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The poor capacity of prefabricated construction cost estimation is the essential reason for the low profitability of the general contractor. Therefore, this study aims to focus on the cost estimation of prefabricated construction as the research object. This research aims to enhance the accuracy of total project cost estimation for general contractors, ultimately leading to improved profitability.

This study used Vensim PLE software to establish a system dynamics model. In the modeling process, a systematic research review was used to identify cost-influencing factors; ABC classification and the analytic hierarchy process were used to score and determine the weights of influencing factors.

The total cost error obtained by the model is less than 2% compared with the actual value. It can be used to cost estimation and analysis. The analysis results indicate that there are 7 key factors, among which the prefabrication rate has the most significant impact. Furthermore, the model can provide the extreme range cost; the minimum cost can reduce by 13% from the value in the case. The factor's value can compose a cost control strategy for general contractors.

The cost of prefabricated buildings can be estimated well, and deciding the prefabrication rate is crucial. The cost can be declined by correct cost control strategies when bidding and subcontracting are in process. The strategies can follow the direction of the model.

A systemic, quantitative and qualitative analysis of cost estimation of prefabricated buildings for general contractors has been conducted. A mathematical model has been developed and validated to facilitate more effective cost-control measures.

In this paper, a systematic review of 284 articles published between 2015 and 2022 and a full-text thematic analysis of 70 selected articles was conducted to catalog and synthesize factors in a framework. Thematic analysis subsequently revealed 18 selective codes under three groups of drivers, barriers, and outcomes. These three groups were explained by four key aspects including organization, stakeholders, infrastructure, and business environment that set a framework for the digitalization of construction. The study finally concluded digitalization strategies with a focus on support mechanisms, government incentives, regulations, the transition from manual labor to technicians, organizational technology culture, methodology development, and innovation processes. Such strategies provide insight into prioritizing resources towards smooth digital transformation in construction businesses.

A two-stage methodology is adopted by undertaking a systematic literature review followed by thematic content analysis. This work concludes with an analysis of remaining research gaps and suggestions for potential future research.

In this paper, a systematic review of 284 articles published between 2015 and 2022 and a full-text thematic analysis of 70 selected articles was conducted to catalog and synthesize variables in a framework. Thematic analysis subsequently revealed a set of variables and factors describing construction digitalization under three groups of success factors, barriers, and outcomes. A critical content analysis of the representative studies was conducted to identify five future research trends as well as associated research gaps and directions on the topic.

This study contributes to practice by providing directions concerning the key strategies and priorities associated with the digitalization of construction businesses.

This ground-breaking research brings to light a classified set of factors that are important for the digitalization of construction businesses. The elicited framework contributes to the current body of knowledge by offering a unique conceptualization of both driving and adverse aspects for the seamless digital transformation of construction.

Nowadays, engineering, procurement and construction (EPC) contracts are being widely used to perform industrial and infrastructure projects because of several reasons like high speed of implementation. However, these contracts are always accompanied by high risks and uncertainties. Thus, selection of the right EPC contractor has significant importance. This paper aims to present a fuzzy multi-criteria decision-making (MCDM) model for EPC contractor prequalification.

First, the EPC contractor prequalification criteria are defined by using literature review and interviewing experts. Second, the weights of criteria are determined by interviewing experts. Then, each EPC contractor is evaluated in each criterion. Finally, fuzzy weighted average (FWA) approach is employed to select the right contractor among potential EPC contractors.

The proposed model is prepared as an applicable model for clients to select the right EPC contractors among contractors who want to conduct the project.

As a lack of applicable model does exist to assign the prequalification of EPC contractors, this study is one of the first research studies which proposed a fuzzy MCDM model for evaluation of EPC contractors. To cope with the uncertainty of the prequalification problem, fuzzy logic has been used. Using fuzzy sets leads to reaching more reliable results. Also, a real case study is provided to explain the proposed model.

