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Article

Risk Assessment of Prefabricated Construction in Iraq Using Fuzzy Synthetic Evaluation

by
Maysoon Abdullah Mansor
* and
Shaalan Shaher Flayyih
Department of Civil Engineering, College of Engineering, Tikrit University, Tikrit 34001, Iraq
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(10), 1622; https://doi.org/10.3390/buildings15101622
Submission received: 16 March 2025 / Revised: 2 May 2025 / Accepted: 8 May 2025 / Published: 11 May 2025
(This article belongs to the Section Construction Management, and Computers & Digitization)

Abstract

Prefabricated construction is an effective method for reducing project time and waste and improving quality and safety compared to traditional construction. However, its widespread adoption faces risks and challenges, having detrimental impacts on project performance. This research aims to assess prefabricated construction risks in Iraq using fuzzy synthetic evaluation (FSE). After determining the mean importance score for the likelihood and impact of risks extracted from comprehensive theoretical reviews, significant risks were selected using normalization, followed by FSE. The theoretical review results yielded 79 risks across 11 categories. After normalization, 34 significant risks across 10 categories were identified. The results showed that all risk categories had a medium probability and impact, except for investment and political risks, while experience risks had a high probability and high impact, respectively. FSE results showed that the highest risk importance index was for experience (12.075), followed by political (11.753), capital investment (11.362), safety (11.242), and design risks (10.902). Through its detailed and integrated methodology, the study contributes to formulating an accurate roadmap for FSE of prefabricated construction risks and provides accurate results that add a deeper understanding of risks, helping project managers identify significant risks and formulate the necessary policies to mitigate and control them.

1. Introduction

Construction projects are considered among the largest within industrial sectors due to the invested currency and resources that are used [1]. Prefabricated construction is an efficient construction method in which prefabricated components can be manufactured in a controlled environment and quickly assembled on-site [2]. Compared with traditional construction methods in the engineering and construction industry, prefabricated construction has many benefits, including reduced project completion time [3,4], improved quality and strength [5,6,7], reduced material waste [8,9], higher seismic load resistance [6], enhanced sustainability and cleaner production [10,11,12], reduced environmental impacts [11], a safer working environment [13], workflow continuity [11], reduced carbon emissions [14,15], reduced number of contractors on-site, and improved worker safety [16,17].
Some or even all of the components of prefabricated buildings are formed in prefabricated factories and then transported to the construction site for lifting operations, which improves the clutter of traditional construction sites, but also increases the number of lifting operations [18]. Since most of the work carried out on-site is performed off-site, it can improve the shortcomings of the construction industry and take advantage of the benefits of the off-site construction industry [19].
Off-site construction (80–85% of the work) [20] can be classified into four types: sub-assembly of components, non-volumetric pre-assembly, volumetric pre-assembly, and modular construction [9,21]. The remaining tasks such as excavation, foundation laying, and module installation are performed on-site [10]. The extensive off-site work of modular construction leads to significantly reduced construction time and reduced risks, injuries, and fatalities compared to conventional construction [22].
Research has proven that it is an environmentally friendly and cleaner approach [23]. It is a sustainable construction method that leads to sustainable market growth [24]. There is also an opportunity to reduce safety incidents because it is not deeply affected by external environmental factors, such as rainfall and wind. Workers can become familiar with the work and workspace as a result of repetitive tasks in manufacturing plants [25]. Additionally, fewer workers are required with a reduced amount of on-site work [26]. A study found that the implementation of modular construction resulted in fewer work interruptions and less pollution and other noise-related features compared to conventional construction [27]. Ref. [28] showed that the increased use of modular construction resulted in a significant reduction in construction waste. Traditional construction methods primarily lead to various types of pollution which subsequently lead to negative consequences, one of which is climate change [29]. Therefore, adopting modular construction can solve the ever-growing issue of environmental sustainability [23].
Ref. [30] focused on the performance evaluation of modular buildings in Australia and highlighted that a 25% reduction in labor costs and a 40% reduction in construction time is achieved for modular construction compared to conventional construction. Modular construction also promises a pleasant working environment for construction workers [19].
Modular construction has recently received increasing attention due to its relative advantages and well-documented benefits compared to conventional construction, leading to its increased adoption in many countries [19]. Modular construction is widely accepted as an efficient construction method, especially in developed countries, for residential building construction [31]. Other less developed countries can also use it as a sustainable alternative to conventional construction [23].
Despite the advantages of prefabricated construction, some challenges hinder its widespread adoption [2]. The widespread adoption and implementation of modular construction is a complex process associated with multiple risks and uncertainties [23]. Developed countries have extensive experience in dealing with modular construction risks, while developing countries lack sufficient expertise in this field. The novelty of modular construction, insufficient skilled workforce, and lack of experience pose a potential risk to its implementation [32]. For less developed countries, modular construction is an innovative construction method with unique scale, steps, and interfaces [33]. These unique characteristics are associated with various complexities that lead to potential risks and thus pose new challenges for decision-makers [23].
Prefabricated construction risks have detrimental effects on project performance, quality, productivity, schedules and safety. Therefore, assessing its potential risks can mitigate their negative impacts on construction projects [31].
The application of general construction risk management to modular construction is limited because the activities involved are different from those in general construction [19]. Modular construction is a custom-made product, and therefore any shortages cannot be compensated by other manufacturers [34]. In addition, transporting modular components to the site requires special transportation vehicles and cranes for assembly. Restrictions on the size and weight of modular components impose constraints on the delivery of components to the construction site [35]. From an economic perspective, uncertainty in demand and the high initial capital cost of implementing modular construction lead to the risk of delays in achieving the return on initial investment [36].

1.1. Literature Review

Ref. [37] modeled the main risks and identified poor logistics, delays resulting from design changes, inefficient scheduling, contractual risks, and limited labor as the main risks affecting the time and cost of modular construction. Ref. [38] identified construction costs, limited storage capacity, and end-user preferences as the main risk factors for implementing modular construction. Ref. [39] assessed the significance of potential design risk factors by reviewing international empirical experts from 18 countries. The analysis results showed that the five most significant design risk factors include design inadequacy; late involvement of suppliers, manufacturers, and contractors; inaccurate information, defective design, and change orders; design information gap between the designer and manufacturer; and a lack of dedicated design codes and guidelines. The Fuzzy Analytical Hierarchy Process (AHP) and simulation techniques were used to assess and rank risks by a focused group of experts from the modular construction industry [31]. Dynamic risk models were created in China, combining safety risks collected from the theoretical literature and through a questionnaire. The model was combined with structural equation modeling (SEM). The results showed that pre-, during-, and post-construction processes significantly impact quality risks [20]. Ref. [40] used an improved Gray-TOPSIS (GRA-TOPSIS) comprehensive risk assessment model. Ref. [41] developed an improved IM-Fuzzy Cognitive Map (IM-FCM) model to assess the safety risks of prefabricated construction. Ref. [42] identified 25 major safety risks in prefabricated construction and their technical safety specifications across the stages of production, entry, transportation, storage, lifting, and installation of prefabricated components. Data were collected through surveys, and the main risk factors were examined using a structural equation model (SEM). Ref. [43] created a safety risk assessment model for prefabricated building lifting based on the structural entropy weight credibility measurement theory. Ref. [19] evaluated the incidence of safety-related accidents in modular construction that occurred in the United States from 2000 to 2018 and analyzed the risk factors by developing a causal map and conducting a comparative analysis of the types and causes of accidents occurring in conventional and modular construction. Ref. [23] proposed a framework to identify and assess the risks affecting modular construction implementation in Pakistan using Fuzzy Delphi. Critical risk factors were prioritized using the new Full Consistency Computation (FUCOM) method. The results showed that the three most important risks in modular construction implementation in Pakistan were “insufficient experience and skills in modular construction”, “insufficient capacity of modular construction manufacturers”, and “insufficient capacity to make design changes during the construction phase”.

