Journal of Construction Engineering and Management

The development of cooperative relationships among project participants makes relationship norms an important governance mechanism to promote cooperative behavior. However, the research on relationship norms mostly ignored the project network environment established by project participants. A static project network cannot accurately reflect the actual project network state from the perspective of network embeddedness, but the research on the mechanism of cooperative behavior from the perspective of dynamic network embeddedness is limited. Therefore, this study overcame the limitation of static analysis. This paper divided the implementation process of construction projects into early, middle, and late phases. The theoretical model of network embeddedness, relationship norms, and cooperative behavior was tested using sample data from 716 surveys regarding the three phases from China to determine the mechanism of the effect of network embeddedness on cooperative behavior. The research results show that network embeddedness (including structural embeddedness and relational embeddedness) has a direct impact on cooperative behavior. The results also indicate that network embeddedness has an indirect impact on cooperative behavior through relationship norms. Additionally, the results further indicate that the direct and indirect effects of network embeddedness on cooperative behavior are different in different phases of construction projects. With the advance of construction projects, the influence of relational embeddedness increases, whereas the effect of structural embeddedness decreases. The research results deepen the understanding of relationship norms in construction projects and make up for the lack of research on cooperative behavior in construction projects from the perspective of dynamic networks. The research results also provide enlightenment for project participants to fully utilize the network relationships in different phases of construction projects, stimulate the establishment of relationship norms, and promote cooperation. This is helpful to improve the cooperative construction management mode.

Due to the complexity of infrastructure megaprojects, using joint venture contractors (JVCs) is an effective method to deliver projects. However, this process is constrained by several factors, including the quality of competing JVCs and knowledge level of decision-makers (DMs). However, existing decision-making methods for megaprojects and JVC selection lack comprehensive JVC evaluation criteria and cannot handle decision-making scenarios using incomplete data. To address these issues, in this study, we propose a novel model based on a hesitant fuzzy soft set and a linear programming techniques for multidimensional analysis of preferences called LINMAP, which can process incomplete decision data and improve decision accuracy. Moreover, a set of criteria was constructed from four dimensions (economy, society, environment, and cooperation), which upholds the sustainable development and internal cooperation of the built environment. Finally, by applying the approach to a complex infrastructure megaproject and conducting a sensitivity analysis, the feasibility and effectiveness of the proposed model were verified. Research shows that DMs in different specialized areas choose different criterion subsets, which leads to different decision-making results. For one, allowing DMs to choose a subset of the criteria can improve the accuracy of a decision and reduce the accompanying risks; further, a large gap exists between partial and global evaluations, so partial and global consistence must be included in the context of multicriteria decision-making; finally, cooperation is the premise of competition, i.e., good cooperation has a positive effect on competition, so the sustainable development of a JVC needs to consider not only economic, environmental, and social factors but also cooperation. This study contributes to the knowledge domain of multicriteria decision-making, extends the knowledge domain in terms of clarifying complicated evaluation scenarios in infrastructure megaprojects, and renders practical managerial implications for future complex evaluation processes in infrastructure megaprojects.

Abstract

This study proposes a novel approach to monitor the fatigue levels of construction rebar benders by measuring chemical biomarkers using sweat sensors. Fatigue resulting from dehydration and energy depletion can severely endanger the safety and health of construction workers. Sodium, lactate, glucose, and sweat rate were chosen as detectable biomarkers in this study, as their concentrations can indicate hydration status, energy consumption, and electrolyte balance, making them suitable for fatigue monitoring. The results were used to construct a fatigue model using supervised machine learning approaches. Construction rebar experiments were conducted while the sweat-based biosensors were applied to rebar workers to evaluate their fatigue with five different classifiers, demonstrating accuracy rates ranging from 71.43% to 96.43%. The results suggested that sweat-based biomarkers offer a noninvasive and accessible fatigue monitoring alternative. This can potentially help alleviate fatigue-related adverse ill effects like dehydration or cramping by enabling instant fluid or nutrient supply recommendations during construction manual tasks. It also provides valuable insights into the physiological effects of rebar work. Besides, this study presents a valuable model for predicting workers’ fatigue levels, which could be applied in the construction industry to improve workers’ safety and productivity. Furthermore, the study highlights the importance of maintaining appropriate hydration, nutrition, and electrolyte balance during physically demanding tasks like construction manual work.

