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Building information modeling (BIM) and blockchain applications have introduced significant benefits to the architecture, engineering, construction, and operation (AECO) industry in recent years. Although publications on BIM and blockchain integration have been increasing, no systematic examination of the present status and managerial implications of integrated BIM and blockchain has been conducted. To bridge this gap, this paper conducts a state-of-the-art review of the development of integrated BIM and blockchain in a built environment. A combination of qualitative and quantitative methods was adopted to synthesize and analyze the research evidence. The results revealed five key managerial implica­tions of BIM integration with blockchain at the project level: design and collaboration, financial management, construction management, information management, and integration management (with other cutting-edge technologies). Challenges and opportunities are outlined and articulated from both technological and managerial perspectives, such as stakeholder management, impact assessment, real-time project management, information redundancy, and incompatibility.

Despite the construction industry’s detrimental effects on environmental sustainability, the concept of green procurement (GP) is still relatively new in the developing world. The barriers hindering GP adoption need detailed investigation as studies in the context of developing countries are still limited and the underlying dimensions remain a known-unknown. This study appraised the critical barriers to GP adoption in the Malaysian construction industry. Twenty (20) barriers were identified from the detailed literature review and a field survey was conducted with 150 professionals. The ranking analysis results indicated the top five barriers are: high initial cost; lack of expertise and regulations; lack of government regulations; lack of top management commitment and lack of awareness among construction stakeholders. A factor analysis further identified five major underlying dimensions. The study showed that both government and organisational commitments are crucial in promoting GP adoption. This study adds to incremental knowledge in GP literature by analysing the critical barriers and the associated underlying dimensions to GP adoption in a developing country, which could help practitioners and policymakers in evaluating the organisation’s or industry’s readiness to adopt GP practices along with devising apt measures to mitigate the barriers to shift to sustainable and environmentally friendly construction.

Resilience is a topic that has recently emerged concerning the basics of the construction project supply chain and we can consider it as a response to disruption in the supply chain of the project. Disruption also is an unavoidable reality in today’s complex and dynamic construction supply chain, the occurrence of which can cause irretrievable damages to the system, such as financial losses. Successful companies seek to minimize disruption and maintain adequate supply chain performance before disruption occurs, rather than looking for costly and challenging post-disruption solutions. This paper covers this gap by proposing a scenario-based mixed integer-programming model aiming to minimize logistics costs and delays, while scheduling projects to address selecting the appropriate supplier at risk of disruption. So far, this quantitative view was not presented in discussions about disruptions in the project supply chain, therefore different scenarios are applied in the process to validate the model. To improve its resilience level, this model benefits from back-up suppliers’ strategy. This study focuses on providing the required materials for the project site in an emergency without incurring additional costs using a back-up supplier. Results reveal the model’s suitability in confronting the unavailability of a supplier due to disruption.

Construction monitoring in dynamic construction site environments poses significant challenges for construction management. To overcome these challenges, the implementation of computer vision (CV) technologies for construction project monitoring has gained traction. This study focuses on investigating the factors influence the successful implementation of CV technologies in monitoring construction activities within building projects. A comprehensive methodology was employed, including a systematic review of CV technologies implemented in construction and qualitative surveys conducted with construction experts. Additionally, a quantitative questionnaire was developed, and the collected data was analysed using structural equation modelling. The findings reveal the presence of 10 factors categorized into four constructs. Notably, all 10 factors demonstrate high value factor loadings and statistical significance, and among the four constructs (device, jobsite, environment, human), device (0.82) has the highest impact on the implementation of CV-based technologies on the construction site, followed by jobsite condition (0.62), human (0.61), and environment (0.51) came in the last place. By addressing these influential factors and mitigating their effects, construction stakeholders can enhance the implementation of CV technologies for monitoring construction sites. This study contributes valuable insights that inform the implementation and optimization of CV technologies in construction projects, ultimately advancing the field of construction management.

With the financial burden of government increasing, the Public-Private-Partnership (PPP) model has become an alternative method to develop public infrastructure. To efficiently promote the private sector to participate in PPP, making a proper incentive policy is critical for the government. This paper examined the effects of two governmental support policies, i.e., tax reduction and risk-sharing, on the investment decision of the private sector, and further compared the relative efficacy of these two policies. The results manifest that: first, both tax reduction and risk-sharing policies motivate private sector to invest earlier; second, although the capital structure decision of the private sector is free from the influence of the risk-sharing policy, the optimal debt level under tax reduction policy shows a U-shape relationship with the incentive ratio; third, when completion risk is large, there exists efficiency loss for total benefits of the project under the risk-sharing incentive policy. Besides, the efficacy of two incentive policies varies depending on the scenario. Firstly, given the same incentive ratio, the risk-sharing policy proves to be more effective than the tax reduction policy. Secondly, when considering the same level of incentive loss for government, tax reduction policy outperforms than risk-sharing policy in terms of efficacy. Thirdly, the efficacy of these policies also depends on the completion risk level: under small completion risk, risk-sharing policy is more effective, whereas under large completion risk, the tax reduction policy takes precedence. Based on these findings, some managerial insights that could assist government in formulating more effective incentive policies are proposed.

This study takes a nearby foundation pit project of a high-speed railway in Yancheng City, Jiangsu Province as an example to investigate the impact of deep foundation pit projects on groundwater and the surrounding environment. Through on-site monitoring, in-situ testing, and numerical simulation, we conducted a comprehensive study of the impact of foundation pit projects on groundwater and the surrounding environment. The evolution of groundwater levels and surface displacement during the excavation of foundation pits was analyzed, and a method for measuring the permeability of target soil layers in situ was studied. The simulation of recharge under various soil conditions was performed, and the outcomes demonstrated the effective isolation of hydraulic connections between the interior and exterior of the foundation pit by the water-resistant curtain. Precipitation head and constant head recharging tests were employed to ascertain the actual permeability. The characteristic time method calculation exhibited remarkable efficiency and accuracy in determining the permeability coefficient of the recharging soil layer. Recharging phreatic and confined water layers has differing degrees of influence on surface deformation, with the impact of confined water layers requiring more time to stabilize. These findings contribute significantly to a more comprehensive understanding of the environmental repercussions associated with deep excavation projects, thereby enhancing safety and environmental protection measures in excavation construction practices.