1.2. Research Gaps

Although numerous studies have addressed risks in prefabricated construction, there is still a lack of comprehensive assessment frameworks that comprehensively examine a wide range of risks beyond design and safety aspects. Furthermore, most existing research focuses on developed countries, with limited attention paid to developing countries such as Iraq, where prefabricated construction is still in its infancy. Iraq faces challenges in construction projects, such as persistent cost overruns, delays, and declining quality [1]. Furthermore, due to the unique nature of prefabricated construction, risk management approaches used in traditional construction are not always applicable. Therefore, there is a need for context-specific, integrated, and data-driven approaches that can monitor and analyze prefabricated construction risks in developing countries.

1.3. Study Objectives

This study aims to fill the identified gaps by pursuing the following objectives:
  • Conduct a comprehensive identification and classification of risks associated with prefabricated construction in developing countries, with a focus on Iraq as a case study.
  • Assess and classify the significance of these risks using an integrated and objective systematic approach based on fuzzy logic.
  • Develop a risk assessment framework adapted to the unique characteristics and challenges of modular construction in resource-constrained environments.
  • Support decision-makers and practitioners in designing appropriate risk mitigation and control strategies tailored to the prefabricated construction sector.

2. Materials and Methods

Figure 1 shows the research methodology as follows:

2.1. Comprehensive Review of Theoretical Literature

The review included the theoretical literature related to all types of prefabricated building risks.

2.2. Exploratory Survey

An exploratory survey was conducted to assess the probability score and impact score of each risk identified from the theoretical review. A five-point Likert scale was used to collect expert evaluations. The questionnaire was developed after an extensive review of the literature on prefabricated building risks.

Questionnaire Development

The questionnaire includes the following:
  • Personal information of respondents: Table 1 shows the personal information of respondents. The questionnaire was distributed in most Iraqi governorates and across various engineering disciplines related to prefabricated building. Due to time constraints, 36 responses were accepted. The sample represents the northern, central, and south regions of Iraq, so the sample can be considered representative of all regions of Iraq based on the similar limited use of prefabricated buildings due to risks.
The personal information of respondents includes the following:
  • Place of work: Most of the respondents (41.9%) are from the Salah al-Din Governorate, followed by 35.5% from the Nineveh Governorate, 9.7% from Kirkuk, and 3.2% from Dohuk. This means that most of the respondents are from the governorates of northern Iraq, where 6.5% are from Baghdad (central Iraq) and 3.2% are from Diwani (south).
  • Academic qualification: In total, 54.8% of respondents hold a bachelor’s degree, 22.6% hold a master’s degree, 19.4% hold a doctorate, and the lowest percentage holds a higher diploma (3.2%).
  • The roles of respondents in the prefabricated construction process: Supervising engineers, 29%, and site engineers, 25.8%, represent the highest percentages of respondents, followed by 12.9% for design engineers, 9.7% for both academics and contractors, respectively, and 6.5%, the lowest percentage, for both project managers and consultants.
  • Specialization: Most of the respondents, 83.9%, are civil, followed by 9.7% architectural, and 6.5% mechanical.
  • Years of experience: Most of the respondents, 41.9%, have 11 or more years of experience, 38.7% have 6–10 years, and 19.4% of the respondents have 1–5, the fewest years of experience.
  • Work sector: Most of the respondents, 80.6%, work in the government sector in infrastructure projects and service and on educational buildings, and 19.4% work in private sector projects.
2.
Evaluation of the probability score and impact score of each risk: The questionnaire included an assessment of all prefabricated building risk categories extracted from the theoretical review and grouped them into 11 risk categories.

2.3. Questionnaire Analysis

Questionnaire analysis using the SPSS program included the following:
  • Assess the internal consistency of the data by obtaining Cronbach’s alpha coefficient using SPSS version 26.
  • Calculate the mean importance scores for both the probability score and the impact score using Equation (1), as referenced in [44,45]:
MS = (∑SF)/(∑F)
where MS represents the mean score, S represents the weight of respondents’ answers according to the five-point Likert scale (1 is very low, 2 is low, 3 is medium, 4 is high, and 5 is very high), and F represents the number of respondents’ answers for each weight.

2.4. Calculate the Risk Assessment Index Initially

Find the initial risk assessment (RA) index using Equation (2) [45,46]:
RA = P × I
where P is the probability score, and I is the impact.
In this study, the term “Probability Score” refers to expert-assigned values on the Likert scale from 1 to 5, representing a qualitative judgment of risk likelihood, rather than mathematical probability.

2.5. Perform Normalization

Perform normalization of the probability, impact, and initial risk index scores using Equation (3) [47,48,49]. The goal of normalization is to extract the important risks according to normalization:
X i ( k ) = Y i ( k ) m i n Y i ( k ) m a x Y i ( k ) m i n Y i ( k )
where Xi(k) is the value of Yi(k) after normalization, and max Yi(k) and min Yi(k) are the smallest and largest values of Yi(k).

2.6. Perform the Fuzzy Synthetic Evaluation

Fuzzy synthetic evaluation (FSE), a branch of fuzzy set theory, has been widely developed and applied in various disciplines to measure multiple evaluations. It is an analytical tool that objectively addresses the subjective judgment inherent in human decision-making [50]. FSE can provide a synthetic evaluation of an object related to many criteria in a fuzzy decision-making environment [51]. It is worth noting that FSE has been adopted in many fields, such as environmental analysis, human resource management, and risk assessment (RA). FSE has been applied to analyze the risks of sustainable projects in Singapore [52]. FSE has been used to evaluate the resource management efficiency of contractors in subway projects in China [53]. According to previous studies, FSE has advantages in dealing with complex evaluations with multiple characteristics and levels [53,54]. Therefore, this method was chosen to assess the risks of prefabricated construction in Iraq. Fuzzy synthetic evaluation (FSE) has seen a significant increase in its adoption in multi-criteria decision-making (MCDM) contexts due to its ability to handle ambiguity and imprecise linguistic data. Recent studies demonstrate that this method offers several advantages over traditional techniques such as the Analytic Hierarchy Method (AHP) and DEMATEL, particularly in highly uncertain environments. One of the most notable features of FSE is its ability to address the subjectivity and uncertainty associated with individual evaluations. Unlike some other methodologies that rely heavily on pairwise comparisons or expert-based weighting, FSE can assign weights automatically or rely on objective and consistent scoring rules, thus reducing human bias [55,56]. FSE also effectively addresses ambiguity arising from human perception by using fuzzy logic, which incorporates qualitative values into the scoring process. This is reflected in its ability to convert inaccurate human responses into fuzzy quantitative scoring. This approach is more suitable for situations where accurate or standardized assessments from participants are difficult to obtain [55,56]. Furthermore, FSE enables the manipulation of correlations between different criteria within the scoring process by using fuzzy fitting coefficients that account for interrelationships between indicators [57,58]. This distinguishes it from traditional methods that typically require combining multiple methodologies to address these correlations, which can increase computational complexity. In contrast, FSE simplifies the evaluation process by eliminating complex hierarchical structures and relying on direct integration between criteria and assessment scores [55].
Based on the above, it can be argued that FSE represents a flexible and effective methodological framework for conducting multi-criteria evaluations in environments characterized by ambiguity and uncertainty, which explains its increasing reliance in applied research and contemporary professional practice.
Fuzzy synthetic evaluation (FSE) was performed to determine the importance levels of both the probability score and impact score for the significant risks identified in the previous step. The objective of this process is to derive the importance index for probability score I prob. and the importance index for impact score Iimp. using the FSE method, as detailed in the following steps [49,59]:
  • Establish weights for risks and risk categories using Equation (4) [49,59]:
W i = M i i = 1 n M i
where 0 ≤ Wi ≤ 1; ∑Wi = 1 (the sum of weights in one category); Wi is the weight of the risk index within the risk category; Mi is the weighted mean score of the indicator; and n is the number of indicators in one category; ∑Wi = 1.
2.
Membership function establishment: (evaluation matrix): Membership function matrices are established for risks and risk categories. The membership matrix is an assessment of the degree of representation (membership) of a risk within a fuzzy set and its values range from zero to 1 [49,59].
In this research, the membership function for risks and risk categories is calculated based on experts’ assessment of the probability score once and impact another time according to the five-point Likert scale, using Equation (5) [49,59], noting that the sign + in the equation does not mean addition.
M F R = X 1 r g 1 + X 2 r g 2 + X 3 r g 3 + X 4 r g 4 + X 5 r g 5
where MFR is the membership function for each risk 0 ≤ MFR ≤ 1; ∑ MFR = 1
Xr: percentage of each risk assessed by the respondent according to the scale used (five-point scale). (g1 = 1, g2 = 2, g3 = 3, g4 = 4, and g5 = 5.)
3.
Establishing the membership function for the risk categories: the membership function for the categories is established using Equation (6):
D = Wi × R
where D represents the membership function matrix for each risk category; Wi represents the risk weights under each category; and R represents the membership function matrix for each risk under each category.
4.
Creating the importance index for risk categories:
The importance index is calculated for each risk category (calculating the importance index for the probability score of the risk Iprob. and the importance index for the impact of the risk Iimp.) using Equation (7).
I = i = 1 5 D × E