Abstract

The study demonstrates that sweat biomarkers, including sweat rate, sodium, lactate, and glucose, can be utilized to assess fatigue among construction rebar workers. Sweat biosensors offer advantages of small size and non-invasiveness, making them suitable for a wide range of scenarios in both the construction industry and sports fields. Moreover, sweat rate and sodium levels can indicate hydration status, and their measurements can be used to recommend immediate fluid intake. Also, lactate and glucose are essential resources that sustain the body, and their measurements can suggest appropriate nutritional intake. These instant recommendations can alleviate the adverse effects of fatigue, consequently reducing fatigue levels. Thus, it is a promising methodology for enhancing the health and safety of the construction industry. In addition, the results of the study will be shared with construction companies, allowing them to introspect fatigue development and chalk out further interventions and policies to manage fatigue effectively.

A railway station subgrade (RSS) is a complex earth structure for supporting various railway facilities and its building information modeling (BIM) is an essential task in a railway station project. During the entire life cycle of an RSS BIM model, it will be frequently updated according to the progress of the project. However, the current way of updating an RSS model is commonly conducted in a complete rebuild, which is very inefficient and time-consuming. To address this issue, we propose a novel approach for realizing a partial BIM update driven by model changes, through which a quick update for an existing RSS BIM model can be achieved. A partial updating strategy that is triggered by a model change is developed to locate a minimum influenced domain of the original model and update it in that domain. An actual railway station case is used for demonstrating the validity of the approach, through which a quick partial update to an existing RSS model is achieved by skipping the redundant computations in the process of a complete rebuild.

Automatically converting three-dimensional (3D) point clouds into building information modeling (BIM) has been an active research area over the past few years. However, existing solutions in the literature have been suffering the limitations of covering all different design scenarios (prior knowledge-based approach) or collecting sufficient point clouds as training data sets (3D deep learning–based approach). To tackle this issue, we propose a fused system to automatically develop as-built BIMs from photogrammetric point clouds. A series of images is captured to generate a high-quality point cloud, which is then preprocessed by removing noise and downsizing points. Meanwhile, a two-dimensional (2D) deep-learning method, DeepLab, is utilized to semantically segment elements (e.g., walls, slabs, and columns) from the collected images. Subsequently, an inverse photogrammetric pipeline is employed to recognize element categories in the point cloud by projecting the isolated 3D planes into 2D images and assigning the identified elements to the 3D planes. Finally, the industry foundation classes are devised to create as-built BIMs based on the segmented point clouds. In order to evaluate the performance of the proposed system, we selected six cases with various elements as the testbed. The prospective results reveal that (1) our system can provide a highly automated solution to develop as-built BIMs; and (2) 39 out of 45 elements in six different cases are successfully recognized in point clouds.

Prefabricated buildings (PBs) have been widely perceived as environmentally friendly buildings and advocated globally. In many countries, the promotion rate of PBs remains slow, hindered by various barriers. With the change of external development environment under different periods, there is also the temporal dynamic evolution in barriers. Many studies have adopted some traditional methods to explore these barriers, but only focused on the research-conducting periods and ignored the changes of obstacles in different periods, which is the so-called dynamic nature of barriers. This would lead to the noncorrespondence between the previously studied barriers and the new situation of different periods. In this regard, this study aims to explore barriers dynamically and efficiently. Taking the online discussions of Chinese construction practitioners (CPs) as the sample, text mining techniques were performed to identify the barriers at different stages in the period from 2016 to 2021. Then, with a proposed sentiment scoring model, CPs’ sentiment and attention level were combined to indirectly evaluate the impact degree of the corresponding barriers. In the end, the findings were verified in semistructured interviews. As a result, the changeable sentiments of CPs were tracked in the timeline, the evolution of key barriers at different stages and the internal driving force were revealed, and some new findings of barriers were identified. This paper not only makes a great theoretical contribution for PB studies, but also provides a research framework worthy of reference for researchers or government to regularly explore problems in the PB industry or other industries.