This article presents an analytical approach for calculating web stiffeners in thin-walled columns. A novel method is introduced, which treats each bending point in the cross-section web as a separate stiffener. The advantages of this calculation method are discussed, highlighting its increased versatility in designing cross-section geometry. The load-bearing strength of axially compressed thin-walled closed cross-section columns, calculated using this method, is compared to analytical calculations based on the Eurocode 3-1-3 methodology and to the finite element method analysis. Calculation results of columns with cross-sections including shallow web stiffeners were up to 9.22% less conservative when compared to the Eurocode 3-1-3 methodology. The results demonstrate great compliance of the proposed method for column crosssections with deep stiffeners. Finite element method (FEM) analysis was performed to verify the calculated load bearing strengths of the columns according to both calculation methodologies. FEM analysis confirmed the reliance of the calculated results and showed, that the load bearing strengths calculated using the newly presented methodology were ranging from 88.77% to 97.86% of load bearing strength calculated using finite element method. These results proved, that the proposed method provides an accurate load bearing strength of thin-walled columns with web stiffeners.

Task conflict is prevalent among construction project parties, however, how task conflict affects construction project performance still lacks a comprehensive investigation. Against this background, this study adopts an uncertainty management perspective to conduct an integrative model including both positive and negative mediators as well as one moderator to investigate the link between task conflict and construction project performance. Using 206 samples from the Chinese construction industry, the proposed hypotheses are empirically examined. Results suggest that project commitment (effect size = –0.046) negatively mediates the link between task conflict and project performance, whereas knowledge integration (effect size = 0.053) has a positive mediating effect. What’s more, task reflexivity is found to mitigate the negative effects of task conflict on both project commitment (coefficient of interaction item = 0.12) and knowledge integration (coefficient of interaction item = 0.08). It is also found that task reflexivity changes the direction and strength of the link from task conflict to project performance by both project commitment (from –0.155 to 0.069) and knowledge integration (from –0.049 to 0.052). These findings provide a holistic understanding of the relationship between task conflict and construction project performance, thus contributing to construction project management theoretically and practically.

Fiber-reinforced polymer (FRP) is an excellent building material for strengthening concrete structures, but it is difficult to accurately evaluate the bond strength of FRP-to-concrete due to the influence of various parameters. In this study, a novel hybrid model which combines particle swarm optimization (PSO) with random forest (RF) was proposed to predict the bond strength of FRP-to-concrete. The PSO algorithm was used to optimize the hyperparameters of the RF model. A total of 749 specimens collected from the literature were used to develop the proposed PSO-RF model. Each sample contains 11 parameters required for the model. These 11 parameters are (1) the compressive strength of concrete, (2) the tensile strength of concrete, (3) the width of concrete specimen, (4) the maximum aggregate size of concrete, (5) the tensile strength of FRP, (6) the thickness of FRP, (7) the elastic modulus of FRP, (8) the tensile strength of adhesive, (9) the bond length of FRP, (10) the bond width of FRP, and (11) the bond strength of FRP-to-concrete. The proposed PSO-RF model was compared with other machine learning models as well as ten empirical equations. Six statistical indices, namely root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Willmott’s Index of Agreement (WIA), and Legates-McCabe’s Index (LM) were used to evaluate the prediction performance of the abovementioned models. The results show that the RMSE, MAE, R2, NSE, WIA and LM values of the PSO-RF model are 1.529 kN, 0.942 kN, 0.986, 0.984, 0.996 and 0.892, respectively, for the training datasets and 2.672 kN, 1.967 kN, 0.963, 0.961, 0.989 and 0.761, respectively, for the test datasets. It can be concluded that the proposed PSO-RF model has the best comprehensive performance in predicting the bond strength of FRP-to-concrete. In addition, the sensitivity analysis of the PSO-RF model was also conducted in this study.

The construction industry is notorious for stressful jobs, particularly for ethnic minority construction workers (EM-CWs), who experience complicated issues in the host country. Despite the high-level stress of EM-CWs compared to native construction workers, the particular coping behaviors of EM-CWs is unclear. Due to their cultural differences, religious beliefs, and possible stigma related to sharing feelings, typical stress management strategies may not be effective in relieving the stress of EM-CWs. To manage the stress of EM-CWs and also to benefit organizational productivity, it is important to identify the specific coping behaviors adopted by EM-CWs in response to stress. Therefore, this study aims to explore the stress management factors of EM-CWs. Six focus group discussions were conducted, made up of five EM-CWs groups (three general worker groups and two skilled worker groups) and one group of foremen. Contextual analysis was used to analyze the focus groups’ qualitative data. The study identified 11 items of stress for EM-CWs in two major categories: emotional and physical stress. In addition to problem-focused (i.e., planned problem-solving, seeking instrumental support, and cognitive appraisal) and emotion-focused coping behaviors (i.e., seeking emotional support, emotional discharge, and escapism), the current study also explored religious-focused coping behaviors (i.e., religious practice, service, and meditation) of EM-CWs. The findings advance coping-behavior literature by addressing important features of religious coping behaviors in the construction industry. The study explored the unique stress management factors of EM-CWs, which can be used to manage the stress of EM-CWs, improve their stress management approaches, and design various stress management programs for EM-CWs. Furthermore, several recommendations were made in initiating time management training, encouraging organizations to consider mental health needs in the construction industry, acknowledging the specific cultural context of EM-CWs, striving to reduce the stigma around sharing emotions under stress, facilitating an adequate on-site environment to practice religion, and introducing mindfulness programs to relieve EM-CW stress and improve their performance.

Due to the complex nature of mega construction projects (MCPs), technological innovation risks have significantly increased. Cooperation is widely accepted as a proactive approach to resolving these risks. An in-depth study of technological innovation cooperative behavior (TICB) helps understand the underlying reasons, but studies need to pay more attention to it. This study explored the factors affecting TICB for MCPs and developed a conceptual model based on the Theory of planned behavior (TPB). It established a structural equation model to verify the relationship between influencing factors. An example verified the feasibility of the model. The results show that cooperative attitude, subjective cooperative norm, perceived cooperative behavior control, and cooperative scenarios positively affect cooperative behavior through cooperative intention. Cooperative attitude plays a mediating role between cooperative scenarios and cooperative intention. Perceived cooperative behavior control has no direct effect on cooperative behavior. This study provides a theoretical reference to guide future empirical studies and enriches the knowledge of TICB for MCPs.

