Content area
The construction industry continues to face challenges such as increased costs, time overruns, and low quality. Off-site construction (OSC) methods are increasingly being adopted as alternatives to traditional construction practices to address these issues, with off-site manufacturing (OSM) representing a key difference in construction methods. However, existing studies have largely neglected the systematic evaluation of OSM risks on quality, cost, and delivery (QCD) outcomes, leaving a significant gap in understanding the complex interdependencies among risk factors. To improve risk management in OSC projects, it is crucial to evaluate the impact of OSM risks on QCD outcomes. This study applies the Bayesian Belief Network (BBN) method to develop an evaluation model that measures the impact of OSM risks on QCD outcomes in OSC projects. The results identify 12 significant risk factors affecting QCD outcomes in OSC projects. Five key risk groups were identified as critical for managing OSM risks. This approach provides a systematic framework for managing OSM risks and optimizing OSC practices in China.
Full text
1. Introduction
Off-site construction (OSC), also known as modular construction, has gained attention in recent years [1]. It differs from traditional construction by involving off-site module prefabrication, component transportation, and on-site component installation [2]. Compared to traditional construction methods, OSC offers significant savings in cost, time, quality control, waste reduction, and improved safety [3].
In that case, many countries, such as Singapore [4], the United States [5], the United Kingdom [6], China [7], and Australia [8], have adopted OSC as an alternative construction method. As off-site construction gains more attention, there is increasing demand for guaranteed quality, cost, and delivery (QCD) in OSC projects [7,9,10]. To maintain high QCD standards in OSC projects, it is essential to effectively identify and manage OSC risks [11].
The OSC project process includes design, off-site manufacturing, transportation, and on-site assembly, requiring joint efforts from owners, consultants, manufacturers, transporters, and contractors [12]. This interrelationship means that problems caused by one stakeholder can significantly impact the entire OSC project. As OSC shifts the on-site construction process to off-site manufacturing, the manufacturer’s role becomes crucial from the outset. Given the persistent challenges in OSC projects, off-site manufacturing (OSM) risks must be considered [13,14]. Therefore, it is crucial to understand how OSM risks influence QCD in OSC projects and identify the significant OSM risk factors that need to be analyzed.
Risk management has significantly improved in recent years. For example, Natural Language Processing with SGD has been used to automatically estimate risk levels from text in marble quarries for effective risk assessment [15], and a GPT-based AIR Agent has been developed to automatically analyze subway construction accident reports, enhancing analysis efficiency [16].
Previous studies on OSC risk and risk management mainly focus on stakeholders such as owners [17], consultants [18], and contractors [19]. However, few studies have considered OSC risks from the perspective of manufacturers. This indicates that most existing OSC risk and risk management studies lack effective methods to evaluate the impact of OSM risks on QCD in OSC projects. To address this gap, this research employs a Bayesian Belief Network (BBN) model to evaluate the impact of OSM risks on QCD in OSC projects.
This research methodology involves three key steps. First, semi-structured interviews were conducted with key Chinese OSC practitioners to identify risk factors in the OSM process. Second, a questionnaire was developed based on the identified risk factors, allowing for the determination of significant risks through quantitative analysis. Third, a BBN model was built using the collected data to evaluate how these significant risks influence QCD in OSC projects.
2. Materials and Methods
2.1. Literature Review
A narrative literature review was conducted to identify and analyze previous studies on risk management in OSC. Electronic databases such as Web of Science, Scopus, and Google Scholar were searched using predefined keywords (‘off-site construction’, ‘risk management’, ‘OSM’, ‘quality, cost, delivery’). This narrative approach provided a solid basis for identifying research gaps and justified the subsequent analysis using the BBN model.
2.1.1. Risk and Risk Management in OSC
Risk management is one of the ten key areas in project management [20]. Compared to traditional construction methods, OSC involves distinct risks but offers advantages such as cost, time, and waste reduction, as well as improved safety and quality control [3]. OSM is unique to OSC projects, making its risks and management distinct from those in traditional construction [21]. For example, OSM requires a higher initial capital outlay for manufacturing facilities [22], the component design is fixed during the early development stage [23], and transporting large components becomes increasingly challenging for logistics providers [24].
Many studies have explored risk management practices in OSC projects [3,25,26]. Lee, Jang [27] identified 12 critical risk factors and 50 management activities in OSC, highlighting a significant performance–importance gap that demands intensive management. Hussein, Eltoukhy [28] demonstrated that BIM technology can significantly improve safety management in modular construction through enhanced training, inspection, and site optimization. Chatzimichailidou and Ma [29] identified supply chain risks in the OSC process from 309 articles through a mixed review method. Risk management in OSC projects is generally divided into three steps: risk identification, risk analysis, and risk response [25]. Enshassi, Walbridge [30] proposed a tolerance-based risk framework for modular construction to manage geometric variability and reduce rework, cost overruns, and schedule delays. Li, Al-Hussein [3] identified key modular construction risk factors using fuzzy AHP and simulation, revealing cost and schedule impacts. Darko, Chan [31] reviewed BIM-based risk management in modular integrated construction, examining current integrations and suggesting future automation- and safety-focused research directions. Vithanage, Sing [32] reviewed OSM safety and classified risks under human, organizational, and environmental factors.
This research focuses on evaluating the influence of OSM risks on QCD in OSC projects, with a particular emphasis on risk analysis methods. However, previous studies do not specifically identify or classify OSM risk factors. Therefore, this research investigates several risk factors that occur in the OSM process and analyzes how they influence OSC projects.
2.1.2. The Importance of QCD in OSC Projects
One risk factor could influence at least one project objective [33]. By impacting project objectives, the risk factor may cause significant project issues. Evaluating the influence of risk factors on project objectives is crucial for effective problem control. Evaluating the influence of risk factors helps to identify significant risks and assess how they affect OSC projects, allowing project managers to respond more efficiently. To understand how risk factors influence OSC projects, the concept of QCD is introduced as a project performance criterion. Quality refers to the level of adherence to design and performance standards, cost refers to the efficiency in budget management, and delivery refers to the timeliness of completing and handing over project components. QCD is commonly used as a measure of project success. Atkinson [34] highlighted the importance of QCD for project success, often referred to as the Iron Triangle. Thomas, Barton [35] proposed QCD as a measure of Six Sigma effectiveness in manufacturing. Kojima and Amasaka [36] developed a quality assurance model to prevent defects and achieve QCD in projects.
QCD is often regarded as a direct result of effective project management in many construction projects. Tayur, Ganeshan [37] emphasized the importance of teamwork, cooperation, and effective coordination throughout the construction supply chain. Carpenter and Bausman [38] analyzed the QCD of a public school construction project over a two-year period to understand the inherent risks. Risk factors closely linked to QCD are considered significant. These significant risk factors have a major impact on the success of OSC projects. Given the relationship between OSC stakeholders, significant risk factors from one stakeholder can affect those of others [39]. To understand how significant risk factors influence the OSM process, it is essential to evaluate their impact on QCD in OSC projects.