Building information modeling (BIM) is a pivotal technology to realizing the digital transformation of the construction industry. Lack of BIM professionals, however, is one of the reasons the application of BIM technology in the construction industry is hindered. Identifying BIM competency requirements is critical for BIM professionals’ training. This paper uses the structural topic model (STM) to mine the topics of BIM recruitment information to deeply understand the BIM competency requirements from a 360° view of the construction industry. The company size, salary level, year of experience, and education in BIM recruitment information are taken as covariates to examine their impact on BIM recruitment topic prevalence. And the changing trend of the topic prevalence and topic correlations are observed through visual analysis. The results reveal that the current BIM competency requirements in the construction industry contain three aspects: management competencies, professional and technical competencies, and personal characteristics. In particular, the requirements for BIM application, construction drawing design, and information technology (IT) skills are relatively strong, and personnel professionalism is also a concern of BIM job recruitment. Companies of different sizes have evident preferences for competencies. Salary levels and years of experience requirements also affect the intensity of corporate demand for BIM competencies. However, education is not the main factor affecting the recruitment of BIM positions. The results can provide a reliable theoretical basis for educational institutions to build a proper BIM professional course system, for companies to develop BIM job recruitment plans, and for individuals to choose their employment goals.

Abstract

Workplace safety remains a concern in the construction industry as fatality rates continue to rise. While hazard recognition training programs have been implemented using multimedia-based modules, their effects have not been broadly reflected on construction sites. In an effort to provide realistic and engaging training, a recent focus on virtual reality (VR) for an immersive learning experience has been shown to offer benefits to improve traditional lecture-based training. Such virtual environments can be especially useful for simulating hazard recognition tasks that are inaccessible in real-life settings due to the potential dangers they pose for trainees. However, due to the focus on applications for performance assessment and procedural training, strategic elicitation of emotional arousal, which has been shown to be a precursor to desired learning outcomes in hazard recognition training, has not been explored for construction-specific VR applications. To guide the development of such VR environments that target emotional arousal for learning, this study used opinion mining to catalogue the features that yield or inhibit an emotional reaction in similar video simulations posted on a public video sharing platform (YouTube). Design insights such as the need to provide agency in the simulations, introducing nonplayer characters in the scene, and the like, are presented. Here the authors discuss specific implementation strategies derived from the study findings that developers can use to elicit emotional arousal in a construction-specific virtual environment.

Practical Applications

Virtual reality (VR) training programs are increasingly being used in the construction industry to improve hazard recognition and reduce fatalities. VR provides an immersive learning experience that can simulate tasks that would be dangerous for trainees to perform in real life. However, previous VR training programs did not focus on leveraging emotional arousal, which has been shown to be beneficial for learning outcomes. This study used natural language processing to analyze user comments and identify key features that lead to an emotional reaction in virtual simulations on YouTube, and provided insights and strategies for developers to create construction-specific VR environments that elicit emotional arousal.

By their nature, building refurbishment projects display unique characteristics, surfacing uncertainties, and complexity. These lead to unforeseen safety risks when compared to ‘new build’ projects. In recognition of this, resilience engineering (RE) provides a significant contribution to safety management in uncertain and complex work settings. Despite a recent trend in RE studies concerning construction, a model that explores the relationships between RE factors, specifically in the unique category of construction refurbishment, is yet to be produced. This research aims to examine the relationships between the RE factors (i.e., top management commitment, awareness, flexibility, and learning culture) that enable a resilient work environment in construction refurbishment projects. One hundred and twenty-six (126) responses were collected from participants involved in building refurbishment projects in Australia. The distinct nature of a four-factor RE model was confirmed using factor analysis. Structural equation modelling (SEM) identified the relationships among the RE factors. The results highlight that the action of top management toward safety was prominent and determined as a positive influence on the other RE factors. Learning culture also has a positive effect on flexibility and awareness and in addition, the effect of top-management commitment to awareness and flexibility is partially mediated by learning culture. Overall, the model shows the interplay between RE factors in attaining a resilient work environment that can anticipate, cope, and adapt to safety risks. The findings of this research provide guidance for practitioners and researchers in the design of safety interventions and can prevent prevailing differences in safety practices between head office and work site to achieve a resilient work environment in construction refurbishment work settings.