Especially in the energy supply of multi-family houses, a wide variety of stakeholders are involved, from owners, to users, to energy service providers and society. They usually have different requirements and understandings of optimality, but ultimately have to make joint decisions and thus sensible compromises. In Germany in particular, there are a large number of multi-family houses and, at the same time, many government restrictions and subsidies in terms of energy supply. This makes it difficult to make clear recommendations for the choice of an energy supply concept that takes all stakeholder interests into account. We first identify the relevant stakeholders and define their objectives. In order to relate these with one another, we present a methodology based on energy system simulation and TOPSIS to make energy concepts objectively evaluable. A generic multi-family house with 40 residential units is examined, combining different energy technologies and insulation standards. There is no energy concept that satisfies all stakeholders equally and it is difficult to build coalitions between them. The best results are achieved by air-source heat pumps in combination with photovoltaic.

This paper focuses on the use of vibration measurements for the purpose of cost-effective performance evaluation for the safety management and maintenance of Japan’s social infrastructure like bridges. Since modal properties are often used to diagnose damage of structures by analysing their changes, various modal identification methods have been developed in the past few decades. Among these, the FDD method has still attractive attention because of its simplicity and practicality. It is also highly applicable to simultaneous observation at multiple points and even complex modes can be identified instantly. On the other hand, the applicability of this method to impact tests applied to evaluate the condition of structures has not been sufficiently discussed to date. In this study, we will clarify the applicability to impact tests by reconstructing the theoretical background of the FDD method. Furthermore, we will show from theory that when there is a correlation between inputs, higher-order singular values, which should be noted when applied to impact tests, will be affected. The conclusions obtained from the reconstruction of the theoretical background will be verified based on numerical experiments and actual observation records.

Multiple criteria decision-making (MCDM) has experienced significant growth in recent years, owing to its capacity to integrate even contradictory criteria. This study conducted a comprehensive literature review of MCDM for assessing, selecting, and retrofitting buildings. The bibliometric search used a search algorithm in specialized databases. A filtering and expansion process was done by reviewing references, and 91 relevant articles were selected. The analysis revealed that in a group of studies, socioeconomic criteria were used to assess the vulnerability of buildings. On the other hand, some research integrated the three dimensions of sustainability (economic, social, and environmental) along with safety considerations when identifying optimal retrofit alternatives. Classic MCDMs are prevalent in research within this field. Among the most used methods, the Analytic Hierarchy Process (AHP) was employed for criteria weighting, Simple Additive Weighting (SAW) for constructing vulnerability indices, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for building retrofitting. This literature review contributes to the path toward a holistic renovation of the existing building stock, providing recommendations for future research to improve decision-making solutions for integrating the safety and sustainability of existing buildings.

In industrial structures in which standing seam metal roofs (SSMRs) are commonly used, heat insulation and waterproofing have emerged as crucial requirements for the protection of internal equipment. However, in newly developed SSMRs, the structural systems have become increasingly complex. The installation of insulation layers between the upper and lower panels poses challenges during roof panel installations, resulting in defects owing to the carelessness of the installer. These clip defects can significantly affect the wind-resistance performance of the SSMR structure during testing. In this study, we employed finite element method (FEM) modeling and verification, utilizing the wind resistance test results of SSMRs. In addition, we conducted a variable analysis as well as a fragility assessment focusing on the location and number of clip defects in the SSMRs. The results of this study indicate that the wind performance of the roof was significantly degraded owing to SSMR clip defects. Moreover, the wind resistance performance can be quantitatively evaluated by considering the roof zone and the exposed environment under a wind load.

Machine learning (ML) presents a promising method for predicting mechanical properties in structural engineering, particularly within complex nonlinear structures under extreme conditions. Despite its potential, research has shown a disproportionate focus on concrete structures, leaving steel structures less explored. Furthermore, the prevalent combination of metaheuristic optimization (MO) and ML in existing studies is often subjective, pointing to a significant gap in identifying and leveraging more effective hybrid models. To bridge these gaps, this study introduces a novel system named the Multiple Metaheuristic Optimizers – Multiple Machine Learners (MMOMML) system, designed for predicting mechanical strength in steel structures. The MMOMML system amalgamates 17 MO algorithms with 15 ML techniques, generating 255 hybrid models, including numerous novel configurations not previously examined. With a user-friendly interface, MMOMML enables structural engineers to tackle inference challenges efficiently, regardless of their coding proficiency. This capability is convincingly demonstrated through two practical applications: steel beams’ shear strength and steel cellular beams’ elastic buckling. By offering a versatile and robust tool, the MMOMML system meets construction engineers’ and researchers’ practical and research needs, marking a significant advancement in the field.

This article presents the characteristics of four straw-filled beetle elytron plate (BEPsc) nonload-bearing walls, with their thermal and mechanical properties ascertained by the finite element method, focusing on structural parameters reported in previous studies and further optimized in this paper. The results are as follows: 1) The sequence of the four models’ mechanical and thermal insulation properties with the reported structural parameters is given. The mechanical properties are mainly affected by the core layer’s out-of-plane bending moment of inertia Ix. The insulation capability is mainly governed by the minimum cross section of the core concrete. Based on this result, a BEP with nonuniform cross-sectional honeycomb walls is proposed. 2) Further optimization is conducted on the two models with the weakest mechanical and thermal properties. The I-beam beetle elytron plate (IBEPsc) is proven most suitable for nonload-bearing walls. The potential for end-trabecular beetle elytron plate (EBEPsc) in hollow floors is expected. 3) As biomimetic models emerge constantly, it is of great significance to conduct parallel comparisons between new and old models to determine their performance levels and advantages. This paper provides an example for selecting suitable BEPs for applications and an inspiration for more work on screening outstanding biomimetic models.

Construction projects inevitably encounter adversities that threaten their functionality and performance. Understanding the enhancement of project resilience, especially in extreme situations like the COVID-19 pandemic, is crucial. This study introduces a theoretical model to explore how partnering and boundary activities among project stakeholders influence project resilience, and in turn, impact project performance. Based on data gathered from a questionnaire survey involving 172 construction projects in China, the results indicate that project resilience directly and positively affects project performance during the COVID-19 pandemic. Partnering and boundary activities emerge as primary determinants of project resilience. They not only directly impact project resilience and performance, but their impact on project resilience also indirectly influences project performance. The findings offer valuable theoretical and practical insights into the improvement of project resilience through effective boundary activities and partnering.