2.7. Find the Final Risk Index

Find the final risk index by multiplying the probability index by the impact index.

3. Results and Discussion

3.1. The Results of the Theoretical Review

Table 2 represents the risks of prefabricated construction according to their categories and references, which includes capital investment, political, material, equipment, safety, spatial mismanagement, workplace and environment, design, supply chain, administrative, poor scheduling, and experience risks.

3.2. Results of Data Consistency

Cronbach’s alpha was used to measure the internal consistency of questionnaire items. It is the most common and widely used test in studies that aims to assess the homogeneity of items in measuring the same dimension or concept. Table 3 shows that the value of the Cronbach coefficient for prefabricated building risks is (0.905) using the SPSS v.26 program analysis, which means that the consistency and reliability of the data are excellent [92,93]. According to [94], the use of alpha is appropriate and sufficient if the scale is designed to measure one or more specific dimensions, without the need for additional reliability tests such as test–retest or inter-rater reliability, which are used in research contexts different from the nature of this study. Therefore, relying solely on Cronbach’s alpha serves the statistical and methodological purpose in this context [92].

3.3. Results of Mean Importance Scores for Probability, Impact, and Risk Index

Based on the analysis conducted using SPSS version 26, the mean importance scores for the probability score and the impact score of each risk within each category were calculated. Subsequently, the initial risk index mean score was derived using Equation (2). The levels of the mean importance scores for the probability, impact, and risk index were classified according to Table 4. Table 5 presents the detailed mean importance scores for the probability score, impact score, and initial risk index for each identified risk within its corresponding category.
  • Capital investment risks: The mean probability score of all investment risks is medium except for the risk of economic conditions, which has a high mean probability score. The mean impact score of all risks is medium, and the mean risk index score of all investment risks is low except for the risk of economic conditions, which is medium.
  • Political risks: The mean probability score for political risks is generally medium except for P4 (poor government support and regulations), which has a high score. Similarly, the mean impact score is medium overall, except for P4 (poor government support and regulations) and P5 (unsupportive planning and building regulations), which have high impact scores. Therefore, the mean risk index is medium, except for P1 (changing government policies), P2 (changing political support due to change in the political environment), and P3 (labor strike), which have low risk index scores.
  • Material and equipment risk: The mean probability score and mean impact score of material and equipment risk are generally medium. However, specific risks such as M6 (crane failure), M9 (technological inefficiency), and M11 (improper use of equipment) show deviations from this trend. Consequently, the mean risk index is low for most of these risks, except for M6, M7 (part obsolescence as a result of long-term operation of equipment), M8 (materials/components, defective structure and crane, and defective equipment), M9, and M10 (improper use of equipment), which have medium risk index scores.
  • Safety Risk: The mean probability score for safety risks is generally medium, except for S8 (no occupational training and safety for workers), S13 (no regular security checks and fixes), and S14 (safety measures not applied), which have high mean probability scores. The mean impact score for safety risks is also medium overall, except for S4 (no hazard indicator for the equipment), S6 (no security check at the time of admission), S11 (not wearing personal protective equipment), S12 (defective personal protective equipment), S13, and S14, which are rated with high impact scores. Consequently, the mean risk index for most safety risks is medium, except for S1 (poor fall prevention), S3 (safety device for crane when not in operation), S5 (overloading lifting), and S7 (low security awareness), which have low risk index scores.
  • Risks of spatial mismanagement: The mean probability score and mean impact score of spatial mismanagement risks are generally medium, except for the high mean impact of T5 (crowded work areas). Therefore, the mean risk index is low, except for T5.
  • Workplace and environment risks: The mean probability score and mean impact score of workplace and environment risks are medium. Therefore, the mean risk index is low except for E2 (lighting and bad ventilation), which has a medium index score.
  • Design Risks: The mean probability score and mean impact score of design risks are generally medium, except for D3 (the inability to make changes in the design), which has a high mean probability score. Therefore, the mean risk index is medium, except for D1 (lack of appropriate codes and design standards for prefabricated construction), D2 (complicated design of prefabricated buildings), which have low risk index scores.
  • Supply chain risks: The mean probability score and mean impact score of supply chain risks are medium, so the mean risk index is low, except for U2 (complex supply chain), U4 (delays in delivering modular components to the site), and U5 (supply chain information gap and inconsistency), which have low risk index scores.
  • Administrative risks: The mean probability score of administrative risks is medium except for R7 (deficiency of professional managers on site), which has a high mean probability score. The mean impact score of administrative risks is also medium, except for R2 (poor cooperation and communication between project participants), R3 (stakeholder fragmentation and management complexity), R4 (lack of best management practices), and R7, which have high impact scores. Therefore, the mean risk index for all administrative risks is medium.
  • Poor scheduling risks: The mean probability score and mean impact score of poor scheduling risks are medium, so the initial mean risk index is low.
  • Experience risk: The mean probability score for experience risk is medium, except for K2 (contractors’ lack of experience in prefabricated construction), which has a high mean probability score. The mean impact score for experience risk is high, except for K1 (insufficient skills and experience in prefabricated construction), which has a medium impact score. Therefore, the mean risk index for experience risk is medium.
  • Summary of mean scores for probability, impact, and risk index:
Table 6 presents a summary of the mean probability, impact, and initial risk index scores for each risk category. It also highlights individual risks whose scores deviate significantly from their respective category means.