Abstract

Quality is a substantial pillar of construction success, as its failure poses a significant threat to the construction budget and schedule. Effective root cause (RC) analysis allows for the early identification of issues leading to quality failure and proactive defect-prevention measures. This study puts forward a flexible RC analysis method that extracts useful information from construction nonconformance reports (NCRs) to identify the future trend RCs of construction defects by employing a novel graph representation learning (GRL) approach called node2vec. Node2vec was used to connect high-cost impact RC information based on shared construction defects to determine the RCs of the construction defects. Compared with the conventional RC analysis in the literature (i.e., association rule mining), the proposed node2vec offers three advantages: (1) responsiveness to large itemset, allowing its application across multiple projects with different data collection systems. (2) It receives richer semantic information (defect-related features, RC connectivity, and different cost impacts), enabling a more comprehensive understanding of underlying defects. (3) Prediction ability of future connectivity RCs, resulting in more efficient defect-prevention actions. In contrast to unsupervised RC analysis approaches, the incorporated word2vec prediction model allows the measurement of the prediction performance of related RCs (73% accuracy and 2.31% loss), providing a noticeably more accountable RC analysis and holistic defect prevention. This in turn facilitates the integration of the proposed approach with decisions regarding quality improvement in construction projects, thereby accelerating targeted decisions and interventions within related defect-prevention policies.

Practical Applications

The proposed RC analysis approach works as a construction quality management (CQM) tool to improve the quality of construction activities and reduce delays, cost overruns, and client satisfaction by incorporating three elements: (1) a comprehensive RC network that factors in the direct and indirect relationships between RCs of defects. (2) A node2vec algorithm that can dynamically find the relationship between different RCs, which facilitates the development of more efficient defect-prevention strategies to prevent similar occurrences. (3) A cosine similarity that allows practitioners to prioritize RCs for specific construction activities, enabling more efficient utilization of resources in the quality delivery of construction activities. This improves the efficiency of the related strategies and data-oriented decisions. Overall, the developed RC analysis method can aid construction managers in improving the quality of construction activities. Although the proposed node2vec approach for improving CQM is not a universal quality solution, the continuous RC analysis of the collected NCRs facilitates error prevention in the long term. Constant identification, documentation, and prioritization of the RCs of construction defects allow construction managers to devise more effective CQM plans that can gradually address and eliminate underlying issues, thereby directing their actions toward achieving the zero-defect goal for each activity.

This paper focuses on the development of a statistical model that estimates the close proximity level (CPX-L) of bituminous mixtures usable in urban, extraurban, and motorway road networks as a function of survey speed, aggregate gradation, bulk properties, and age of the layer. The proposed equation derives from an existing model developed on Italian urban and extraurban roads whose coefficients were recalibrated through analysis of variance by using data from mixtures used in the Italian motorway network. The original equation was further refined by introducing the age of the layer as a new covariate to predict the durability of the acoustic performance. The age of the layer resulted in a higher statistical relevance than the cumulative traffic for predicting acoustic degradation. A unique aging coefficient of $\sim 0.03\pm 0.01\mathrm{dB}(\mathrm{A})/\mathrm{month}$ was calculated for dense, porous, and semiporous mixtures installed in similar Italian intermediate-mild climatic regions. This value predicts an increase in the tire-pavement noise of $\sim +0.4\mathrm{dB}(\mathrm{A})/\mathrm{year}$ and $\sim +2.2\mathrm{dB}(\mathrm{A})$ in 5 years, which is compatible with other research projects and European noise regulations. The effects of the mixture type were also investigated, providing a negligible variability of $+0.3\mathrm{dB}(\mathrm{A})/5\mathrm{years}$ if only porous and semiporous mixtures are considered. The use of the model is recommended between 50 and $90\mathrm{km}/\mathrm{h}$, where the current equation explicates almost 94% of the variability and the predicted values show an accuracy compatible with the precision of the CPX method. Future works can contribute to further improving the accuracy and the range of applicability of the model providing also more detailed insights into the acoustic aging of different mixtures.