2.1.3. Risk Analysis in OSC
The traditional probability and impact (P-I) model has been widely used to assess various risks in OSC project management. For example, Arashpour, Abbasi [40] defined risk using the P-I method for theorizing risk analysis in hybrid projects. Wu, Xu [17] used the P-I method to analyze risks in OSC-integrated design and construction project delivery. Yang, Pan [41] used the P-I method for risk analysis in OSC logistics for high-rise building projects. According to the Project Management Body of Knowledge (PMBOK) [42], the P-I model is a standard technique for risk analysis, where each risk is assigned a likelihood (probability) and a potential effect on project objectives (impact). By multiplying or otherwise combining these two dimensions, project teams can prioritize risks, allocate resources, and develop appropriate response strategies.
Other risk analysis methods have also been adopted in OSC projects. Monte Carlo simulation is commonly used for risk estimation. Rausch, Nahangi [43] employed this method for tolerance analysis to reduce the risk of component rework. The multi-criteria decision-making (MCDM) framework has been used for complex decision-making processes, and Shahpari, Saradj [44] developed an MCDM framework to compare the productivity of OSC and traditional construction. Decision support systems (DSS) were used by Wuni and Shen [45] to review the determinant factors in deciding to use OSC methods. The Analytical Hierarchy Process (AHP) is another widely used approach for prioritizing decision alternatives. Li, Al-Hussein [3] deployed AHP to identify risk factors affecting project cost and duration in OSC projects.
2.1.4. BBN in Construction Risk Analysis
The Bayesian Belief Network (BBN) is widely used in risk management due to its ability to graphically represent conditional dependencies between variables [46,47,48]. For example, Zhou and Zhang [49] developed a BBN model for deep foundation pit construction projects to evaluate the risk probability in China. Kabir, Balek [50] used the BBN to prioritize buried infrastructure for maintenance, rehabilitation, or replacement. Xue and Xiang [51] developed a BBN model to analyze key social risks in high-speed rail projects in China.
In the construction project, a BBN could present the interrelationship between each risk factor and update when new information is available [52]. A BBN can combine both objective data and subjective data, which can improve the quality of input data, especially when the historical data are limited and difficult to obtain [53]. Hon, Sun [54] indicated that a BBN to risk management still has room for improvement in dynamic risk management, which should cover all stages of the project.
Although the BBN has been widely applied to manage risks in construction-related research, few articles have used the BBN to analyze risks in OSC construction projects, especially for the OSM process. Although Yu, Man [12] deploys a BBN model for OSC projects, it focuses on stakeholder impacts regarding quality issues, not on OSM risk management. Considering the clear advantages of a BBN, this research uses a BBN model to analyze OSM risk factors and the relationship between QCD and OSM risk factors.
2.2. Methods
In the previous step, a literature review was carried out to compile a preliminary set of risk factors that potentially affect the QCD outcomes in OSM. Table 1 below presents these factors, categorized by risk group. Many of these items may influence multiple aspects of QCD, thus reflecting the range of challenges identified in existing studies.
To achieve the objectives of this study, a Bayesian Belief Network (BBN) model was employed. The BBN has been widely applied to risk management in various fields, including computer engineering [59], civil engineering [60], economics [60], and disaster prevention [61]. In traditional construction, the BBN has proven effective in managing risks during the construction process [62,63,64]. Since OSM involves a multi-stage production process similar to traditional construction, the BBN is a suitable method for managing OSM risks. Additionally, the BBN can effectively analyze complex, multi-factor systems with interdependent risk factors, making it an ideal approach for evaluating the relationship between risks and QCD outcomes in OSC projects. Therefore, a BBN was selected as the primary method for this research.
Figure 1 presents the research flow chart of this study. The study followed a systematic approach consisting of three primary steps:
Literature Review: The existing literature on OSC risk factors was analyzed to identify the most relevant OSM risk factors and compare existing risk management methods.
Data Collection: Interviews and questionnaires were conducted with OSC practitioners to identify significant OSM risk factors.
BBN Model Development: A BBN model was developed based on the identified significant risk factors, and sensitivity analysis was conducted to evaluate the relationship between OSM risks and QCD in OSC projects.
3. Results
3.1. Risk Collection and Validation
3.1.1. Risk Collection—Interview
According to Purdy [65], OSM risk is defined as an uncertain event or condition that, if it occurs, may positively or negatively impact one or more objectives of an OSC project. In this context, OSM risks can result in changes to QCD in the OSM process, such as increases or decreases in the QCD of OSC manufacturing production. The literature on OSM process risks is limited and incomplete [66]. However, as the OSM process is part of the broader OSC project, risks in OSC projects can serve as a reference for identifying OSM process risks [67]. This research tried to identify the OSM risk by recognizing the OSC risk that may happen in the OSM process and influencing the QCD of the OSM process.
The purpose of this phase was to identify the risk factors in the OSM process. Based on Table 1, semi-structured interviews were conducted with key members of the OSC project team. Since OSC projects require cooperation among all participants, OSM risks may arise not only from the OSC manufacturer but also from other stakeholders. Therefore, the interviewees in this study included owners, consultants, manufacturers, transporters, and contractors. This approach is consistent with the methods used in similar studies focusing on OSC risks and risk management [7,21,68]. A total of 25 managers and engineers from various OSC projects were interviewed to identify the OSM risks for further analysis.
The data collected during the interview process were analyzed using NVivo 14 software. A total of 61 risk factors were identified as associated with the OSM process risks. Risk types in this study are classified into three categories: Internal risks, which are internal factors inherent to the manufacturing process. Participant risks relate to the interactions and coordination among various stakeholders. External risks relate to external environmental conditions. Table 2 presents the OSM process risk that affects the OSC project.
3.1.2. Risk Validation—Questionnaire
After the OSM process risk factors were identified, the next step was to determine which factors were the most significant. A five-point Likert scale questionnaire was then used to validate the OSM risks. The questionnaire was carefully designed based on the 61 risk factors identified in the interviews to assess the significance of each risk factor.
The snowball sampling method was used in the questionnaire process. Similar to the interview process, all OSC participants were considered appropriate targets for the questionnaire data, including owners, consultants, manufacturers, transporters, and contractors. The questionnaire consisted of two sections: personal data and the identification of significant risks. The first section asked respondents to provide information on their experience, company type, and the materials used in their OSC projects. The second section included the OSM process risk factors identified during the interview process and asked respondents to assess the probability and impact of each risk using a five-point Likert scale.