3.4. Result of Risk Filtering

Risks were filtered according to their importance based on normalization, where risks with a normalization value greater than or equal to 0.5 were taken into consideration [95,96]. Then, fuzzy synthetic evaluation analyses were conducted on them, and the risks with a normalization of less than 0.5 were considered to be of little importance and were not included in the rest of the analysis. Table 7 shows the normalization results. According to the mean probability score of risks, the filtering included selecting 41 risks, while the normalization of risks according to the mean impact score included filtering the selection of 55 risks, and normalization according to the initial risk index score included filtering 34 risks. Since most of the risks had a medium importance level according to the Likert scale, the important risks were selected according to the risk index to include thirty-four risks (two political risks, three experience risks, seven administrative risks, ten safety risks, six material and equipment risks, three supply chain risks, one design risk, one investment risk, and one spatial mismanagement risk). In order for each category to have at least two risks, the highest risk was added to each of the categories of investment risks, design risks, and spatial mismanagement risks, and the highest two risks were added to the risks of the workplace and environment, so that the number of important risks that were selected became thirty-nine risks. The risks of poor scheduling were neglected because their risk index was low.

3.5. Results of Fuzzy Synthetic Evaluation

Results of establishing weights for significant risk and risk categories. Table 8 presents the calculation of appropriate weights for the probability scores and impact scores of each risk and each risk category.

3.5.1. Membership Function Calculation

The membership function for risks was calculated before calculating the membership function for categories based on the respondents’ assessment of the importance of each risk according to the five-point Likert scale. For example, for risk I6, from the questionnaire results, it was shown that 3.2% of respondents rated the probability score of risk I6 (high cost price) as very high, 22.6% rated the weights for the probability scores of its occurrence as high, 51.6% rated the weights for the probability scores of its occurrence as medium, and 22.6% rated the weights for the probability scores of its occurrence as low. The membership function for I6 can be calculated using Equation (3):
M F I 6 = 0.00 1 + 0.226 2 + 0.516 3 + 0.226 4 +   0.032 5
The membership function for I6 can be written as (0.00, 0.226, 0.516, 0.226, and 0.032) [49], in the same way the membership function for the residual risk is calculated, as shown in Table 9.

3.5.2. Membership Function (Evaluation Matrix) for Risks Categories

After finding the membership function for the risks under each category, the membership function for the risk category is found using Equation (6). To find the evaluation matrix for the probability score of the capital investment risk category (I), the fuzzy evaluation matrix R for capital investment risks is as follows:
R = 0.000 0.226 0.516 0.226 0.032 0.000 0.097 0.452 0.355 0.097
The corresponding weight vector W for the two risk factors the following:
W = 0.529         0.470
The fuzzy synthetic evaluation vector DI is calculated as follows:
D I = W .   R = 0.529 × 0.000 + 0.471 × 0.000   , 0.529 × 0.226 + 0.471 × 0.097 ,   0.529 × 0.516 + 0.471 × 0.452 ,   0.529 × 0.226 + 0.471 × 0.355 ,   0.529 × 0.032 + 0.471 × 0.097 ,
This results in
D I = 0.000 ,   0.165 ,   0.486 ,   0.286 ,   0.063 .
Table 9 shows the details of the membership matrix calculations for the probability score of the occurrence of risks and risk categories, and Table 10 shows the results of the membership matrix for the impact of the occurrence of risks and risk categories.

3.5.3. Importance Index for Risk Categories

The risk probability score index Iprob. and risk impact index were calculated for each category as follows:
  • Importance index of the probability score and impact score Iimp.:
Table 10 presents a detailed calculation of the importance index of each risk category for the probability score Iprob. and the impact score Iimp. using Equation (7). To calculate the importance index for the probability score of capital investment risks, Equation (7) is applied:
I = 0.000 0.165 0.486 0.286 0.063   ×   1 2 3 4 5 = 3.246 I = ( 0.0   0.165   0.486   0.286   0.063 )   ×   ( 1   2   3   4   5 )   =   3.246
2.
Risk Importance Index: Table 11 shows the risk importance index according to the categories, based on the multiplication of the importance probability score index and the importance impact score index. The importance probability score index and importance impact score are medium for all risks, and the importance index is medium for all risks except for material and equipment risks, administrative risks, and workplace and environment risks. It is clear that the highest risk probability score index is for political risks, followed by safety risks and experience risks, respectively. The highest impact score index is for experience risks, followed by political risks and capital investment risks. Therefore, the highest risk index is for experience risks, followed by political risks and capital risks. Material and equipment risks, administrative risks, and workplace and environment risks share the lowest ranking for each of the probability, impact, and risk index scores, respectively.

4. Discussion

This study gives a detailed overview of the risk environment surrounding prefabricated construction in Iraq—a country where this method remains underutilized despite its globally recognized advantages. The findings illustrate the complex and multifaceted nature of risks that hinder the widespread adoption of this construction approach.

4.1. Interpreting the Findings in the Iraqi Context

The results showed that all risk categories have medium importance using FSE, with experience risks leading the way (12.075), followed by political risks (11.753), capital investment risks (11.362), safety risks (11.242), and design risks (10.902).
The results showed that the highest important risks (probability score, impact, and risk index) are P4 (poor government support and regulations), K2 (contractors’ lack of experience in prefabricated construction), R7 (deficiency of professional managers on site), S11 (not wearing personal protective equipment), S14 (safety measures not applied), and I7 (increase in cost price). Experience-related risks (12.075), particularly contractors’ lack of experience in prefabricated construction, emerged as the most significant. This outcome aligns with broader limitations in Iraq’s vocational education and training systems, where hands-on instruction in modern construction practices remains inadequate. Political risks (11.753)—driven by poor government support and regulations —reflect broader institutional fragility and a lack of strategic policymaking, which commonly characterizes Iraq’s public infrastructure development. Capital investment and safety-related risks also ranked highly, emphasizing the difficulty of obtaining financing for technologically advanced solutions like prefabrication, increases in cost price, and workers not wearing personal protective equipment, as well as S14 (safety measures not applied). Additionally, design-related risks—the inability to make changes in the design (stopping the design and wrong specifications) during the construction phase—were identified as impediments to effective implementation.
The results of the current study showed that material and machinery risks (7.84), administrative risks (9.429), and workplace and environment risks (9.913) are of little significance, given that prefabricated construction in Iraq is still in its early stages. This finding likely reflects the relatively early stage of prefabricated construction in Iraq, where widespread adoption has not yet revealed deeper logistical and managerial challenges often seen in more developed markets.

4.2. Practical Recommendations for Managing the Most Serious Risks

In light of the most critical risks identified, the following practical strategies are recommended:
  • Enhancing expertise and training: Establish specialized training initiatives targeting contractors, engineers, and construction workers to enhance competencies in prefabrication, collaborate with academic institutions and technical colleges to integrate prefabrication methodologies into engineering curricula, and promote knowledge-sharing platforms to facilitate the dissemination of best practices among industry professionals.
  • Strengthening the governmental role: Develop a comprehensive and supportive policy framework that includes updated building codes, simplified approval processes, and dedicated public investment mechanisms to encourage prefabrication adoption.
  • Financial facilitation: Establish public–private partnerships offering low-interest loans and tax incentives to reduce the financial burden of adopting prefabricated technologies.
  • Safety regulation enforcement: Enforce stringent occupational safety protocols, conduct routine inspections, and provide incentives to firms that demonstrate high compliance with PPE usage and safety training.
  • Improving supply chain management: Encourage local manufacturing of prefabricated components to reduce reliance on imported materials, integrate digital supply chain tracking systems to improve logistics and mitigate delivery disruptions, and establish strategic partnerships with logistics service providers to enhance transportation efficiency and reduce supply chain vulnerabilities.
  • Promoting knowledge transfer: Create a centralized digital platform to disseminate best practices, case studies, and innovative applications of prefabricated construction from both local and international contexts.