Revenue risk is the most critical factor that hampers the long-term viability of highway infrastructure service projects. This paper develops an integrated multi-method decision framework to evaluate and compare multiple revenue risk mitigation strategies for build-operate-transfer (BOT) highway infrastructure concessions. Using the project data of a real highway BOT case project in India, this paper models and evaluates various risk mitigation strategies on different performance metrics using Monte Carlo simulation analysis. The technical efficiencies of the risk mitigation strategies are estimated using data envelopment analysis (DEA) and efficient strategies are identified. The findings suggest that strategies comprising appropriate combinations of minimum revenue guarantee, excess revenue share, and revenue shortfall loan mitigate the revenue risk most efficiently. The decision framework and the findings facilitate efficient public infrastructure contract design by policymakers and set multiple research directions.

It is critical for project owners to have a reasonable estimation of project duration before the letting date for contract time determination and project management purposes. To determine the project duration, highway agencies employ scheduling techniques and arrange activities in sequential order. Activity sequencing is a crucial task since a slight change in the sequence of critical activities can significantly influence project duration. Also, the task of activity arrangement is time-consuming for a broad portfolio of projects and requires skillful schedulers. To aid activity sequence determination, prior studies used project drawings, expert knowledge, and historical data to identify sequence rules, logic templates, and sequence prediction models. However, weaknesses and areas of improvement exist, including a lack of adequately leveraging available historical data, the necessity of human input, reliance on human experience rather than data, and poor detection of the overlapping time of activities. This study proposes a novel framework that predicts the sequences of work activities using historical daily work reports to train a long short-term memory recurrent neural network to predict the activity sequence and overlapping in future projects. The daily work reports of 720 highway projects obtained from a highway agency are used as the case study. A novel evaluation technique based on conditional probability is used to assess the model and compare its output sequence to sequences created randomly. The assessment results indicate that the model’s output is superior in 94.4% of situations, suggesting a high level of model reliability. The impact of key project characteristics such as project work type and size on activity prediction is examined, indicating a significant impact of project work type and no impact of project size on activity prediction. The results of this study can assist highway project owners in activity sequence and overlap determination by entering a series of activities and receiving the likely next successors.

When using lean waste management in construction project delivery, computational methodologies are currently an innovative technology for the implementation of efficient and effective improvement strategies in the development of Industry 4.0 in Chile. Lean models are able to manage data obtained from construction projects along with the data obtained from the knowledge base of professional experts (expert survey). The waste management of construction projects under the lean philosophy requires cooperative efforts, where the opinion of professional experts is completely paramount to analyze multidisciplinary knowledge. Therefore, new protocols and disruptive procedures based on artificial intelligence (AI) tools can help decision makers prioritize activities, minimize uncertainty, and avoid wasteful actions that add no value to the project and thus can be minimized or completely eliminated. The vagueness of subjective human judgment in the degree of application of lean waste management in project delivery is modeled by a fuzzy logic model that includes additional considerations related to the lean implementation. Moreover, multiple linear regression analysis has been implemented in order to verify and validate the previous digital fuzzy model. In this sense, the main aim of this study is to develop new approaches regarding AI systems, using fuzzy sets and multiple linear regression for managing waste in construction project delivery in the metropolitan area of Santiago, Chile. A theorized application of the models reveals that the sample (100 construction projects) can be classified into three lean waste condition levels: high, medium, or low waste effects. The outcomes of this research will contribute to the Chilean construction industry environment and will open new ways for harnessing AI-based technology in the construction industry to the fullest potential, to achieve better time and cost predictability with a client- and end-user-centered world view.