This paper aims to focus on the assessment of a domestic property's energy performance status by a domestic energy assessor (DEA), to ascertain the possible underlying reasons for variability in the results of Energy Performance Certificates (EPCs). By variability, the authors mean discrepancies in assessment between different DEAs on similar properties. This is important because the uses for the EPC have been extended beyond their original function as an asset rating system, to include themes encompassing building policy decisions, building performance and the distribution of incentives and grants. Consequently, inaccuracies in EPC reporting will have a greater impact than may have been the case at the outset.

A case study approach involving the conducting of semi-structured interviews with 20 practicing DEAs was carried out, with transcribed recordings of the interview material subjected to thematic analysis. This formed part of a wider mixed methods study.

The results identify a wide range of underlying reasons for variability driven by issues in both practice and process, including conflicts of interests, the EPC auditing process, the default inputting of missing data by RdSAP where information may not be available/discoverable by the DEA, the quality and perception of EPCs and DEA training and experience.

The sample size of 20 is by definition limiting, and it is possible that different results would have been obtained from a different sample. Although thematic saturation from the analysis of the responses on the key question of whether EPCs are considered variable does mitigate this. The respondents were all in possession of five years or more experience and of carrying out EPCs for different purposes. Less experienced DEAs may inevitably have responded to questions differently. The thematic analysis gives the researcher control over the presentation of the results, and it is noted that this creates a potential for bias. The researcher is immersed in the world of construction and property, with regular contact with DEAs and EPCs, which may influence the perspective of the results.

The research identifies risks to the accuracy of EPCs. To this end, and with the specific research findings in mind, this research may be of interest to construction professionals with respect to EPC practice and procurement, to the Accrediting Bodies who audit EPCs, to the creators of RdSAP with respect to automated EPC inputs, to academics either at face value or for use in further research and to policy makers who may wish to consider RdSAP data in future with qualifiers or margins of error, or may even look to review the EPC as the instrument of choice for some applications.

There is much literature analysing the shortcomings and nuances of RdSAP results, and the software model that generates the EPC, but only very limited literature extending the discussion about RdSAP to its operator: the DEA. At the time of writing, there is no literature focusing directly on the DEA and its role within the EPC production process. Their role is more important now, given the expanding use of EPCs, and increased reliance on EPC data.

The paper covers mega infrastructure construction supply chain (MICSC) in Engineering-Procurement-Construction (EPC) projects, where the frequent occurrence of risk incidents has greatly affected human life. The research aims to establish a risk evaluation index system for MICSC in EPC projects, exploring what risk factors lead to risk incidents and measure the importance and causality of all these risk factors.

The research applies a combination of quantitative and qualitative analysis methodology to process data sequentially. In the first place, risk factors for MICSC in EPC projects are extracted and identified from literature survey and expert interviews. In the second place, an integration model fuzzy Analytic Hierarchy Process (f-AHP) and fuzzy Decision-making Trial and Evaluation Laboratory (f-DEMATEL) is constructed to comprehensively analyze all these risk factors.

12 primary risk factors and 36 secondary risk factors comprise the risk evaluation index system for MICSC in EPC projects from 178 literature and 5 professionals. The results indicate that Political Situation (F1), Social Security (F2) and Management Mode (F8) are critical risk factors, where F1 and F2 are cause factors and F8 is an effect factor.

There are three main contributions of this paper. First and foremost, from the perspective of the research content, no other study has been able to assess risk factors for MICSC in EPC projects, while embedding nine phases of the whole project life cycle and six subjects of stakeholders into a risk evaluation index system. Additionally, from the perspective of research method, a combined model incorporating f-AHP and f-DEMATEL is constructed to avoid the one-sidedness of a single model. Last but not least, from the perspective of practical significance, focusing on the critical risk factors, a series of effective measures are formulated to make appropriate management decisions for nodal enterprises of MICSC, which can improve their risk management capabilities.