The Statistical Package for Social Science (SPSS, version 29.0) was used to analyze the data generated from the questionnaire. A total of 438 copies of the questionnaire were distributed to Chinese OSC practitioners, who were then asked to distribute the questionnaire to other OSC practitioners. A total of 120 questionnaires were received from the first-tier respondents. After one month of data collection, a total of 436 questionnaires were returned to the researcher as part of the snowball sampling process. More than 64% of respondents have more than 5 years of experience in the construction industry. Although only 26% have more than 5 years of experience in the OSC industry, considering OSC was vigorously promoted in China after the 13th Five-Year Prefabricated Building Action Plan in 2016, it is acceptable for many of the respondents to have less than 5 years of experience in OSC industry [69]. The types of respondents’ company types show that the respondents’ company types are, on average, between the owner (17%), consultant (28%), manufacturer (28%), transporter (4%), contractor (36%), and others (7%); this confirms the reliability of collected data for identifying significant OSM risk factors for a variety of angles. Moreover, most of the respondents (84%) have been involved in the concrete structure OSC project, which implies that this research focuses mainly on the concrete structure OSC project.
The collected data were statistically analyzed to determine the mean values for the 61 OSM risk factors. Since the questionnaire considered both the probability and impact of each risk factor, those factors with mean scores for both probability and impact that were higher than the average mean scores (3.30 for probability and 3.34 for impact) were identified as significant risks. This dual-measurement approach enables the identification of risks that consistently exhibit high levels in both dimensions, which are more likely to have a sustained effect on project performance. The Cronbach’s alpha value was calculated, and the result was 0.991, which exceeds the 0.7 threshold, indicating that the five-point scale measurement was reliable at the 5% significance level [70].
A total of 28 valid significant risk factors were identified, as shown in Table 3. These 28 significant risk factors were grouped into three types: internal risk, participant risk, and external risk.
3.2. BBN Model Analysis
After the significant OSM risk factors were identified, the BBN model was employed. Since the effects of the OSM risks are not always simple or direct, the relationships between different OSM risks need to be visually represented for further analysis [25]. The BBN model was constructed to illustrate these relationships.
3.2.1. BBN Model Development
The data collected from the interviews and questionnaires were analyzed using Netica v5.18 software, a type of BBN decision-making tool. By presenting a visual representation of the interactions between the significant risk factors in the OSM process, the relationships and levels of risk could be determined. It also linked the risk factors to the QCD outcomes to assess how these risks lead to cost overruns, quality issues, and time delays in OSC projects.
Figure 2 presents the structure of the BBN model, which is based on the interview results. The interviewees were asked to verify the relationships between the risk factors and QCD. This BBN model consists of 31 nodes, including 28 risk factors that could impact the OSM process objectives—low quality, extra cost, and time delays in OSC projects—and three nodes representing quality, cost, and delivery (QCD). The rectangles represent nodes, which denote the OSM risk factors or QCD, and the arrows represent edges, reflecting the causal relationships between different nodes. This figure illustrates that all significant risk factors can directly or indirectly affect the QCD of the OSM process. The risk factors that directly affect QCD are referred to as proximal factors, while those that indirectly affect QCD are referred to as distal factors.
However, Figure 2 only presents the relationship between the significant OSM risk factors and QCD outcomes in OSC projects; it does not show the effects of each node’s state on the distribution of the other factors. To capture these dynamic interactions, each node in the Bayesian Belief Network was assigned discrete states (e.g., low, medium, or high) that represent different levels of occurrence or impact. In this framework, the Conditional Probability Table (CPT) is not only used to illustrate the static relationships between nodes but also serves as the basis for probabilistic inference and risk propagation throughout the network. To demonstrate the impacts between risk factors and QCD, each node must be assigned an appropriate state. In this research, a CPT was used to reflect the strength of the edges between directly connected nodes [71]. A CPT is a table that provides probabilities for every possible combination of the parent and child states. For example, if node B is a child of node A, the CPT includes a table showing whether node A has occurred or not, along with the corresponding probabilities of node A’s state.
In this research, Figure 2 was presented to the 25 experts who participated in the previous interviews. These experts were asked to identify the conditional probabilities for each node and fill in the CPT for each node. Table 4 presents an example of a CPT, where IR7 is the child node, and IR4 and ER7 are the parent nodes. This table illustrates the probability evaluation of the relationships between the nodes. To assist the experts in identifying the nodes, each node was assigned three states: low, medium, or high, representing the probability levels for each node. The probability levels are typically categorized into three ranges: low probability (less than 10%), medium probability (between 10% and 20%), and high probability (greater than 20%). These probability levels are used to assess the likelihood of various risk events occurring [72].
After review, the BBN model for OSM risks and QCD outcomes in OSC projects was developed, as shown in Figure 3. In Figure 3, each node has four characteristics essential for the BBN model: node name, node status, relationships with other nodes, and the CPT.
To assist the OSM project manager in evaluating the effects of each node’s state on the distribution of other nodes, a sensitivity analysis will be presented in the next section.
3.2.2. Sensitivity Analysis for BBN Model
After the BBN model was constructed, a sensitivity analysis was conducted. The sensitivity analysis measures the degree to which any node can influence the beliefs of another node [73]. Table 5, Table 6 and Table 7 show that the risk factors ER5, ER1, ER4, PR9, IR5, and IR10 were identified as sensitive quality-related factors, each having a relatively high degree of impact on the occurrence of low quality. Risk factors IR2, IR1, and PR7 were identified as sensitive cost-related factors, each having a relatively high degree of impact on the occurrence of extra costs. Risk factors PR9, IR5, PR3, IR10, IR3, IR4, and IR7 were identified as sensitive delivery-related factors, each having a relatively high degree of impact on the occurrence of time delays. Quality and delivery were identified as critical factors that significantly affect the results of the final OSM production. Therefore, more attention should be given to quality and delivery in order to reduce risks in OSC projects.
In this research, IR1, IR2, IR3, IR4, IR5, IR7, IR10, PR3, PR7, PR9, ER1, ER4, and ER5 were highlighted as significant OSM risk factors that can substantially affect the QCD outcomes of the OSM process. Based on the BBN model, these factors were also identified as sensitive nodes, underscoring the importance of these risk factors in OSC projects. Quality and delivery were identified as critical factors affecting the objectives of the OSM process, and many of these sensitive factors were directly or indirectly linked to the two critical factors.
4. Discussion
To specify how the significant OSM risk factors affect the OSM process, the viewpoints of the interviewees are summarized. Based on different risk types, all significant and sensitive risks are analyzed in the following section. Table 8 presents the risk group of each sensitive risk factor.