The design of stud connectors is aided by determining the relationship between shear strength and the input variables (number, diameter, height, tensile strength and elastic modulus of the studs, and compressive strength and elastic modulus of the concrete) that influence strength. Since strength is nonlinearly related to the influencing variables, which makes the predictions of the relevant empirical equations unreliable, the use of machine learning (ML) models is preferred. The prediction results of eight machine learning models were evaluated, including linear regression (LR1), ridge regression (RR), lasso regression (LR2), back-propagation artificial neural network (BP ANN), genetic algorithm optimized BP ANN (GA-BP ANN), extreme learning machines (ELM), random forests (RF), and support vector machines (SVM). The results show that the GA-BP ANN model is the most accurate model for prediction with a mean absolute percentage error (MAPE) of 6.17% and an R2 of 0.9599. Based on the GA-BP ANN model and the global sensitivity analysis (GSA) method, a new parameter importance analysis method was developed to compare the magnitude of the effect of different input variables on strength. It was found that stud diameter had the greatest effect on shear strength.

In light of growing environmental concerns, the reduction of CO2 emissions is increasingly vital. Particularly in the construction industry, a major contributor to global carbon emissions, addressing this issue is critical for environmental sustainability and mitigating the accelerating impacts of climate change. This study proposes the Optimal Green Design Model for Mega Structures (OGDMM) to optimise CO2 emissions, cost-effectiveness, and serviceability in highrise buildings with mega structures. The OGDMM examines the impact of each material and structural design of main members on these three critical aspects. Analytical results for high-rise buildings (120–200 m, slenderness ratio: 2.0–8.0) demonstrate that OGDMM can reduce CO2 emissions and costs by an average of 4.67% and 3.97%, respectively, without compromising serviceability. To ensure comprehensive evaluation, this study introduces five new evaluation indicators encompassing environmental, economic, and serviceability performances of high-rise buildings. Based on these criteria, optimised structural designs for high-rise buildings are classified into four categories according to slenderness ratio, leading to the formulation of corresponding design guidelines. The model’s applicability is further validated through its application to a 270-m-tall high-rise building in Korea, showing reductions in CO2 emissions and costs by 8.99% and 18.50%, respectively, while maintaining structural serviceability.

In recent years, the construction industry worldwide has shown significant interest in Building Information Modeling (BIM). This study aims to analyze the dissemination of knowledge about BIM in construction engineering applications using Main Path Analysis (MPA). The research sample comprises 3,761 papers related to BIM’s application in the construction industry, sourced from the ISI Web of Science database. Initially, we investigate trends in paper publications, conduct country and journal analyses, and examine author statistics. Subsequently, we calculate traversal counts along the search path links to reveal the development trajectory of BIM. The trajectory of BIM’s evolution in the construction industry can be divided into four stages as identified through the global key-route main path analysis: 1) BIM standardization; 2) Integration of completed building projects using BIM; 3) BIM applications in precast construction projects; and 4) BIM applications in land management. These findings provide a clear understanding of how BIM has been applied and evolved within the construction industry.

New advances in top-down construction are developing with the construction of supertall buildings in China. This study presents a finite element analysis for high-rise structural construction with a top-down method (TDM) considering complex environmental conditions. Based on this analysis model, the forces and the deformation of the diaphragm wall, beams, and soldier piles at various stages of construction are computed. Taking a super high-rise building with a 5-story basement in Nanjing as an example, the reliability and accuracy of the model is verified by comparing the measured and simulated results of displacement and stress values at various locations. The research results reveal the relationship between excavation depth, soil settlement and pile displacement, which is convenient for finding the optimal construction critical surface. It lays a foundation for the study of the critical height of the subsequent construction and facilitates the prediction of the weak link in the process. At present, this project is under construction, so this study has reference value for subsequent construction projects.

Aiming at safety regulation in the operation of major infrastructure projects (MIPs) to prevent potential risk loss and adverse social impacts, this research presents a novel model integrating evolutionary game and system dynamics (SD) for optimizing safety regulation strategies with different stakeholders involving the operating company (OC), government section (GS), and public under the bounded rationality, where the evolutionary game theory is applied to describe the interactions among stakeholders in the safety regulation of MIPs followed by simulating through adopting the SD to analyze the effects of different strategies on equilibrium solutions and the stability of game equilibrium. In view of the simulation results based on five scenarios, the dynamic penalty-incentive scenario not only effectively restrains the fluctuations of the strategy selection, but also provides an ideal evolutionary stable strategy, in which the OC could nearly choose to comply with the regulations, while the public could nearly choose to supervise the OC as their optimal strategy to prevent risks. All results indicate that the application of the evolutionary game with the SD model is an effective way to analyze the effects of different strategies and provide effective solutions to study complex multi-player game problems. Overall, this research contributes to developing an evolutionary game with the SD model for the safety regulation of MIPs, which can serve as a platform to identify reasonable regulatory strategies with great practical application.

Building and other construction assets constitute a significant part of a country’s physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries’ specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980–2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country’s construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development.

The underground powerhouse of the Shuangjiangkou hydropower station is one of the largest caverns under construction in China, and its stability during construction is crucial for safe construction. To study the stability of the surrounding rock during excavation, the displacement and stress of the surrounding rock were monitored by multi-point displacement meters and bolt stress meters. Based on the monitoring data, the elastic modulus, Poisson’s ratio, friction angle, and cohesion of surrounding rock were inversely analyzed by the PSO-BP algorithm. Then, the back-analyzed parameters were used to simulate the subsequent excavations and predict the stability of surrounding rock during the following construction. The analysis results show that the surrounding rocks were generally stable during the initial four stages of excavation, and the main factors affecting their stability were blasts and unfavorable geological structures, including the lamprophyre vein and the F1 fault. These unfavorable geological structures also significantly decrease the mechanical parameters of surrounding rock as demonstrated by back analysis, and the stability prediction results show that the omnibus bar cave and the tailrace tunnel were at the greatest risk of instability during the subsequent excavations. This study provides a practical analysis for engineering excavation of the underground caverns.

Subway station fires frequently result in massive casualties, economic losses and even social panic due to the massive passenger flow, semiconfined space and limited conditions for escape and smoke emissions. The combination of different states of fire hazard factors increases the uncertainty and complexity of the evolution path of subway station fires and causes difficulty in assessing fire risk. Traditional methods cannot describe the development process of subway station fires, and thus, cannot assess fire risk under different fire scenarios. To realise scenario-based fire risk assessment, the elements that correspond to each scenario state during fire development in subway stations are identified in this study to explore the intrinsic driving force of fire evolution. Accordingly, a fire scenario evolution model of subway stations is constructed. Then, a Bayesian network is adopted to construct a scenario evolution probability calculation model for calculating the occurrence probability of each scenario state during subway station fire development and identifying critical scenario elements that promote fire evolution. Xi’an subway station system is used as a case to illustrate the application of Bayesian network-based scenario evolution model, providing a practical management tool for fire safety managers. The method adopted in this study enables managers to predict fire risk in each scenario and understand the evolution path of subway station fire, supporting the establishment of fire response strategies based on “scenario–response” planning.