Human–machine collaboration is a promising approach to improve on-site hazard inspection because it can complement the inherent limitations of human cognitive functions. Nevertheless, research on the effective integration of opinions from humans and machines to form optimal group decision-making is lacking. Prior work suggests that a confidence-weighted voting strategy is superior, but self-reported decision confidence is often unreliable. Thus, this study proposes an innovative methodological framework to predict workers’ hazard response choices and decision confidence from brain activities captured by a wearable electroencephalogram (EEG) device. First, we developed a Bayesian inference–based algorithm to ascertain the decision threshold above which a hazard is reported characterized by the power of human brain activity. Furthermore, we describe hazard recognition as a process of probabilistic inference involving a decision uncertainty evaluation. Benchmarking against an optimal Bayesian observer, the optimal criteria to differentiate between low-, medium-, and high-confidence levels were obtained based on numerical simulations. The proposed method was tested empirically with a predesigned experiment in which 77 construction workers participated in a hazard recognition task while their EEG data were simultaneously collected. Cross-validating with behavioral indexes of the signal detection theory, the results confirmed the possibility of EEG measurement to observe workers’ internal representations when discriminating hazards. Parietal $\alpha$-band EEG power was chosen as a proxy for confidence-level evaluation prior to responses. Theoretically, this framework characterizes workers’ mental model when recognizing hazards. Practically, it enables the prediction of workers’ hazard responses and decision uncertainty, supporting the design of future hazard confirmation mechanisms in the collaborative human–machine systems research field.

With an increasing number of multinational enterprises (MNEs) participating in public-private partnership (PPP) projects worldwide, the liability of foreignness in project construction and management become a manifested research topic in both academia and practice. This study aims to explore the impact of MNE participation on the survival of PPP projects. A unified theoretical framework with internal governance and external institutional environment is elaborated, and a dataset of 4,336 projects in 121 developing countries is derived from the World Bank database to empirically verify our hypotheses. The findings suggest that MNE participation has a detrimental impact on PPP project survival. Additionally, such detrimental impact is mitigated by the two dimensions (regulatory quality, and rule of law) and intensified by two other dimensions (political stability and absence of violence, and control of corruption) of the institutional conditions in the host country. This study contributes to the body of knowledge in two ways: (1) it is one of the pioneer studies that shed light on the liability of foreignness in the context of PPP projects and enhances our understanding of the MNEs’ impacts on infrastructure projects; and (2) it enriches the stream of research on project success/failure by linking MNE participation to project survival and revealing the contingencies of host country’s institutional conditions in the complex global market.

The main challenges and barriers facing the effective implementation of risk management (RM) practices in the construction industry have increased the ceiling of challenges associated with the implementation of construction projects during the COVID-19 era. The aim of this paper is to assess the level of significance of risk management implementation challenges and barriers (RMICB) and the level of efficacy of construction RM practices during COVID-19 by considering the case of Iraq. A multistage research methodology was adopted, including semistructured interviews with construction experts to identify the main RMICB and RM practices indicators; surveys to assess the identified RMICB and RM practices indicators; analytical hierarchy process–based and fuzzy-based models to quantify RMICB level of significance, and RM practices level of efficacy. Based on the adopted methodology, the authors identified a total of 34 RMICB grouped under four categories, namely analytical approach–related, behavior-related, management-related, and team-related RMICB. Moreover, the assessment of the current construction RM practices during the COVID-19 era indicated a low level of RM practices efficacy. This study significantly contributes to the body of knowledge by helping construction practitioners and researchers to better understand the RMICB that are hindering effective implementation of RM strategies for construction projects during the COVID-19 era. Furthermore, it provides valuable information to decision-makers regarding the current construction RM practices efficacy during extreme conditions.

Ambivalence brings great uncertainty to workers’ decision making of intentional unsafe behaviors. It is of great theoretical and practical significance to find the factors that have significant regulating effect on workers’ ambivalence. [Research Content] On the basis of constructing the decision-making model of workers’ intentional unsafe behaviors under the ambivalence of workers, this paper analyzed the moderating effect of optimism bias on the ambivalence of workers, and employed the structural equation model to empirically verify the proposed hypothesis. [Empirical results] Individual optimism bias has a significant moderating effect on the ambivalence in the decision-making process of workers’ behavior. Workers with higher optimism bias are more likely to have intentional unsafe behaviors. At the same time, the generation of optimism bias is related to workers’ self-esteem and risk preference. [Enlightenment] Managers should pay more attention to workers’ optimism bias psychology, and in particular, psychological counseling should be conducted regularly for workers engaged in high-risk work for a long time to help workers establish a correct view of risk.