This study aims to introduce and evaluate the COPULA framework, a construction project monitoring solution based on blockchain designed to address the inherent challenges of construction project monitoring and management. This research aims to enhance efficiency, transparency and trust within the dynamic and collaborative environment of the construction industry by leveraging the decentralized, secure and immutable nature of blockchain technology.

This paper employs a comprehensive approach encompassing the formulation of the COPULA model, the development of a digital solution using the ethereum blockchain and extensive testing to assess performance in terms of execution cost, time, integrity, immutability and security. A case analysis is conducted to demonstrate the practical application and benefits of blockchain technology in real-world construction project monitoring scenarios.

The findings reveal that the COPULA framework effectively addresses critical issues such as centralization, privacy and security vulnerabilities in construction project management. It facilitates seamless data exchange among stakeholders, ensuring real-time transparency and the creation of a tamper-proof communication channel. The framework demonstrates the potential to significantly enhance project efficiency and foster trust among all parties involved.

While the study provides promising insights into the application of blockchain technology in construction project monitoring, future research could explore the integration of COPULA with existing project management methodologies to broaden its applicability and impact. Further investigations into the solution’s scalability and adaptation to various construction project types and sizes are also suggested.

This research offers a comprehensive blockchain solution specifically tailored for the construction industry. Unlike prior studies focusing on theoretical aspects, this paper presents a practical, end-to-end solution encompassing model formulation, digital implementation, proof-of-concept testing and validation analysis. The COPULA framework marks a significant advancement in the digital transformation of construction project monitoring, providing a novel approach to overcoming longstanding industry challenges.

Power plant projects are very complex and encounter serious cost overruns worldwide. Their cost overrun risks are not independent but interrelated in many cases, having structural relationships among each other. The purpose of this study is, therefore, to establish the complex structural relationships of risks involved.

In total, 76 published articles from the previous literature are reviewed using the content analysis method. Three risk networks in different phases of power plant projects are depicted based on literature review and case studies. The possible methods of solving these risk networks are also discussed.

The study finds critical cost overrun risks and develops risk networks for the procurement, civil and mechanical works of power plant projects. It identifies potential models to assess cost overrun risks based on the developed risk networks. The literature review also revealed some research gaps in the cost overrun risk management of power plants and similar infrastructure projects.

This study will assist project risk managers to understand the potential risks and their relationships to prevent and mitigate cost overruns for future power plant projects. It will also facilitate decision-makers developing a risk management framework and controlling projects’ cost overruns.

The study presents conceptual risk networks in different phases of power plant projects for comprehending the root causes of cost overruns. A comparative discussion of the relevant models available in the literature is presented, where their potential applications, limitations and further improvement areas are discussed to solve the developed risk networks for modeling cost overrun risks.

Offsite construction (OC) is an efficient method to reduce waste in the construction industry from a circular economy perspective. Yet, its uptake is subdued by the ambiguities around its supply chain. Hence, the purpose of this study is twofold: to identify the OC project delivery models, the limitations in their procurement approach to facilitate the resilience of the supply chain and interventions to promote supply chain resilience (SCR) and to identify the gaps in the existing procurement process and propose further research areas that implement strategies to improve SCR.

The study was conducted as a systematic literature review. In total, 41 peer-reviewed research papers published between 2013 and 2023 were shortlisted through the preferred reporting items for systematic reviews and meta-analysis guidelines. A descriptive analysis was conducted, followed by a thematic analysis.

The descriptive analysis reveals that the emphasis on digitising OC has shifted to transforming the business model, procurement and supply chain with a human-centric view. In thematic analysis, the predictability of the SC partners and the probabilities of evaluating the prospects are revealed as arbitrary characteristics in the current procurement strategies. Rewarding collaborative relationships among SC partners and incorporating provisions to postpone the module delivery are some interventions to promote flexibility. Drafting comprehensive and effective contracts that address transparency issues and facilitating the need for continuous development of capabilities through procurement are among the further research avenues proposed.

This study is a precursor demonstrating the potential of the procurement process to implement the decrees of SCR for better goal congruence of the OC supply chain.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.