Visual impact is defined as the modification of a visual resource of the landscape, generating an effect on the perception of potential observers. This effect is evaluated using the value of the landscape that has not been altered or destroyed (visual quality of the landscape), as is the case with building projects that generate visible changes in residential areas. Numerous authors have developed methodologies to evaluate visual intrusion; however, deficiencies exist, such as the predominance of subjectivity in procedures and the lack of evaluations for buildings. Therefore, this paper proposes a methodology to evaluate and quantify the visual impact of a new building in a high population density environment. This research is divided into a description of the basic methodology, the proposal of the methodology to capture and process photographs and information, and the application of a case study of a high-rise building in a sector of Valparaíso, Chile. The main contribution of this work is the delivery of a methodological proposal that allows the evaluation and quantification of the visual quality before and after the new structure to complement structural and urban design.

Embracing sustainable strategies that consider Design for Manufacture and Assembly (DfMA) has become a rapidly growing trend in urban development. Continued uncertainty on the sustainability assessment of design could drive a series of indecisive decision-making among design alternatives, further disrupting the potential opportunities toward sustainable DfMA. However, there is a lack of research on sustainable design assessments for DfMA and establishing a sustainable index. This research establishes an integrated value model for the sustainability assessment framework and DfMA sustainability index to address this challenge. This model integrates Building Information Modelling (BIM) with MIVES, a customisable Multi-Criteria Decision Making (MCDM) tool. The pilot case of this framework is the retrofit of a commercial building’s façade system, which demonstrated the capability of the proposed framework. Data collection and analysis include the comparisons between five design alternatives. This research furthers previous studies and has three-fold significance: 1) Establishing reasonable multi-criteria for the sustainable DfMA indices; 2) Adapting the MIVES approach for comparative analysis across three building phases to make it compatible with DfMA; 3) developing a quantitative analysis method for sustainable design assessment of DfMA in the construction industry.

Several techniques were developed to improve resource allocation in construction projects, yet few in repetitive construction projects. Accordingly, this research aims at minimizing resource consumption and fluctuations using Line-of-Balance (LOB) repetitive scheduling technique. An optimization model has been developed consisting of three modules: LOB schedule module, resource leveling module, and optimization module. The model helps determine the optimum (a) start time for each activity considering any needed delays to level resources, (b) number of crews travelling from one unit to another, (c) unit to change the number of crews, and (d) the new changed number of crews. Unlike the existing efforts, the developed model provides the capabilities of (a) allowing the change of number of crews of an activity at a certain repetitive unit to increase or decrease the progress rate; and (b) accommodates non-serial repetitive projects to enhance the model’s practicality. Using a pipeline project, the model outperformed the existing LOB-based models in minimizing resource consumption and fluctuations within the desired project duration. This study offers the project planners a useful tool to efficiently utilize their projects’ resources and avoid hidden costs due to inefficient resource utilization on-site as well as overcoming shortage in resource availability.

The increased complexity of construction projects has caused various management challenges. To clarify the mechanism of construction system complexity and improve the ability to manage the complexity of construction projects, the Agent-based modeling (ABM) method has been introduced and used in the construction management field. Nevertheless, a systematic, holistic, and panoramic understanding of the use of the ABM model in the construction management field is still lacking. To address this research gap, this study reviewed 133 historical explorations retrieved from the database of Web of Science. By using the multiple objectives of construction management as the literature classification framework, the study described the research status of the agent-based modeling method in the field of construction management. On this basis, this paper suggested the improvement paths in the application of this method from three aspects. It is expected that this study will provide a theoretical basis for enhancing understanding of the use of the ABM method in construction management, and also provide insights for future explorations in the area.

A series of performance measurement need to be carried out throughout the project life cycle to ensure the successful transfer of public-private partnership (PPP) assets and meet stakeholder needs. More and more water projects are stepping into the transfer phase, but less studies carry out in-depth and systematic discussion on the performance measurement of PPP water projects at the transfer phase. Hence, to fill this gap, this research establishes a new performance measurement system (PMS) for evaluating the performance of the PPP projects stepping into the transfer phase based on the key performance indicators (KPIs). Through case study, expert interview and questionnaire, this paper formulates the logical basis behind PPP water projects at the transfer phase and subsequently constructs the transfer performance measurement system (TPMS) of these projects. Then, it conducts a confirmatory analysis of the impact relationship between the indicators based on structural equation modeling (SEM). Findings of expert interview and questionnaire indicate that there are 7 primary and 26 secondary indicators, and the model has a good fit. The TPMS will provide governments, operators and other stakeholders with a comprehensive and complete understanding as to indicators required of an effective performance measurement of PPP water projects.

Enhancing legal compliance towards corporate anti-corruption has become a tendency to assure corporate operation sustainable. Corruption that jeopardizes construction supervision, project management, operation maintenance remains a governance challenge in public construction and procurement. The research purpose is to explore the impact factors for anti-corruption legal compliance towards top 5 engineering consulting companies in the Taiwan construction industry. A comprehensive literature review brings 8 hypotheses based on the Structural Equation Modeling (SEM) involving 28 stems for a 5-scale Likert questionnaire. The pilot survey with 22 effective returns demonstrates the validation for the questionnaire. The comprehensive survey collects 314 valid returns from top engineering consulting firms and proves that 5 out of 8 hypotheses stand. The findings conclude the legal compliance framework and top 5 impact factors: commitment, ethical management policy, organization and responsibility, prevention programs, and legal compliance policy. The contributions lie on (1) the identification and ranking for the impact factors, and (2) establishment of legal compliance framework toward corporate legal compliance and anti-corruption for top engineering consulting firms in the Taiwan construction industry. The legal compliance framework is correspondingly built to present how engineering consulting companies carry out anti-corruption legal compliance to assure corporate operation sustainable.