Many studies highlight the importance of costs in OSC projects [8,10]. This research emphasizes that cost also has a significant impact on the OSM process. For manufacturers, a large initial investment leads to inflexible capital turnover. For example, material costs, component model fees, and factory building costs make the initial investment for the OSC method considerably larger than for traditional construction methods. Additionally, the ’pay after delivery’ convention, which originates from traditional construction practices, exacerbates the cost problem. This financial pressure results in cost challenges for OSM, which significantly affects the QCD outcomes of OSC projects.
As mentioned in the literature review, few articles have considered OSM risks and risk management, which has led to project management risks in the OSM process. Compared with traditional construction, OSC involves an additional OSM process. Conventional risk management methods, which are designed mainly for on-site construction, do not address the unique challenges presented by OSM and are, therefore, not suitable for OSC projects. Traditional construction risk management methods are not suitable for OSC projects; however, many OSC companies still use traditional construction risk management methods to manage OSC project risks. The cost of new risk management methods for OSC projects has weakened the willingness of OSC practitioners to develop and implement OSC-specific risk management strategies. Manufacturers complained about the lack of cooperation among all OSC practitioners, which resulted in the magnification of OSM risks. For example, contractors frequently change or adjust the on-site construction period, which forces OSC manufacturers to alter their production cycle, leading to other risks such as additional storage requirements for components, component production delays, etc.
The entire OSC project is based on the requirements provided by the owner. If any step does not align with the owner’s demands, it may result in OSM risks. In this case, the owner’s demands should remain consistent throughout the OSC project process. However, many owners frequently change their demands during traditional construction projects, and this habit persists in OSC projects as well. Due to the owner’s lack of experience with OSC, few owners realize that changes in demand can significantly disrupt the processes of other OSC stakeholders.
Consultants play a significant role in the OSM process. Errors in the design scheme can significantly affect the accuracy of the production process. A reasonable, accurate, and detailed design scheme is essential in OSC projects [12]. Reducing design errors will help the manufacturer meet their requirements more easily and reduce the chances of rework. Consultants should also avoid designing arc components or special-shaped components. Otherwise, the manufacturer must purchase special-shaped models to produce these components, which increases the cost of OSM. In this case, consultants should have more experience in OSC projects and communicate more effectively with manufacturers.
Manufacturers have been shown to significantly influence the final quality of OSC projects [74]. One of the reasons for this is the lack of experience among manufacturers. Although many OSC factories have industrialized OSC production lines, few manufacturers have the experience to operate these lines. For example, one manufacturer imported a German OSC production line, but no one knew how to program the line, and only several pre-set component types could be produced. When the production line broke down, no one could fix it, and due to COVID-19, it was nearly impossible to bring German experts into China. This 200 million Yuan production line became an ‘extremely expensive storage yard’. Therefore, the manufacturer’s lack of OSC experience greatly affected the QCD outcomes of the OSC project.
The public has a prejudice that OSC buildings are unsafe, a bias that stems from the 1976 Tangshan earthquake, during which over 95% of the houses in the city collapsed, and more than 600,000 residents were either killed or injured. Many of the victims lived in OSC buildings. As a result, prefabricated components were dubbed ‘coffin boards’ by the public after the earthquake [75]. This historical issue continues to affect the promotion of OSC today. Another issue is the occasional practice of subcontracting in some areas of China. Some contractors may win contracts at prices well below the market rates and then seek to subcontract the work in order to make a profit. This forces manufacturers who aim to produce high-quality components to make a difficult choice: whether to accept lower profits to secure the contract or maintain profits but lose the contract.
5. Conclusions
In this research, a BBN model was used to evaluate the risk factors in the OSM process, considering the relationship between these risk factors and the QCD outcomes of OSC projects. Existing studies on construction risk management have predominantly focused on traditional on-site methods, while the unique challenges and interdependencies inherent in OSM have received limited attention. This research addresses a notable gap in the literature by systematically evaluating the OSM risks and their impacts on project performance, thereby offering a novel framework for risk assessment in OSC projects. Interviews and questionnaires were developed and conducted in China to identify the significant risk factors in the OSM process. The BBN model was developed based on these significant risk factors, and the relationships between risk factors and QCD in OSC projects were evaluated using the BBN model. The analysis identified 12 significant risk factors that were further grouped into six categories: cost, project management, owner, consultant, manufacturer, and society. Notably, the sensitivity analysis revealed that factors such as high off-site manufactory building costs, high component model and material fees, and manufacturer’s lack of experience have a particularly strong influence on project outcomes. The results identified significant and sensitive risk factors for the OSM process, which were categorized into six risk groups: cost, project management, owner, consultant, manufacturer, and society. These findings contribute to the body of knowledge regarding OSM process risks and risk management in China.
Based on the findings, several key recommendations are proposed: Consultants are advised to adopt standardized design practices and minimize the use of complex or atypical components. Manufacturers should optimize prefabrication designs and enhance staff training to reduce production-related risks. Transporters should develop flexible logistics strategies to accommodate production and delivery uncertainties. Contractors need to improve coordination with manufacturers to ensure aligned project schedules. Academics are encouraged to extend research on OSC risk management to a broader international context and consider the development of a comprehensive OSC risk management database.
The limitations of this research should be acknowledged. First, the data collection process may reflect subjective biases, which could reduce the accuracy of the results. Second, the data were collected from mainland China; therefore, the findings may apply to other OSC projects in China with minor modifications. However, these results may not be applicable to other countries, especially developed nations. Therefore, future research should focus on OSM in other countries, and it is also recommended that an OSM risk management database be developed to help practitioners evaluate OSC projects from a more objective perspective. Moreover, further studies employing more complex modeling techniques are warranted to quantify the impact of individual risk factors on QCD outcomes in greater detail, thereby enhancing the understanding of risk interdependencies and refining OSC risk management strategies. In future studies, complementary methods such as Monte Carlo simulation could be integrated to provide a more objective quantification of uncertainties and to further validate the BBN results.
Conceptualization, Y.H.; methodology, L.Z.; software, L.Z.; validation, L.Z.; writing—original draft preparation, L.Z.; writing—review and editing, Y.H.; visualization, L.Z.; supervision, Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.