4.3. Comparison with Previous Research in Similar Contexts

The results are broadly consistent with findings from other developing countries facing similar socioeconomic conditions. The results of the study are consistent with [23,32], which indicated that the lack of professional expertise and specialization is a major obstacle to adopting prefabricated construction in developing nations like Nepal and Pakistan. They also roughly agreed with [36] in that the most significant risk is the delay in achieving the return on initial investment. The results of this study differ from those of [37], which emphasized the weaknesses of logistics services, delays caused by design changes, and inadequate scheduling. However, both studies agree on the limited number of experienced workers as a significant risk. Additionally, the findings differ significantly from [38], where the most critical risks identified were construction costs, storage capacity, and end-user preferences in developed countries (such as England). In Iraq, these risks appear less prominent, likely due to the smaller scale of ongoing modular construction efforts. Overall, while the exact prioritization of risks may differ by region, a consistent trend emerges in developing countries: challenges related to workforce skills, policy development, and capital access remain prevalent. This underlines the necessity for localized risk management frameworks and opportunities for cross-national knowledge exchange.

5. Conclusions

The main conclusions, based on the discussion and analysis conducted during this study, are as follows:
  • The main risks to prefabricated construction in Iraq: The research identified a list of major risks hindering prefabricated construction in Iraq, most notably the lack of experienced contractors, weak government support systems, and financial risks associated with initial investment. This demonstrates the failure of developing countries to adopt advanced construction technologies.
  • The importance of increasing expertise and training human resources: The skills and experience gap in prefabricated construction is the most significant factor hindering the use of this technology in Iraq.
  • Strengthening government support and regulatory frameworks: The Iraqi government plays a crucial role in supporting the adoption of prefabricated construction by establishing clear and supportive regulatory policies. Incentives such as tax exemptions and low-interest loans can encourage investment in this sector. In addition, maintaining a stable political environment will further promote the sustainability and growth of this construction method.
  • Enhancing occupational health and safety standards: Occupational safety regulations at prefabricated construction sites should be strengthened, particularly with regard to the mandatory use of protective equipment and strict adherence to preventive safety protocols. Improving safety practices will enhance both worker and investor confidence, while also minimizing accidents during the implementation phase.
  • Improving financial risk management: To avoid concerns about initial investment expenditures, the government could prioritize the formulation of flexible and supportive financing arrangements for projects involving prefabricated construction, in the form of subsidized loan programs or grants that help companies overcome financial obstacles in the early implementation phase.
  • Supply chain innovation: Although risks in materials and equipment are of minor importance in this research, it is also necessary to strengthen the supply chain for prefabricated components by promoting local production and digital tools to monitor materials and streamline delivery processes.
  • Investing in modernizing production plants: Companies should invest in upgrading production plants and adopting automation and robotics technologies to enhance manufacturing accuracy and the quality of prefabricated components. Building Information Modeling (BIM) technology should also be used to enhance stakeholder coordination and project risk management. Based on these findings, it is essential to emphasize the need for Iraq to implement comprehensive approaches to facilitate and encourage prefabricated construction in infrastructure projects. Coordination between the private and public sectors, as well as the design of encouraging policies, will ensure this required technological change.

5.1. Limitations of the Study

Although this study provided comprehensive insights into the risks of prefabricated construction in Iraq, there are some limitations that should be taken into consideration:
  • Limited data: Data were collected from a specific group of participants in the Iraqi construction sector, which may affect the generalizability of the results to all sectors or other regions in the country.
  • Changing economic and political environment: Since the study relied on current data, the results may be subject to change over time due to political and economic shifts that may affect the sector. Future developments may lead to changes in risk assessment.

5.2. Suggestion for Future Research

There are several suggestions for future research in this area:
  • Studying economic and environmental impacts: Studying the impact of prefabricated construction technologies on energy consumption and carbon emissions could provide important insights that support the shift towards greater sustainability in the construction sector.
  • Evaluating the impact of government policies: Studies should be conducted on the impact of various government policies on the adoption of prefabricated construction in Iraq. Examining the experiences of other countries can provide valuable lessons for adapting local policies to the Iraqi context.
  • Analysis of consumer behavior and end-user preferences: Studying consumer and end-user attitudes toward prefabricated construction projects in Iraq and understanding how these preferences affect the success of these projects can help improve marketing and design strategies.
  • Using information technology to improve supply chains: Research on how modern technologies such as artificial intelligence and the Internet of Things (IoT) can be used to improve supply chain operations in prefabricated construction projects can be developed. This technology can contribute to reducing costs and improving efficiency.
  • Evaluating the effectiveness of vocational training programs in capacity building: In-depth study should be conducted on the effectiveness of vocational training programs for prefabricated construction in Iraq to determine whether these programs actually contribute to improving skills and reduce the risks associated with a lack of experience.

Author Contributions

Conceptualization, M.A.M.; Methodology, M.A.M.; Software, M.A.M.; Validation, S.S.F.; Formal analysis, M.A.M.; Investigation, M.A.M.; Resources, M.A.M.; Data curation, S.S.F.; Writing—review & editing, S.S.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BIMBuilding Information Modeling
FSEFuzzy synthetic evaluation

