (with air injections), and their corresponding difference in H2O content in the syngas are both in the order of 0.1 mol/min. This supports the hypothesis that the missing H2 in the tests with air injections has been combusted and converted into additional H2O, as confirmed by the consistency of the reported values of molar flows. In all of the tests, the H2S content was approximately 400 ppm on a dry N2-free basis. The NH3 content, favored by the presence of steam as a gasification agent [32], was around 1000 ppm, with lower values at 850 ◦C. The higher NH3 content at 800 ◦C, noticed both in the tests with and without air injections, was also observed by Xie at al. in gasification experiments on coal macerals [33], and could be related to the combination of two effects: the increase of NH3 production from the N-containing structures in coal enhanced in steam gasification with a higher temperature [32,34,35], and the thermal decomposition of NH3 occurring for increasing temperatures, as found in the literature [36]. For tests #4, #5, and #6, in which air was injected in the freeboard, the presence of O2 increases the possibility of NH3 combustion and consequently decreases its content in the product gas, showing the same trend as a function of temperature.

As mentioned above, in tests #4, #5, and #6, combustion of part of the gas took place, as expected, as a consequence of the air injections, especially those performed in order to increase the temperature in the freeboard and enhance the reactions of tar decomposition. In spite of the combustion reactions, it was observed that the tar content was not really affected by the air injections. The explanation for this could be that in the bench-scale gasifier, the temperature of the reactor is controlled by the electric furnace, so the temperature increase caused by the combustion did not have a relevant e ffect for the promotion of the tar conversion reactions. In addition, due to the reduced dimensions of the bench-scale reactor, the air injections in the freeboard are close to the exit of the gasifier, and consequently, tars could have had too little residence time to decompose. Moreover, the high content of inert N2 in the air injected, which dilutes the O2, could be the cause of the attenuation of the temperature increase due to the combustion, thus reducing the beneficial e ffect of the air injections.

A global mass balance was carried out, taking into account the mass flows of lignite and steam as inputs for the duration of the test. The outputs were calculated as the sum of the masses of the gases produced, the liquid water condensed downstream of the reactor, the tar contents in the samples (reported as the total gas flow), the ash content separated and collected from the bed material after the tests, and the char and residual carbon in the reactor (including the carbon particles deposited on the surface of the filter candle), which were evaluated from the post-combustion carried out after each test.

#### *3.3. Analysis of Materials after the Test*