The raw data supporting the conclusions of this article will be made available by the authors upon request.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| OSC | Off-site construction |
| OSM | Off-site manufacturing |
| QCD | Quality, cost, and delivery |
| BBN | Bayesian Belief Network |
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
OSC risk group and risk factor.
| Risk Group | Risk Factor | References |
|---|---|---|
| Cost | High cost of initial investment | [ |
| High design costs | [ | |
| High transport costs | [ | |
| High manufacturing costs | [ | |
| Culture | There is a bias that OSC can only produce low-cost products | [ |
| Flexibility | Clients and designers might change their demands | [ |
| The design must be frozen | [ | |
| Health and safety | Regular mobile cranes are unsuitable for off-site components | [ |
| Heavy OSC components may increase hazards in the event of an earthquake | [ | |
| Knowledge | Lack of adequate knowledge of OSC | [ |
| Supply chain | Components from a foreign country may not comply with the standards | [ |
| OSC developers choose certain suppliers only | [ |
Risk factors for OSM process.
| Risk Type | Risk Group | Risk Factors |
|---|---|---|
| Internal risks | Cost | High off-site manufactory building costs |
| Overall working process increase | ||
| Components paid after delivery | ||
| High training costs | ||
| More types of workers | ||
| More regular employees | ||
| High component model and material fees | ||
| Off-site feature | Component is too heavy | |
| Component support interference | ||
| On-site assembly limitations | ||
| Project management | Lack of cooperation | |
| On-site construction period adjustment or change | ||
| Lack of risk management method | ||
| Hard to deploy new management method | ||
| Time | Complexity of joint assembly | |
| Insufficient production time | ||
| Participant risks | Owner | Owner changes demand |
| Demands not suitable for off-site project | ||
| Owner changes project partners | ||
| Consultant | Design error | |
| Consultant lacks off-site experience | ||
| Consultant lacks on-site experience | ||
| Consultant lacks responsibility | ||
| Consultant lacks standardization | ||
| Consultant lacks suitable professional software | ||
| Manufacturer | Manufacturer lacks experience | |
| Manufacturer lacks employees | ||
| Manufacturer lacks responsibility | ||
| Unavoidable errors in component production | ||
| Assembly line lacks control | ||
| Lack of off-site manufactory facilities and equipment | ||
| Producing different types of components at the same time | ||
| Insufficient component yard storage | ||
| Insufficient assembly line | ||
| Transporter | Transporter lacks experience | |
| Transporter lacks responsibility | ||
| Mistakes arise during the transfer process | ||
| Transportation road problems | ||
| Long transport distance | ||
| Contractor | Contractor has a lack of willingness to participate in off-site project | |
| Contractor lacks experience | ||
| Contractor lacks responsibility | ||
| Contractor lacks experienced employees | ||
| Lack of on-site assembly standardization | ||
| External risks | Environment | Geographical environment |
| Manufactory indoor environment | ||
| Seasonal changes | ||
| Natural disaster | ||
| Government policy | Lack of government policy standards | |
| Local government policy standard differentiation | ||
| Rigid prefabricated rate requirement | ||
| Lack of subsidy and support | ||
| Resource | Low material quality | |
| Supply delay or not on time | ||
| Lack of material | ||
| Component model lacks standardization | ||
| Manufactory equipment damage | ||
| Society | Contract bidding problem | |
| Unstable economic situation | ||
| Public society’s prejudice for off-site building | ||
| Inconsistent quality demand for OSC project |
Significant OSM risk factors.
| Risk Type | Risk Code | Risk Factor | Risk Factor Explanation |
|---|---|---|---|
| Internal risk | IR1 | High off-site manufactory building costs | The high capital investment for off-site manufacturing, especially manufactory building costs, is a major cost for off-site manufacturers. |
| IR2 | High component model and material fees | As the component model and material lack standards, the cost is higher than the set price. | |
| IR3 | On-site construction period adjustments or changes | On-site period change results in the manufacturer having to change their production plan. | |
| IR4 | Lack of cooperation | Cooperation includes cooperation between project participants and cooperation inside the manufacturing facility. | |
| IR5 | Lack of risk management methods | The new process in the OSC project requires a new risk management method. | |
| IR6 | On-site assembly limitations | On-site assembly limitation includes extra support for the component, extra steps for the on-site assembly process, and interference for the construction workers. | |
| IR7 | Components paid after delivery | Manufacturer cannot get their funds until the project is finished, which increases their costs. | |
| IR8 | Component is too heavy | Heavy component causes all transport processes to require more time. | |
| IR9 | Complexity of joint assembly | The component joint assembly is still a new technology that needs more technical support. | |
| IR10 | Hard to deploy new management method | Project management methods like Six Sigma and lean production are still relatively new for off-site manufacturers, which requires more time to establish these methods. | |
| Participant risk | PR1 | Consultant lacks off-site experience | The consultant has little knowledge about OSC process, which causes the design diagram to be unsuitable for off-site component production. |
| PR2 | Consultant lacks on-site experience | The consultant does not need to go on-site to learn how to work on-site, which causes the design diagram to be unsuitable for the construction project. | |
| PR3 | Owner changes demand | The owner can change their demand during a traditional construction project. However, the feature of the OSC project result’s changed demands require more time and costs. | |
| PR4 | Demands not suitable for off-site project | Some owners still use traditional construction requirements for OSC projects. | |
| PR5 | Contractor lacks experienced employees | There are two reasons for contractor’s lack of experienced employees. First, there are a few experienced OSC workers. Second, young people are unwilling to become on-site workers. | |
| PR6 | Contractor lacks experience | Only a few contractors have experience with OSC projects. | |
| PR7 | Design error | Design error is caused by a consultant, which include conceptual design errors and design development errors. | |
| PR8 | Consultant lack of standardization | The consultant’s lack of standards results in other participants needing to change in different projects. | |
| PR9 | Manufacturer lacks experience | The off-site manufacturer experience includes product experience, transport experience, manufactory design experience, etc. | |
| PR10 | Lack of on-site assembly in standardization | Lack of on-site assembly standardization causes the on-site assembly time to extend, which leads to the manufacturing times to extend. | |
| External risk | ER1 | Contract bidding problems | Current construction contract bidding is the lowest price win the bid, which means the quality may be relatively low. |
| ER2 | Lack of subsidy and support | Although the government provides subsidies and support for OSC, many companies still think the support is insufficient. | |
| ER3 | Component model lacks standardization | Different component model companies have different standards, which means the components from different component models cannot be assembled. | |
| ER4 | Public society’s prejudice against off-site building | OSC projects require much less time for on-site processes than traditional construction. However, many people think it is too quick; it must not be safe to live in these. | |
| ER5 | Inconsistent quality demands for OSC project | OSC could increase the quality of a building. However, as the off-site manufacturer has a similar production environment as a general manufacturer (car, phone, etc.), some people think the OSC project has a similar quality to a general manufacturer. | |
| ER6 | Rigid prefabricated rate requirement | The government policy gives the off-site company a certain requirement for a prefabricated rate. However, some buildings are not suitable for OSC; to reach the prefabricated rate, the OSC company has to pay extra costs. | |
| ER7 | Unstable economic situation | As the trade war and COVID-19 happened in recent years, the economic situation is unstable, which caused OSC project reduction. | |
| ER8 | Local government policy standard differentiation | Different provinces have different policies, which leads to an OSC company having to change its operation method in a new province. |
Example for CPT.