Public-private partnerships (PPPs) have been widely applied in infrastructure development around the world. However, reasonable concessionary items are critical to compromise interest conflicts between government agencies and sponsors to ensure project success. A broad literature review centering on PPP transaction structuring revealed two significant research gaps: (1) a lack of attention to the ‘availability payment only’ (APO) funding method and (2) negligence of the public side’s perspective in determining concessionary items. The research objective was to develop a methodological framework for determining concessionary items in APO PPP projects while considering the interests of the public side. This study proposed a value-for-money (VFM) and social values integrated framework which accommodates discounted cash flow (DCF) analysis, bargaining game modeling, and multi-objectives decision-making (MODM). This framework enables a decision-making process based on both an indifferent feasible interval of concessionary items under a discount rate agreed upon by both parties and an optimal set of concessionary items. Additionally, results of a sensitivity analysis indicated that project construction profit can significantly affect feasible and optimal concession items, and the optimal concession period is less sensitive to changes in risk allocation. The application of proposed model indicated that this paper successfully provides a methodology for determining a feasible interval and an optimal concession items group tailored to APO PPP projects. This study paves the way towards a platform for the public and private partners to jointly and quickly come up with sound PPP concessional items in light of the win-win principle, particularly under the APO funding mechanism.

Performance-based contracts (PBC) have been adopted increasingly in various countries. However, establishing an objective and convincing benefit evaluation framework to facilitate promotion remains a significant challenge for those who wish to use PBCs. A recent study introduced PBC in the context of daily inspection and repair of urban roads. The pilot program in that study successfully addressed the needs of road users, road maintenance authorities, and contractors. Consequently, this study develops the benefit evaluation framework for this type of PBC for broader applications. The proposed benefit evaluation framework, consisting of one qualitative and eight quantitative performanceevaluation items, calculates the effectiveness of introducing PBC to verify its practicality. The validation results show that qualitative benefit evaluation using the Pavement Condition Index achieves a “satisfactory” rating. For the quantitative benefit evaluation, an overall benefit-cost ratio (BCR) of 5.35 suggests that it is economically viable. The developed benefit evaluation framework can be used as a simple and easy-to-use evaluation tool for other organizations to assess whether to introduce PBC. This study fills this research gap by addressing the use of PBCs in the maintenance of all categories of toll-free roads, and contributes to expanding the application of PBCs in road maintenance services.

Supply chain management plays a pivotal role in the smooth execution of prefabricated construction. One key aspect involves strategically placing and sizing buffers to handle uncertainties (e.g., stochastic material lead-times and activity durations) within the prefabricated construction supply chain (PCSC). This study examines three buffering policies based on varying combinations of time and inventory buffers to mitigate stochastic material delays and activity prolongs in PSCS, namely, pure inventory buffering policy, pure time buffering policy, and mixed inventory-time buffering policy. To enable this analysis, we characterize how stochastic material delays originating from off-site supply chains impact project schedules, and then develop mathematical procedures for sizing inventory and/or time buffers under the three buffering policies. Case application and numerical analysis are conducted to investigate the performance of these buffering policies and the impact of the project characteristics on them (e.g., due date and arrival rate). Finally, insights are extracted to assist managers in choosing appropriate policies for projects with different characteristics. In general, combining inventory and time buffers results in better performance, particularly under tight project deadlines and high arrival rates. The pure time buffering policy can also be a viable option in specific situations, allowing managers to have more choices.

Pavement management has traditionally relied on human-based decisions. In many countries, however, the pavement stock has recently increased, while the number of management experts has declined, posing the challenge of how to efficiently manage the larger stock with fewer resources. Compared to efficient computer-based techniques, human-based methods are more prone to errors that compromise analysis and decisions. This research built a robust probabilistic pavement management model with a safety metric output using inputs from image processing tested against the judgment of experts. The developed model optimized road pavement safety. The study explored image processing techniques considering the trade-off between processing cost and output accuracy, with annotation precision and intersection over union (IoU) set objectively. The empirical applicability of the model is shown for selected roads in Japan.

Despite green building and BIM technology being hot spots in the construction industry, most research remains at the technical level. Leading to exploring the fundamental driving reason and mechanism of BIM for green buildings is still lacking. This paper explored BIM’s impact mechanism and driving path on green buildings from the management’s perspective to fill this gap. Based on a literature review, 18 expert interviews, and three case studies of green buildings, the influence mechanism was analysed via a qualitative method (ISM). Then, the importance of driving factors was evaluated via quantitative analysis (ANP). Specifically, this study probed the driving path by combining qualitative and quantitative analysis (ISM-ANP). The research findings show that the driving force of BIM for green buildings comes from the fundamental factor layer and is transferred to the intermediate and direct factors layer. The critical driving path of BIM for green building is to promote the visualization of building information, collaborative management, and expand real estate investment through the guidance of policies and standards. Based on research results, this paper puts forward five suggestions: 1) Improving the policy and standard system; 2) Striving to research native software; 3) Adopting an informatization project management mode; 4) Accelerating the construction and improvement of the green building industry chain; 5) Promoting government enterprise cooperation. These results may benefit not only the coupling and coordination of the two but also the construction industry’s green transformation and high-quality development.

Random variables introduced in modelling of seismic engineering are often the result of cognitive limitations and the unpredictability of structures, leading to uncertainties in the field. A practical method for dealing with them is to develop sensitivity analysis in the framework of data and probability statistics. Of existing non-structural components, cable tray systems are characterized by a number of uncertainties which may influence their bearing capacity drastically. In this research, the main characteristics of material, geometry, member layout along with the connection stiffness in cable tray are considered as random variables using global sensitivity analysis, with their results relative importance of these potential uncertainties on the seismic performance of cable tray. The sensitivity analysis method developed especially for cable tray under seismic excitation is constructed based on modal analysis and equivalent inertia force method combined with the Latin hypercube sampling method. The final results demonstrate the need to consider the effects of random variables in modeling assumption in seismic performance analyses of cable tray and can be further used in optimization design.

Over the past decade, prefabricated construction has increasingly gained popularity in addressing environmental concerns while meeting the high construction demand, particularly in developing countries. Accordingly, the concept of stakeholder engagement increasingly garnered attention as it is highly relevant for understanding and explaining the relationships among various stakeholders, like contractors, suppliers, and producers, especially within the fragmented context of prefabrication. To further the field, this study aims to provide a comprehensive qualitative and quantitative analysis for stakeholder engagement in the realm of prefabricated construction via a value-based network. This value-based network analysis is utilized to model multiple relationships between stakeholders as value exchanges, thus enabling to engage them though management with a focus. Specifically, A total of 110 values were identified and analyzed among 12 stakeholders throughout the whole lifecycle via an extensive literature review, interviews, and questionnaires directed at prefabrication practitioners in China. This data collection process garnered 194 valid responses with a 64.7% response rate. The findings show that enhancing stakeholder engagement requires improvements in both the abilities and experiences of developers, and in the collaborative relationships among key stakeholders. These improvements can be achieved by prioritizing the engagement of prefabrication consultants and encouraging the government to issue policies that support developers, producers, and consultants in fostering collaboration. This in-depth network analysis offers practical insights for decision-makers in the management of core stakeholders and value-based relationships, thereby improving stakeholder engagement. Additionally, it expands the current body of knowledge on stakeholder engagement by incorporating the value-based network analysis into the exploration of its stakeholders and relationships.