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Figure 1. The research methodology.
Figure 1. The research methodology.
Buildings 15 01622 g001
Table 1. The personal information of the respondents.
Table 1. The personal information of the respondents.
Work Place%Academic Degree%Respondent Role%Specialization
%
Years of Experience%Work Sector
%
Salah al-Din2Bachelor’s54.8Project Manager6.5Civil General83.911 or more41.9Governmental80.6
Kirkuk9.7Master’s22.6Supervising Engineer29.0Architectural9.76–1038.7Private19.4
Baghdad6.5Higher Diploma3.2Site Engineer25.8Mechanical6.51–519.4
Nineveh35.5Doctor19.4Contractor9.7
Dohuk3.2 Design Engineer12.9
Diwaniyah3.2 Academic9.7
Consultant6.5
Table 2. The risks of prefabricated construction according to their categories and references.
Table 2. The risks of prefabricated construction according to their categories and references.
CodeCapital Investment Risks IReferences
I1Cost of construction and productivity[31]
I2Increase in the prices of prefabricated components[60]
I3The cost estimate is inaccurate[60]
I4High initial investment (high cost of initial capital)[60,61,62,63,64]
I5Limited capacity of prefabricated manufacturers/suppliers[60]
I6Increase in the prices of prefabricated components[50]
I7Increase in cost price[65]
I8Volatile economic conditions[12,66,67]
I9Difficulty in achieving a return on initial investment (longer break-even period)[31]
I10Market demand for standard homes and general consumer habits[60]
I11Failure of the ready production system[12,22,68]
CodePolitical risks PReferences
P1Changing government policies[31]
P2Changing political support due to change in the political environment[31]
P3Labor strike[31]
P4Poor government support and regulations[33,34,60,69,70]
P5Unsupportive planning and building regulations[60]
CodeMaterial and equipment risks MReferences
M1Improper use of a crane[19]
M2Quality and type of manufactured materials[31]
M3The state of the factory production line for prefabricated materials[31]
M4Construction equipment case[31]
M5The condition of heavy equipment in terms of lifting and installation[31]
M6Crane failure[65]
M7Part obsolescence as a result of long-term operation of equipment to[18]
M8Materials/components, defective structure and crane, and defective equipment[19]
M9Technological inefficiency[12,28]
M10Improper use of equipment[19]
CodeSafety risk SReferences
S1Poor fall prevention (no fall risk indicator)[19]
S2Incomplete equipment safety device [19]
S3Safety device for crane when not in operation[19]
S4No hazard indicator for the equipment[19]
S5Overloading lifting[18]
S6No security check at the time of admission[18]
S7Low security awareness[18]
S8No occupational training and safety for workers[18]
S9There is no safety device[19]
S10No indication sign of risk[19]
S11Not wearing personal protective equipment[19]
S12Defective personal protective equipment[19]
S13No regular security checks and fixes[18]
S14Safety measures not applied[18]
CodeThe risks of spatial mismanagement TReferences
T1Insufficient consideration of the moving line (insufficient moving space)[68]
T2Insufficient consideration of the radius of the crane[68]
T3Ignore the power line[68]
T4Improper arrangement of the workplace[68]
T5Crowded work areas (insufficient work space to install the unit)[68]
T6Unstable or slippery floor[68]
T7Unstable working areas/platforms (unstable support, unstable crane, unstable ladder, and unstable unit structure)[68]
CodeWorkplace and environment risks EReferences
E1Unexpected weather and weather disturbances (temperature, wind speed, etc.)[31,60,65]
E2Lighting and bad ventilation[19]
E3Excessive noise[19]
E4Exposure to fumes, noise, and toxic compounds in production units[65]
E5The state of the land and the environment of the place[31]
E6Ignore the influence of the environment on the job[18]
CodeDesign risks DReferences
D1Lack of appropriate codes and design standards for prefabricated construction[12,60,66,71]
D2Complicated design of prefabricated buildings[60,63,72,73]
D3The inability to make changes in the design (stopping the design and wrong specifications) during the construction phase[12,22,27,74,75]
D4Change orders due to defective design and change in project scope[19,31,60,76]
CodeSupply Chain Risks SC
SC1Stakeholder management risks and supply chain management limitations[60]
SC2Complex supply chain[60,77,78,79,80]
SC3Poor supply chain integration[27,60,81,82]
SC4Delays in delivering modular components to the site[5,60,71,81,83]
SC5Supply chain information gap and inconsistency[60]
SC6Transport restrictions (size and weight)[60,63,78,84,85]
SC7Damage to prefabricated components during transportation to construction sites and installation[12,28,86]
SC8Supply chain disruptions[65]
CodeAdministrative risks RReferences
R1Inadequate information coordination between project participants (poor coordination between multiple interfaces)[64,65,87,88]
R2Poor cooperation and communication between project participants[60]
R3Stakeholder fragmentation and management complexity[65]
R4Lack of best management practices[60]
R5Managers have not seriously fulfilled management responsibilities[18]
R6There is no special lift plan[18]
R7Deficiency of professional managers on site[18]
R8Lack of quality control systems[12,89]
CodePoor scheduling risk CReferences
C1Delays in delivery of modular components to the construction site[60,65]
C2Ineffective or inappropriate scheduling[60,79,83,87]
C3Workers’ unreasonable scheduling leads to worker stress[18]
CodeExperience risks KReferences
K1Insufficient skills and experience in prefabricated construction[31,61,65,90,91]
K2Contractors’ lack of experience in prefabricated construction[60]
K3Skilled labor requirements[60]
Table 3. Cronbach coefficient.
Table 3. Cronbach coefficient.
Cronbach’s AlphaN of Items
0.90579
Table 4. The levels of mean importance scores for probability, impact, and risk index.
Table 4. The levels of mean importance scores for probability, impact, and risk index.
The Mean Importance Scores for the Probability and the ImpactThe Mean Importance for Risk Index
The Range of ValuesLevelThe Range of ValuesLevel
1–1.8Very Low (V.L)1–5Very Low (V.L)
1.81–2.6Low (L)5.1–10Low (L)
2.61–3.4Medium (M)10.1–15Medium (M)
3.41–4.2High (H)15.1–20High (H)
4.21–5Very High (V.H)20.1–25Very High (V.H)
Table 5. The mean importance scores for probability, impact, and risk index for each risk within each category.
Table 5. The mean importance scores for probability, impact, and risk index for each risk within each category.
CodeProbabilityLevelRankImpactLevelRankRILevelRank
Capital Investment Risks I
I12.94M82.68M117.86L11
I22.94M92.97M88.71L9