| IR7 | ||||
|---|---|---|---|---|
| Parent Nodes If | Child Node | |||
| IR4 | ER7 | Low | Medium | High |
| Low | Low | |||
| Low | Medium | |||
| Low | High | |||
| Medium | Low | |||
| Medium | Medium | |||
| Medium | High | |||
| High | Low | |||
| High | Medium | |||
| High | High | |||
Quality sensitivity analysis.
| Node | Entropy Reduction | Percent | Variance of Beliefs |
|---|---|---|---|
| ER5 | 0.11969 | 8.07 | 0.0257942 |
| ER1 | 0.10619 | 7.16 | 0.0214719 |
| ER4 | 0.10141 | 6.84 | 0.0218519 |
| PR9 | 0.08731 | 5.89 | 0.0187870 |
| IR5 | 0.08731 | 5.89 | 0.0187870 |
| IR10 | 0.05499 | 3.71 | 0.0120208 |
| Delivery | 0.01980 | 1.34 | 0.0041432 |
Cost sensitivity analysis.
| Node | Entropy Reduction | Percent | Variance of Beliefs |
|---|---|---|---|
| IR2 | 0.16912 | 11.4 | 0.0329685 |
| IR1 | 0.13547 | 9.12 | 0.0310714 |
| PR7 | 0.02009 | 1.35 | 0.0036497 |
Delivery sensitivity analysis.
| Node | Entropy Reduction | Percent | Variance of Beliefs |
|---|---|---|---|
| PR9 | 0.23067 | 15.3 | 0.0523023 |
| IR5 | 0.23067 | 15.3 | 0.0523023 |
| PR3 | 0.16621 | 11 | 0.0333913 |
| IR10 | 0.14536 | 9.66 | 0.0328930 |
| IR3 | 0.09125 | 6.06 | 0.0175443 |
| IR4 | 0.08042 | 5.34 | 0.0159779 |
| IR7 | 0.03508 | 2.33 | 0.0069591 |
| Quality | 0.01980 | 1.32 | 0.0039923 |
Sensitive risk factor.
| Node | Risk Group | Risk Factor |
|---|---|---|
| IR1 | Cost | High off-site manufactory building costs |
| IR2 | High component model and material fees | |
| IR7 | Components paid after delivery | |
| IR3 | Project management | On-site construction period adjustments or changes |
| IR4 | Lack of cooperation | |
| IR5 | Lack of risk management methods | |
| IR10 | Hard to deploy new management method | |
| PR3 | Owner | Owner changes demand |
| PR7 | Consultant | Design error |
| PR9 | Manufacturer | Manufacturer lacks experience |
| ER1 | Society | Contract bidding problems |
| ER4 | Public society’s prejudice against off-site building |
References
1. Doran, D.; Giannakis, M. An examination of a modular supply chain: A construction sector perspective. Supply Chain Manag. Int. J.; 2011; 16, pp. 260-270.
2. Nadim, W.; Goulding, J.S. Offsite production in the UK: The construction industry and academia. Archit. Eng. Des. Manag.; 2009; 5, pp. 136-152.
3. Li, H.X.; Al-Hussein, M.; Lei, Z.; Ajweh, Z. Risk identification and assessment of modular construction utilizing fuzzy analytic hierarchy process (AHP) and simulation. Can. J. Civ. Eng.; 2013; 40, pp. 1184-1195.
4. Hwang, B.-G.; Shan, M.; Looi, K.-Y. Knowledge-based decision support system for prefabricated prefinished volumetric construction. Autom. Constr.; 2018; 94, pp. 168-178.
5. Lu, N.; Liska, R.W. Designers’ and general contractors’ perceptions of offsite construction techniques in the United State construction industry. Int. J. Constr. Educ. Res.; 2008; 4, pp. 177-188.
6. Goodier, C.; Gibb, A. Future opportunities for offsite in the UK. Constr. Manag. Econ.; 2007; 25, pp. 585-595.
7. Hong, J.; Shen, G.Q.; Li, Z.; Zhang, B.; Zhang, W. Barriers to promoting prefabricated construction in China: A cost–benefit analysis. J. Clean. Prod.; 2018; 172, pp. 649-660. [DOI: https://dx.doi.org/10.1016/j.jclepro.2017.10.171]
8. Sutrisna, M.; Cooper-Cooke, B.; Goulding, J.; Ezcan, V. Investigating the cost of offsite construction housing in Western Australia. Int. J. Hous. Mark. Anal.; 2019; 12, pp. 15-24. [DOI: https://dx.doi.org/10.1108/IJHMA-05-2018-0029]
9. Johnsson, H.; Meiling, J.H. Defects in offsite construction: Timber module prefabrication. Constr. Manag. Econ.; 2009; 27, pp. 667-681.
10. Gbadamosi, A.-Q.; Oyedele, L.; Mahamadu, A.-M.; Kusimo, H.; Bilal, M.; Delgado, J.M.D.; Muhammed-Yakubu, N. Big data for Design Options Repository: Towards a DFMA approach for offsite construction. Autom. Constr.; 2020; 120, 103388.
11. Shahtaheri, Y.; Rausch, C.; West, J.; Haas, C.; Nahangi, M. Managing risk in modular construction using dimensional and geometric tolerance strategies. Autom. Constr.; 2017; 83, pp. 303-315.
12. Yu, T.; Man, Q.; Wang, Y.; Shen, G.Q.; Hong, J.; Zhang, J.; Zhong, J. Evaluating different stakeholder impacts on the occurrence of quality defects in offsite construction projects: A Bayesian-network-based model. J. Clean. Prod.; 2019; 241, 118390.
13. Yang, Z.; Ma, Z.; Wu, S. Optimized flowshop scheduling of multiple production lines for precast production. Autom. Constr.; 2016; 72, pp. 321-329.
14. Kong, L.; Li, H.; Luo, H.; Ding, L.; Luo, X.; Skitmore, M. Optimal single-machine batch scheduling for the manufacture, transportation and JIT assembly of precast construction with changeover costs within due dates. Autom. Constr.; 2017; 81, pp. 34-43.
15. Eker, H. Natural Language Processing Risk Assessment Application Developed for Marble Quarries. Appl. Sci.; 2024; 14, 9045. [DOI: https://dx.doi.org/10.3390/app14199045]
16. Zhang, L.; Hou, Y.; Ren, F. AIR Agent—A GPT-Based Subway Construction Accident Investigation Report Analysis Chatbot. Buildings; 2025; 15, 527. [DOI: https://dx.doi.org/10.3390/buildings15040527]
17. Wu, P.; Xu, Y.; Jin, R.; Lu, Q.; Madgwick, D.; Hancock, C.M. Perceptions towards risks involved in off-site construction in the integrated design & construction project delivery. J. Clean. Prod.; 2019; 213, pp. 899-914.