As one of the concrete admixtures, water reducing admixtures and superplasticizers are usually used to reduce the mixing water volume and improve the performance of the harden concrete while maintaining better workability of the fresh concrete. However, the concrete strength and durability properties are affected differently by different types and dosages of the water reducing admixtures and superplasticizers. Based on the published literatures, this paper comprehensively reviews and analyzes this problem. Different types of the concretes, including ordinary Portland cement concrete, ordinary Portland cement concrete containing pozzolan, fly ash and ground granulated blast furnace slag, calcium sulfoaluminate cement concrete, ferrite aluminate cement concrete, recycled aggregates concrete, lightweight aggregate concrete, self-compacting concrete and ultra-high performance concrete, are considered to discuss the influence of types and dosages of the water reducing admixtures and superplasticizers on their strengths. Water absorption, frost resistance and permeability resistance of the concrete are mainly reviewed to discuss this influence on the durability properties of the concrete. Then, some suggestions on the application of the water reducing admixtures and superplasticizers in reinforced concrete structures and projects are proposed.

The seismic response of the large-space underground structure (LSUS) is significantly influenced by the physical properties of the surrounding soil media, while the soil owns a strong spatial variability. This study proposes a seismic response analysis process of the soil-LSUS interaction system is proposed, which can consider the characteristic of the spatially distributed soil properties. The proposed process begins with establishing the spatially random field model of the soil properties using the improved latent space method. Then, the model is calibrated based on the real data and Bayesian approach, and the realization of the random field is accomplished. Further, the soil-LSUS interaction finite element (FE) model is established, which incorporating the soil physical properties generated from the random field. Finally, the nonlinear time-history analysis of the soil-LSUS interaction FE model is conducted. As an illustration of the proposed process, a typical LSUS located in Guangzhou is selected as an example, and the seismic mitigation measure which the lead-filled steel tube damper (LFSTD) is installed between the intermediate column and the top beam is adopted for the LSUS. The influence of the spatial variability of soil properties on the seismic mitigation effect of the LSUS is investigated. Results indicate that the spatial variability of the soil properties can cause a minor influence on the force and deformation of the intermediate column and the energy dissipation ratio between the LFSTD and structure, while it can bring a significant influence on the maximum deformation and force and the shape of the hysteresis loop of the LFSTD.

Although the Internet of Things (IoT) has aroused much interest as a potential approach for improving various construction activities, the extent of its adoption remains limited. The multiple barriers that prevent the wider adoption of IoT in the construction industry need detailed investigation. However, limited research has attempted to understand the barriers to IoT adoption. Therefore, this study aims to identify the critical barriers to IoT adoption in the construction industry and explore the prioritization and hierarchical structure of the barriers factors. Data were collected from relevant literature and feedback from Chinese industry experts, sixteen barriers against IoT adoption were identified and categorized based on the TOE framework assessed in the construction industry. An integrated interpretation structure model and decision-making and trial evaluation laboratory (ISM-DEMATEL) approach is adopted to analyze the interdependence between identified constructs and their intensities. In addition, the identified constructs are also clustered into a suitable group using MICMAC analysis. Results show that inadequate infrastructure, lack of governance, and top management support are the fundamental barrier against IoT adoption. By revealing the mutual relationships and interlinking of barriers, this study will help researchers and practitioners in the construction industry to focus on strategic efforts to overcome these obstacles to effective IoT implementation. This research revealed the barriers to IoT implementation in the Chinese construction industry. Also, it provides methodological tool references for exploring the impact factor of other similar innovative technology applied in this industry.

Real-world optimization problems are ubiquitous across scientific domains, and many engineering challenges can be reimagined as optimization problems with relative ease. Consequently, researchers have focused on developing optimizers to tackle these challenges. The Snake Optimizer (SO) is an effective tool for solving complex optimization problems, drawing inspiration from snake patterns. However, the original SO requires the specification of six specific parameters to operate efficiently. In response to this, enhanced snake optimizers, namely ESO1 and ESO2, were developed in this study. In contrast to the original SO, ESO1 and ESO2 rely on a single set of parameters determined through sensitivity analysis when solving mathematical functions. This streamlined approach simplifies the application of ESOs for users dealing with optimization problems. ESO1 employs a logistic map to initialize populations, while ESO2 further refines ESO1 by integrating a Lévy flight to simulate snake movements during food searches. These enhanced optimizers were compared against the standard SO and 12 other established optimization methods to assess their performance. ESO1 significantly outperforms other algorithms in 15, 16, 13, 15, 21, 16, 24, 16, 19, 18, 13, 15, and 22 out of 24 mathematical functions. Similarly, ESO2 outperforms them in 16, 17, 18, 22, 23, 23, 24, 20, 19, 20, 17, 22, and 23 functions. Moreover, ESO1 and ESO2 were applied to solve complex structural optimization problems, where they outperformed existing methods. Notably, ESO2 generated solutions that were, on average, 1.16%, 0.70%, 2.34%, 3.68%, and 6.71% lighter than those produced by SO, and 0.79%, 0.54%, 1.28%, 1.70%, and 1.60% lighter than those of ESO1 for respective problems. This study pioneers the mathematical evaluation of ESOs and their integration with the finite element method for structural weight design optimization, establishing ESO2 as an effective tool for solving engineering problems.

Probabilistic analysis based on random field (RF) has been widely adopted in the safety assessment of shield tunnels. However, its practical applicability has been limited by the intricacy involved with integrating geotechnical data and tunneling information. This paper addresses the following research question: How can the RF-based probabilistic safety assessment be carried out efficiently? In addressing this research question, we suggested an RF-based tunneling information modeling (TIM) framework to realize the probabilistic safety assessment of shield tunnels. In the proposed framework, the modeling of tunnel structure and geological conditions is initially introduced. The numerical safety assessment model is then created via an automated procedure using the RF-based TIM. A case study is conducted to verify the suggested framework, and results demonstrate that the framework can offer an automated design-to-analysis solution to improving the safety assessment of shield tunnels by comprehensively considering the uncertainties of geological conditions.