I33.00M73.20M39.60L6
I43.26M22.97M109.66L5
I53.03M63.10M69.39L7
I63.06M43.26M29.98L2
I73.45H13.39M111.69M1
I83.19M33.10M79.89L3
I92.68M112.97M97.95L10
I102.81M103.16M58.87L8
I113.06M53.19M49.79L4
3.04M 3.09M 9.40L
Political risks P
P12.97M43.06M59.09L5
P23.10M33.23M49.99L3
P32.94M53.32M39.75L4
P43.55H13.55H112.59M1
P53.16M23.45H210.91M2
P3.14M 3.32M 10.47M
Material and equipment risks M
M13.06M53.32M510.18M6
M22.90M73.13M89.08L8
M32.87M103.19M79.17L7
M42.90M82.94M98.52L9
M52.90M92.84M108.24L10
M63.10M23.45H310.69M3
M73.16M13.26M610.30M5
M83.10M33.39M410.49M4
M93.10M43.48H210.79M2
M103.06M63.55H110.87M1
M3.02M 3.25M 9.83L
Safety risks S
S13.10M123.16M139.79L13
S23.19M93.23M1110.30M10
S32.90M143.03M148.80L14
S43.23M73.45H311.13M6
S53.13M113.23M1210.09L12
S63.16M103.45H410.91M8
S73.03M133.35M710.17L11
S83.55H13.32M1011.79M3
S93.23M83.35M810.82M9
S103.26M103.35M910.93M7
S113.45M23.58H112.36M1
S123.35M53.42H511.47M5
S133.42H33.42H611.69M4
S143.42H43.55H212.13M2
S3.24M 3.35M 10.89M
The risks of spatial mismanagement T
T13.00M53.00M79.00L 6
T22.77M73.16M58.77L 7
T32.87M63.16M69.08L 5
T43.13M23.19M49.99L 3
T53.26M13.52H111.46M1
T63.06M43.23M39.89L 4
T73.10M33.26M210.09L 2
T3.03M 3.22M 9.75L
Workplace and environment risks E
E13.10M33.10M29.59L2
E23.16M23.23M110.20M1
E33.10M43.03M39.39L4
E42.97M62.97M48.81L5
E53.03M52.90M68.80L6
E63.19M12.97M59.48L3
E3.09M 3.03M 9.38L
Design risks D
D13.00M43.26M29.77L3
D23.10M33.03M49.39L4
D33.42H13.39M111.58M1
D43.13M23.26M310.19M2
D3.16M 3.23M 10.24M
Supply Chain Risks SC
SC13.13M32.87M78.98L7
SC23.19M13.29M310.51M3
SC33.10M63.13M59.69L4
SC43.13M43.39M110.60M2
SC53.16M23.39M210.71M1
SC63.06M72.87M88.80L8
SC72.90M83.16M49.18L6
SC83.13M53.00M69.39L5
SC3.10M 3.14M 9.73L
Administrative risks R
R13.23M63.26M710.51M7
R23.39M23.45H311.69M2
R33.29M43.52H211.57M3
R43.35M33.42H411.47M4
R53.19M73.35M510.71M6
R63.13M83.26M810.19M8
R73.48H13.55H112.36M1
R83.29M53.32M610.93M5
RK3.29M 3.39M 11.18M
Poor scheduling risks C
C13.06M22.97M29.09L2
C23.13M13.03M19.49L1
C32.94M32.94M38.62L3
C3.04M 2.98M 9.07L
Experience risks K
K13.35M23.35M311.25M3
K23.42H13.65H112.46M1
K33.19M33.58H211.43M2
3.32 M3.53H 11.72M
Table 6. Summary of mean scores for probability, impact, and initial risk index.
Table 6. Summary of mean scores for probability, impact, and initial risk index.
Risk CategoriesMean Probability ScoreMean Impact ScoreInitial Mean Risk Index
Capital investmentMLL
Increase in cost price (I7)HIncrease in cost price (I7)MIncrease in cost price (I7)M
PoliticalMMM
Poor government support and regulations (P4)HPoor government support and regulations (P4)HPoor government support and regulations (P4)M
Unsupportive planning and building regulations (P5)HUnsupportive planning and building regulations (P5)M
Material and equipmentMML
Improper use of equipment (M10)HImproper use of equipment (M10)M
Technological inefficiency (M9)M
Technological inefficiency (M9)HCrane failure (M6)M
Part obsolescence as a result of long-term operation of equipment to (M7)M
Crane failure (M6)HMaterials/components, defective structure and crane, and defective equipment (M8)M
SafetyMMM
No occupational training and safety
for workers (S8)
HNot wearing personal
protective equipment (S11)
HPoor fall prevention (S1)L
Safety measures not applied (S14)H
Safety measures not applied (S14)H
No hazard indicator for the equipment (S4)HSafety device for crane when not in operation (S3)L
No security check at the time of admission (6)HOverloading lifting (S5)L
Defective personal protective equipment (S12)HLow security awareness (S7)L
No regular security checks and fixes (S13)H
Spatial
mismanagement
MMM
Crowded work areas (T5)HCrowded work areas (T5)M
Workplace and
environment
MMM
Lighting and bad ventilation (E2)M
DesignMMM
The inability to make changes in the design (D3)H Lack of appropriate codes and design standards for MiC prefabricated construction (D1)L
The inability to make changes in the design (D2)L
Supply ChainMML
Supply chain information gap and M
inconsistency (SC5)
Delays in delivering modular components to the site (SC4)M
Complex supply chain (SC2)M
AdministrativeMMM
Deficiency of professional managers on site (R7)HDeficiency of professional managers on site (R7)H
Stakeholder fragmentation and management complexity (R3)H
Poor cooperation and communication between project participants (R2)H
Lack of best management practices (R4)H
Poor scheduling
Experience
MHM
Contractors’ lack
of experience in prefabricated construction (K2)
HInsufficient skills and experience in prefabricated construction (K1)M
Table 7. The normalization N.
Table 7. The normalization N.
Normalization N Based on Probability ScoreNormalization N Based on Impact Score Normalization N Based on Risk Index Score
CodeProbability ScoreRankNCodeImpact ScoreRankNCodeRIRankN
P43.5511K23.6511P412.5911
S83.5521S113.5820.93K212.4620.97
R73.4830.92K33.5830.93R712.3630.95
I73.4540.89P43.5540.9S1112.3640.95
S113.4550.89M103.5550.9S1412.1350.9
S133.4260.85S143.5560.9S811.7960.83
S143.4270.85R73.5570.9S1311.6970.81
D33.4280.85T53.5280.86I711.6980.81
K23.4290.85R33.5290.86R211.6990.81
R23.39100.81M93.48100.83D311.58100.79
S123.35110.78P53.45110.8R311.57110.78
R43.35120.78M63.45120.8S1211.47120.76
K13.35130.78S43.45130.8R411.47130.76
R33.29140.7S63.45140.8T511.46140.76
R83.29150.7R23.45150.8K311.43150.76
I43.26160.66S123.42160.76K111.25160.72
S103.26170.66S133.42170.76S411.13170.69
T53.26180.66R43.42180.76R810.93180.65
S43.23190.63I73.39190.73S1010.93190.65
S93.23200.63M83.39200.73P510.91200.65
R13.23210.63D33.39220.73S610.91210.65
I83.19220.59U43.39230.73M1010.87220.64
S23.19230.59U53.39240.73S910.82230.63
E63.19240.59S73.35250.7M910.79240.62
U23.19250.59S93.35260.7R510.71250.6
R53.19260.59S103.35270.7U510.71270.6
K33.19270.59R53.35280.7M610.69280.6
P53.16280.55K13.35290.7U410.6290.58
M73.16290.55P33.32300.66R110.51300.56
S63.16300.55M13.32310.66U210.51310.56
E23.16310.55S83.32320.66M810.49320.56
SC53.16320.55R83.32330.66S210.3330.52
S53.13330.52U23.29340.63M710.3340.52
T43.13340.52I63.26350.6E210.2350.49
D43.13350.52M73.26360.6D410.19360.49
SC13.13360.52T73.26370.6R610.19370.49
SC43.13370.52D13.26380.6M110.18380.49
SC83.13380.52D43.26390.6S710.17390.49
R63.13390.52R13.26400.6S510.09400.47
C23.13400.52R63.26410.6T710.09410.47
P23.1420.48P23.23420.56T49.99420.45
M63.1430.48S23.23430.56P29.99430.45
M83.1440.48S53.23440.56I69.98440.45
M93.1450.48T63.23450.56I89.89450.43
S13.1460.48E23.23460.56T69.89460.43
T73.1470.48I33.2470.54S19.79470.41
E13.1480.48I113.19480.53I119.79480.41
E33.1490.48M33.19490.53D19.77490.4
D23.1500.48T43.19500.53P39.75500.4
SC33.1510.48I103.16510.5U39.69510.39
I63.06520.44S13.16520.5I49.66520.38
I113.06530.44T23.16530.5I39.6530.37