18. Sutrisna, M.; Goulding, J. Managing information flow and design processes to reduce design risks in offsite construction projects. Eng. Constr. Archit. Manag.; 2019; 26, pp. 267-284.
19. Mao, C.; Xie, F.; Hou, L.; Wu, P.; Wang, J.; Wang, X. Cost analysis for sustainable off-site construction based on a multiple-case study in China. Habitat Int.; 2016; 57, pp. 215-222.
20. Rose, K.H. A Guide to the Project Management Body of Knowledge (PMBOK® Guide)—Fifth Edition. Proj. Manag. J.; 2013; 44, e1.
21. Wang, Z.; Hu, H.; Gong, J. Framework for modeling operational uncertainty to optimize offsite production scheduling of precast components. Autom. Constr.; 2018; 86, pp. 69-80.
22. Pan, W.; Goodier, C. House-building business models and off-site construction take-up. J. Archit. Eng.; 2011; 18, pp. 84-93.
23. Li, H.; Guo, H.; Skitmore, M.; Huang, T.; Chan, K.; Chan, G. Rethinking prefabricated construction management using the VP-based IKEA model in Hong Kong. Constr. Manag. Econ.; 2011; 29, pp. 233-245.
24. Aburas, H. Off-site construction in Saudi Arabia: The way forward. J. Archit. Eng.; 2011; 17, pp. 122-124.
25. Hashemi, A. Offsite Manufacturing: A Survey on the Current Status and Risks of Offsite Construction in Iran. J. Civ. Eng. Archit.; 2015; 9, pp. 141-152.
26. Khameneh, A.-H.; Taheri, A.; Ershadi, M. Offering a framework for evaluating the performance of project risk management system. Procedia-Soc. Behav. Sci.; 2016; 226, pp. 82-90.
27. Lee, S.; Jang, Y.; Yi, J.-S.; Son, J. Identifying critical risk factors and management activities for effective offsite construction projects during construction phase. J. Manag. Eng.; 2023; 39, 04023018.
28. Hussein, M.; Eltoukhy, A.E.; Karam, A.; Shaban, I.A.; Zayed, T. Modelling in off-site construction supply chain management: A review and future directions for sustainable modular integrated construction. J. Clean. Prod.; 2021; 310, 127503.
29. Chatzimichailidou, M.; Ma, Y. Using BIM in the safety risk management of modular construction. Saf. Sci.; 2022; 154, 105852.
30. Enshassi, M.S.; Walbridge, S.; West, J.S.; Haas, C.T. Integrated risk management framework for tolerance-based mitigation strategy decision support in modular construction projects. J. Manag. Eng.; 2019; 35, 05019004.
31. Darko, A.; Chan, A.P.; Yang, Y.; Tetteh, M.O. Building information modeling (BIM)-based modular integrated construction risk management–Critical survey and future needs. Comput. Ind.; 2020; 123, 103327. [DOI: https://dx.doi.org/10.1016/j.compind.2020.103327]
32. Vithanage, S.C.; Sing, M.; Davis, P.; Pillay, M. Systematic review on the identification of safety risks in off-site manufacturing (OSM). J. Eng. Des. Technol.; 2022; 20, pp. 935-964. [DOI: https://dx.doi.org/10.1108/JEDT-12-2020-0498]
33. Zavadskas, E.K.; Turskis, Z.; Tamošaitiene, J. Risk assessment of construction projects. J. Civ. Eng. Manag.; 2010; 16, pp. 33-46.
34. Atkinson, R. Project management: Cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. Int. J. Proj. Manag.; 1999; 17, pp. 337-342. [DOI: https://dx.doi.org/10.1016/S0263-7863(98)00069-6]
35. Thomas, A.; Barton, R.; Byard, P. Developing a Six Sigma maintenance model. J. Qual. Maint. Eng.; 2008; 14, pp. 262-271. [DOI: https://dx.doi.org/10.1108/13552510810899463]
36. Kojima, T.; Amasaka, K. The total quality assurance networking model for preventing defects: Building an effective quality assurance system using a total QA network. Int. J. Manag. Inf. Syst. (IJMIS); 2011; 15, pp. 1-10. [DOI: https://dx.doi.org/10.19030/ijmis.v15i3.4637]
37. Tayur, S.; Ganeshan, R.; Magazine, M. Quantitative Models for Supply Chain Management; 3rd ed. Springer Science & Business Media: Berlin, Germany, 2012; Volume 17.
38. Carpenter, N.; Bausman, D.C. Project delivery method performance for public school construction: Design-bid-build versus CM at risk. J. Constr. Eng. Manag.; 2016; 142, 05016009. [DOI: https://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0001155]
39. Zailani, S.; Ariffin, H.A.M.; Iranmanesh, M.; Moeinzadeh, S.; Iranmanesh, M. The moderating effect of project risk mitigation strategies on the relationship between delay factors and construction project performance. J. Sci. Technol. Policy Manag.; 2016; 7, pp. 346-368. [DOI: https://dx.doi.org/10.1108/JSTPM-12-2015-0041]
40. Arashpour, M.; Abbasi, B.; Arashpour, M.; Hosseini, M.R.; Yang, R. Integrated management of on-site, coordination and off-site uncertainty: Theorizing risk analysis within a hybrid project setting. Int. J. Proj. Manag.; 2017; 35, pp. 647-655. [DOI: https://dx.doi.org/10.1016/j.ijproman.2017.02.016]
41. Yang, Y.; Pan, M.; Pan, W.; Zhang, Z. Sources of Uncertainties in Offsite Logistics of Modular Construction for High-Rise Building Projects. J. Manag. Eng.; 2021; 37, 04021011. [DOI: https://dx.doi.org/10.1061/(ASCE)ME.1943-5479.0000905]
42. Project Management Institute. A Guide to the Project Management Body of Knowledge (PMBOK® Guide)–Seventh Edition and The Standard for Project Management; Project Management Institute: Upper Darby, PA, USA, 2021.
43. Rausch, C.; Nahangi, M.; Haas, C.; Liang, W. Monte Carlo simulation for tolerance analysis in prefabrication and offsite construction. Autom. Constr.; 2019; 103, pp. 300-314.
44. Shahpari, M.; Saradj, F.M.; Pishvaee, M.S.; Piri, S. Assessing the productivity of prefabricated and in-situ construction systems using hybrid multi-criteria decision making method. J. Build. Eng.; 2020; 27, 100979.
45. Wuni, I.Y.; Shen, G.Q. Towards a decision support for modular integrated construction: An integrative review of the primary decision-making actors. Int. J. Constr. Manag.; 2019; 22, pp. 929-948.