Unexpected damages or failures of steel pipes in refineries cause significant disruption to economic activity. While research has been conducted on the prevention of damage to steel pipes, no systematic methods or practical techniques for monitoring of vibrations to estimate the state of pipeline system have been reported. In this study, vibration safety evaluation model consisting of design – evaluation – control steps was developed to measure and control the vibration level during operation of the piping system of an oil refinery. The measurement location was designed by examining the structure of the pipe, and the vibration level measured at each location was compared with the allowable vibration level. Subsequently, two types of vibration reduction measures, namely, dynamic absorbers and viscous dampers, were introduced to reduce the vibration level. The effect of the application of the monitoring system was evaluated by comparing the vibration levels of the steel pipes before and after the application of the dynamic absorbers and viscous dampers. The vibrations of steel pipes in the oil refinery during operation decreased by over 50%. Upon applying the dynamic absorbers and viscous dampers, the responses of the frequency component also exhibited local and global reductions of approximately 50–80%.

The authors studied the influence of metro loads on the destruction of nearby buildings and construction structures with the help of BIM technologies in order to eliminate the human factor at the design stage. In the study, numerical modeling was carried out using the LIRA-SAPR software package, and dynamic loadings were set by the time integration technique. The suggested technique involved a nonlinear dynamic analysis conducted considering the time factor; the parameters of the stress-strain state (displacement, force, stress) were determined at each moment of exposure, changing the rigid characteristics of the building structures. The authors conducted a structural assessment of an unfinished construction facility, considering the vibrodynamic loads of the metro. Numerous models were adopted as the structural designs of buildings that consider various impact factors, such as nonlinear soil behavior and permanent action and the nature of dynamic loads. The comparison with experimental data confirmed the theoretical and computational parts of the developed technique. The study determined the vibrodynamic impact of the metro on the construction structures. Verification of the developed methodology based on BIM technologies was carried out by comparing the results of numerical experiments with the results of subsequent full-scale vibration tests.

Cellular lightweight concrete (CLC), also known as foamed concrete, has been extensively used in construction for decades. Foamed concrete’s properties include low density, excellent thermal conductivity, great workability, and selfcompaction; these features enable foamed concrete to be utilized in various contexts. However, the excessive use of conventional materials in concrete production harms the environment. Therefore, using agro-waste as a material to construct ecologically sustainable structures has numerous practical and financial benefits. Palm oil clinker (POC) is a waste product resulting from solid waste combustion during palm oil extraction. This research focused on the properties of foamed concrete with POC at 0%, 25%, 50%, 75%, and 100% as the fine aggregate replacement to develop lightweight foamed concrete (LFC) with a density of 1300 kg/m3. Besides, the potential of POC powder (POCP) and thermally activated POCP (TPOCP) at 0%, 10%, 20%, and 30% as cement replacements was examined. The development of compressive strength during a 90-day curing period was investigated. In addition, tensile and flexural strengths were assessed and reported, and the elastic modulus of the LFC was discussed. The transport properties of water absorption, porosity, and sorptivity were also investigated. The durability of concrete derivatives can exhibit the product’s resistance to chemical attacks and environmental conditions. After 75 days of immersion in hydrochloric acid and magnesium sulfate, the chemical resistivity of the produced LFC was determined by measuring the loss in weight and compressive strength. In addition, the effects of elevated temperatures on the LFC were determined by analyzing the mass loss and compressive strength degradation of specimens exposed to temperatures ranging from 200 to 800 °C. The test results demonstrated that the complete replacement of sand with POC enhanced the compressive strength of LFC by more than 50%. Similarly, POC-based LFC had higher flexural and tensile strengths than normal LFC. Besides, substituting 20% of cement with TPOCP could improve the strength of LFC by 23% during the initial curing days. Utilizing the optimal proportions of POC and POCP could enhance the residual strengths of LFC. Therefore, POC has the potential to be utilized as a fine aggregate and cementitious material to produce sustainable concrete.

Construction business involves people from different employers who have to work together in one project, thus a project manager needs to apply scientific principles to understand and to utilize those common psychological phenomena such as Hedgehog Effect, Butterfly Effect, Pygmalion Effect, Boiled Frog Syndrome, Parkinson’s Law, and Bandwagon Effect in completing work well and keeping people satisfied. The aim of this study is to develop a fuzzy mapping to assist project managers in implementing significant psychological phenomena in construction management through reflections on common psychological phenomena in the construction management. Through a structured interview survey among construction managers, the inferential association among gender, working experience, and the six psychological phenomena were plotted based on the Partial Least Square Structural Equation Modelling. Through the pairwise comparison technique, a fuzzy mapping of psychological phenomena in time, cost, and quality management was developed to facilitate the managerial efficiency in construction.

Traditional engineering risk management has been unable to adapt to the complexity and variability due to its constituent elements and dynamic nature of internal and external environments. Vulnerability, as a concept closely related to risk, has been neglected in the traditional risk management due to its hidden characteristics. This study attempts to quantify and evaluate vulnerabilities of complex engineering projects independently and explore the transmission mechanism between risk and vulnerability factors. Twenty different types of large-scale engineering projects in China were selected as case studies from the Mega Project Case Study Center (MPCSC) of Tongji University. Vulnerability and risk factors of each project were identified and analysed. A mechanism model was developed to explore the impacts of vulnerabilities and risks through ta Fuzzy Petri Net. Four main vulnerability-risk critical paths were identified through the reverse labelling method. The overall evaluation of engineering project risks considering the impacts of vulnerabilities is the highlight of this paper. This study interprets the cognition and evaluation of complex engineering risks from a new perspective, enriches the connotation of engineering risk management, and provides a reference for risk management and decisionmaking of complex engineering projects.

The Failure Mode and Effects Analysis (FMEA) is paramount for analytical skills of reliability design in dynamic prevention. The FMEA model is a significant method which can simultaneously reduce the operating errors or delays as well as improve the construction quality. In particular, the Risk Priority Number (RPN) in the FMEA model is a vital tool which helps construction managers prioritize problem-solving. As the Internet of Things and big data analytical skills have become progressively widespread and mature, among the three risk indicators of RPN, the number of operating errors or delays per unit time can be estimated by the data collected from the analysis of statistical methods and regarded as the basis of 10-level classification. In addition, when the loss is larger, then the severity is higher. This paper proposed three evaluation criteria, including Occurrence, Severity, and Detection of RPN in construction engineering, and a 10-level classification model. To assist the construction managers, priority for construction improvement can be identified based on RPN calculations.