M13.06540.44T33.16540.5E19.59540.37
M103.06550.44U73.16550.5C29.49550.34
Table 8. The calculation of appropriate weights.
Table 8. The calculation of appropriate weights.
Weights for the Probability ScoresWeights for the Impact Scores
Risk and Risk CategoryMean for Risk∑MeanWi for RiskMean for Risk CategoryWi for Risk CategoryMean for
Risk
∑MeanWi for
Risk
Mean for Risk CategoryWi for Risk Category
Political risks
Pf43.55 0.529 3.55 0.507
Pf53.16 0.471 3.45 0.493
6.71 3.3550.1035 7 3.50.1035
Experience Risks
Kf23.42 0.343 3.65 0.345
Kf33.19 0.320 3.58 0.338
Kf13.35 0.336 3.35 0.317
9.97 3.3230.1025 10.58 3.5270.1043
Administrative risks
Rf73.48 0.150 3.55 0.149
Rf23.39 0.146 3.45 0.145
Rf33.29 0.142 3.52 0.147
Rf43.35 0.144 3.42 0.143
Rf83.29 0.142 3.32 0.139
Rf53.19 0.137 3.35 0.141
Rf13.23 0.139 3.26 0.136
23.23 3.3190.1024 23.87 3.410.1008
Safety risks
Sf113.45 0.104 3.58 0.105
Sf143.42 0.103 3.55 0.104
Sf83.55 0.107 3.32 0.097
Sf133.42 0.103 3.42 0.100
Sf123.35 0.101 3.42 0.100
Sf43.23 0.097 3.45 0.101
Sf103.26 0.098 3.35 0.098
Sf63.16 0.095 3.45 0.101
Sf93.23 0.097 3.35 0.098
Sf23.19 0.096 3.23 0.095
33.26 3.3260.1026 34.13 3.4130.1009
Material and equipment risks
Mf103.06 0.164 3.55 0.173
Mf93.10 0.166 3.48 0.170
Mf63.10 0.166 3.45 0.168
Mf83.10 0.166 3.39 0.165
Mf73.16 0.169 3.26 0.159
15.52 3.1040.0957 17.13 3.4260.1013
Supply Chain Risks
SCf53.16 0.333 3.39 0.337
SCf43.13 0.330 3.39 0.337
SCf23.19 0.337 3.29 0.327
9.48 3.160.0974 10.06 3.3530.0991
Capital investment risks
If73.45 0.529 3.39 0.509
If63.06 0.470 3.26 0.490
6.52 3.26 6.653.3253.3250.0983
Design risks
Df33.42 0.522 3.39 0.509
Df43.13 0.478 3.26 0.490
6.55 3.2750.1010 6.65 3.3250.0983
The risks of spatial mismanagement
Tf53.26 0.513 3.52 0.519
Tf73.10 0.488 3.26 0.481
6.35 3.1750.0979 6.77 3.3850.1001
Workplace and environment risks
Ef23.16 0.505 3.23 0.510
Ef13.10 0.495 3.10 0.490
6.26 3.130.0965 6.32 3.160.0934
32.427 33.824
Table 9. The membership matrix for the probability score.
Table 9. The membership matrix for the probability score.
Membership Function of RisksMembership Function of Risk Criteria
CodeWi For Each Risk12345
Political risks
Pf40.5290.0320.0650.3550.4190.1250.0320.1260.3850.3580.097
Pf50.4710.0320.1940.4190.2900.065
Experience Risks
Kf20.3430.0000.1290.4520.2900.1290.0000.1810.4310.2690.119
Kf30.3200.0000.2580.3870.2580.097
Kf10.3360.0000.1610.4520.2580.129
Administrative risks:
Rf70.1500.0320.2260.2900.3870.0650.0140.1520.3110.3270.046
Rf20.1460.0000.1940.2580.5110.032
Rf30.1420.0000.1290.4840.3550.032
Rf40.1440.0000.0000.0000.0000.000
Rf80.1420.0000.1610.3550.3870.065
Rf50.1370.0320.1290.5160.2580.065
Rf10.1390.0320.2260.2900.3870.065
Safety risks
Sf110.1040.0320.1290.2900.4520.0970.0230.1410.4060.3450.086
Sf140.1030.0000.1290.4520.2900.129
Sf 80.1070.0650.0320.3870.3230.194
Sf130.1030.0320.1290.3230.4190.097
Sf120.1010.0000.1610.4520.2580.129
Sf 40.0970.0650.1290.3550.4190.032
Sf100.0980.0000.0970.5810.2900.032
Sf 60.0950.0000.2260.4190.3230.032
Sf90.0970.0320.1940.3550.3550.065
Sf20.0960.0000.1940.4520.3230.032
Material and equipment risks
Mf100.1640.0000.1610.6130.2260.0000.0270.1070.3540.2730.070
Mf90.1660.0970.1610.3230.3870.032
Mf60.1660.0650.1290.4840.2900.032
Mf80.1660.0000.0320.1610.4840.323
Mf70.1690.0000.1610.5480.2580.032
Supply Chain Risks
SCf50.3330.0000.1940.5160.2260.0650.0220.1400.5480.2370.054
SCf40.3300.0000.1610.5810.2260.032
SCf20.3370.0650.0650.5480.2580.065
Capital investment risks
If70.5290.0000.2260.5160.2260.0320.0000.1650.4860.2860.063
If60.4700.0000.0970.4520.3550.097
Design risks
Df30.5220.0000.2260.2580.3870.1290.0150.1950.3810.3100.098
Df40.4780.0320.1610.5160.2260.065
The risks of spatial mismanagement
Tf50.5130.0000.1610.5160.2260.0970.0160.1930.4690.2420.081
Tf70.4880.0320.2260.4190.2580.065
Workplace and environment risks
Ef20.5050.0320.2260.3550.3230.0650.0160.2580.3550.3230.049
Ef10.4950.0000.2900.3550.3230.032
Table 10. Calculation of the importance index for probability score Iprob. and impact score Iimp. for each risk category.
Table 10. Calculation of the importance index for probability score Iprob. and impact score Iimp. for each risk category.
Probability Score IndexImpact Score Index
Membership Function of Risk CriteriaIprob.Membership Function of Risk CriteriaIimp.
12345 12345
Political risks
0.0320.1260.3850.3580.0973.3550.0000.1780.3060.3550.1623.503
Experience Risks
0.0000.1810.4310.2690.1193.3250.0000.0970.3750.3340.1943.627
Administrative risks:
0.0140.1520.3110.3270.0462.7900.0140.1560.3870.3230.1203.380
Safety risks
0.0230.1410.4060.3450.0863.3310.0230.1220.4020.3010.1393.375
Material and equipment risks
0.0270.1070.3540.2730.0702.7430.0110.0910.3600.2750.0972.858
Supply Chain Risks
0.0220.1400.5480.2370.0543.1650.0210.1480.4300.3020.0973.302
Capital investment risks
0.0000.1650.4860.2860.0633.2460.0000.1780.3050.3550.1623.500
Design risks
0.0150.1950.3810.3100.0983.2810.0000.1450.5150.2100.1303.323
The risks of spatial mismanagement
0.0160.1930.4690.2420.0813.1830.0150.1590.3880.2930.1443.394
Workplace and environment risks
0.0160.2580.3550.3230.0493.1310.0160.1790.5310.1780.0983.166
Table 11. The risk importance indices according to the categories.
Table 11. The risk importance indices according to the categories.
CodeRisk CategoriesIRprob. IRimp. I
PPolitical risks3.35513.503211.7532
KExperience Risks3.32533.627112.0571
RAdministrative risks:2.79093.38059.4299
SSafety risks3.33123.375611.2424
MMaterial and equipment risks2.743102.858107.84010
SCSupply Chain Risks3.16573.302810.4517
ICapital investment risks3.24653.500311.3623
DDesign risks3.28143.323710.9025
TThe risks of spatial mismanagement3.18363.394410.8036
EWorkplace and environment risks3.13183.16699.9138
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Mansor, M.A.; Flayyih, S.S. Risk Assessment of Prefabricated Construction in Iraq Using Fuzzy Synthetic Evaluation. Buildings 2025, 15, 1622. https://doi.org/10.3390/buildings15101622

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Mansor MA, Flayyih SS. Risk Assessment of Prefabricated Construction in Iraq Using Fuzzy Synthetic Evaluation. Buildings. 2025; 15(10):1622. https://doi.org/10.3390/buildings15101622

Chicago/Turabian Style

Mansor, Maysoon Abdullah, and Shaalan Shaher Flayyih. 2025. "Risk Assessment of Prefabricated Construction in Iraq Using Fuzzy Synthetic Evaluation" Buildings 15, no. 10: 1622. https://doi.org/10.3390/buildings15101622

APA Style

Mansor, M. A., & Flayyih, S. S. (2025). Risk Assessment of Prefabricated Construction in Iraq Using Fuzzy Synthetic Evaluation. Buildings, 15(10), 1622. https://doi.org/10.3390/buildings15101622

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