46. Liu, Y.; Xia, Y.; Lu, H.; Xiong, Z. Risk Control Technology for Water Inrush during the Construction of Deep, Long Tunnels. Math. Probl. Eng.; 2019; 2019, 3070576.
47. Sun, J.; Liu, B.; Chu, Z.; Chen, L.; Li, X. Tunnel collapse risk assessment based on multistate fuzzy Bayesian networks. Qual. Reliab. Eng. Int.; 2018; 34, pp. 1646-1662.
48. Wu, W.-S.; Yang, C.-F.; Chang, J.-C.; Château, P.-A.; Chang, Y.-C. Risk assessment by integrating interpretive structural modeling and Bayesian network, case of offshore pipeline project. Reliab. Eng. Syst. Saf.; 2015; 142, pp. 515-524.
49. Zhou, H.-b.; Zhang, H. Risk assessment methodology for a deep foundation pit construction project in Shanghai, China. J. Constr. Eng. Manag.; 2011; 137, pp. 1185-1194.
50. Kabir, G.; Balek, N.B.C.; Tesfamariam, S. Consequence-based framework for buried infrastructure systems: A Bayesian belief network model. Reliab. Eng. Syst. Saf.; 2018; 180, pp. 290-301.
51. Xue, Y.; Xiang, P. The Social Risk of High-Speed Rail Projects in China: A Bayesian Network Analysis. Sustainability; 2020; 12, 2087. [DOI: https://dx.doi.org/10.3390/su12052087]
52. Qazi, A.; Dikmen, I.; Birgonul, M.T. Prioritization of interdependent uncertainties in projects. Int. J. Manag. Proj. Bus.; 2020; 13, pp. 913-935.
53. Wang, L.; Zhang, X. Bayesian analytics for estimating risk probability in PPP waste-to-energy projects. J. Manag. Eng.; 2018; 34, 04018047.
54. Hon, C.K.; Sun, C.; Xia, B.; Jimmieson, N.L.; Way, K.A.; Wu, P.P.-Y. Applications of Bayesian approaches in construction management research: A systematic review. Eng. Constr. Archit. Manag.; 2021; 29, pp. 2153-2182.
55. Blismas, N.; Pasquire, C.; Gibb, A. Benefit evaluation for off-site production in construction. Constr. Manag. Econ.; 2006; 24, pp. 121-130.
56. Arif, M.; Blismas, N.; Wakefield, R. Drivers, constraints and the future of offsite manufacture in Australia. Constr. Innov.; 2009; 9, pp. 72-83.
57. Blismas, N.G.; Pendlebury, M.; Gibb, A.; Pasquire, C. Constraints to the use of off-site production on construction projects. Archit. Eng. Des. Manag.; 2005; 1, pp. 153-162.
58. Tam, V.W.; Tam, C.; Zeng, S.; Ng, W.C. Towards adoption of prefabrication in construction. Build. Environ.; 2007; 42, pp. 3642-3654.
59. Fan, C.-F.; Yu, Y.-C. BBN-based software project risk management. J. Syst. Softw.; 2004; 73, pp. 193-203.
60. Siemaszko, A.; Grzyl, B.; Kristowski, A. Evidence-Based Risk Management for Civil Engineering Projects Using Bayesian Belief Networks (BBN). Proceedings of the 2018 Baltic Geodetic Congress; Olsztyn, Poland, 21–23 June 2018; pp. 191-195.
61. Cockburn, G.; Tesfamariam, S. Earthquake disaster risk index for Canadian cities using Bayesian belief networks. Georisk Assess. Manag. Risk Eng. Syst. Geohazards; 2012; 6, pp. 128-140.
62. Cárdenas, I.C.; Al-Jibouri, S.S.H.; Halman, J.I.M.; van de Linde, W.; Kaalberg, F. Using Prior Risk-Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project. Risk Anal.; 2014; 34, pp. 1923-1943.
63. Kayis, B.; Arndt, G.; Zhou, M.; Savci, S.; Khoo, Y.; Rispler, A. Risk quantification for new product design and development in a concurrent engineering environment. CIRP Ann.; 2006; 55, pp. 147-150.
64. Kembłowski, M.W.; Grzyl, B.; Siemaszko, A.; Kristowski, A. Risk Diagnosis and Management with BBN for Civil Engineering Projects during Construction and Operation. Proceedings of the E3S Web of Conferences; Pekan, Malaysia, 18–19 September 2018.
65. Purdy, G. ISO 31000: 2009—Setting a new standard for risk management. Risk Anal. Int. J.; 2010; 30, pp. 881-886.
66. Zhang, L.; Agapiou, A. Influence Diagrams for Off-Site Construction Manufacturing Risk Assessment. Proc. Inst. Civ. Eng.-Manag. Procure. Law; 2020; 174, pp. 1-15.
67. Durdyev, S.; Ismail, S. Offsite manufacturing in the construction industry for productivity improvement. Eng. Manag. J.; 2019; 31, pp. 35-46.
68. Luo, L.-Z.; Mao, C.; Shen, L.Y.; Li, Z.D. Risk factors affecting practitioners’ attitudes toward the implementation of an industrialized building system: A case study from China. Eng. Constr. Archit. Manag.; 2015; 22, pp. 622-643.
69. MOHURD. “13th Five-Year” Prefabricated Building Action Plan. 2017; Available online: https://zjt.hainan.gov.cn/szjt/zjzlaqdt/201703/2534f0f788ed48d1a406fbaddc46a975.shtml (accessed on 15 October 2022).
70. Kline, P. A Psychometrics Prime; 1st ed. Free Association Books: London, UK, 2000.
71. Tang, Z.; McCabe, B. Developing complete conditional probability tables from fractional data for Bayesian belief networks. J. Comput. Civ. Eng.; 2007; 21, pp. 265-276.
72. Kim, S.-Y.; Van Tuan, N.; Ogunlana, S.O. Quantifying schedule risk in construction projects using Bayesian belief networks. Int. J. Proj. Manag.; 2009; 27, pp. 39-50.
73. Rohmer, J.; Gehl, P. Sensitivity analysis of Bayesian networks to parameters of the conditional probability model using a Beta regression approach. Expert Syst. Appl.; 2020; 145, 113130.
74. Qi, B.; Chen, K.; Costin, A. RFID and BIM-Enabled Prefabricated Component Management System in Prefabricated Housing Production. Construction Research Congress 2018; ASCE: New Orleans, LA, USA, 2018; pp. 591-601.
75. Wang, Y.; Li, H.; Wu, Z. Attitude of the Chinese public toward off-site construction: A text mining study. J. Clean. Prod.; 2019; 238, 117926.
© 2025 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.