Augmented reality (AR) has become one of the most pervasive technologies for immersive applications with potential to improve worker performance in construction. However, the cognitive impact of using AR is still being studied and a comprehensive assessment of changes on user’s cognitive status is needed. This study proposed an electroencephalogram (EEG)-based mental workload evaluation method for a wood frame assembly, comparing the differences between two information display types (conformal model and paper layout). Additionally, a task performance analysis was conducted for completion rate and accuracy. The experiment recruited thirty construction students with similar backgrounds who were novice AR users. The findings indicated that AR improved the assembling efficiency with significantly higher completion ($p<0.001$) in comparison to the paper display. Furthermore, both mean and maximum values during the task process were analyzed to compare the difference between the displays, where AR display generated significantly less mental work in mean ($p=0.007$) and maximal ($p=0.030$) values. The comparison of the changing trends of mental workload increment in assembly revealed that AR display maintained lower mental workload and smaller fluctuation (standard deviation) during the entire assembly. The outcomes examined the usability of AR head-mounted display (HMD) in the construction assembly task with a moment-to-moment physiological evaluation of user’s mental workload. This work shows that EEG-based mental workload evaluation can be used as an effective method for revealing changes in workers’ mental status in real time during assembly, which has implications for future research in areas such as hazard recognition and situational awareness evaluation.

Green Public Procurement (GPP) is an alternative for public agencies to purchase goods and services with a reduced environmental impact. GPP encourages the integration of environmental criteria in the procurement procedure to achieve goals related to climate change, resource use, and sustainable consumption and production. The inclusion of environmental criteria in public procurement of civil infrastructure has been claimed as an essential step to enhance sustainability in construction. However, there is limited knowledge of how environmental criteria currently are included in public procurement. This study analyzed the inclusion of environmental criteria in public works procurement. Three research objectives were established: (1) identify the environmental criteria included in public-works procurement of civil engineering projects, (2) analyze how these criteria are included in the procurement processes, and (3) identify the characteristics of projects main factors driving in the inclusion of these environmental criteria. This study analyzed 343 tendering documents from 11 different countries at the international level. Tendering documents were analyzed using quantitative content analysis, descriptive statistics, nonparametric techniques, and logistic regressions. Within the 343 tenders analyzed, the study identified 4,500 instances in which environmental indicators were included in the procurement processes. These instances can be classified into 9 criteria (emissions, energy, environmental management, flora and fauna preservation, landscaping, materials, noise and vibration, waste, and water) and 22 subcriteria. These criteria were analyzed considering their inclusion within the procurement process, in general, and in the different stages of the procurement process, in particular: selection criteria, award criteria, and technical specifications and contract performance clauses. The results show that although more than 90% of the analyzed tenders included environmental criteria, the environmental management criterion was the only one included as selection criteria and award criteria. Environmental criteria tend to be included as technical specifications and contract performance clauses. Their inclusion depends on the type of civil infrastructure, the size of the contract, and the type of construction work. Nevertheless, the project delivery and procurement methods do not influence the inclusion of environmental criteria. This study helps procurement professionals and researchers implement GPP by providing guidance on how to incorporate environmental criteria in the procurement process based on the type of infrastructure and characteristics of the project. This action is necessary for governments interested in improving their performance toward the Sustainable Development Goals.

Public–private partnership (PPP) project risk management through risk-factor attention has been widely studied for a long time; however, it remains unknown whether the attention degree of risk indicators corresponds to their loss degrees. This study investigated the difference between risk-factor attention and losses in PPP projects by utilizing text mining, questionnaire survey, fuzzy comprehensive evaluation, and importance-performance analysis methods (IPA). The results show a surprising attention-loss phenomenon: some factors attract more attention but bring fewer losses; other factors attract less attention while causing more losses. The research findings provided valuable references not only for PPP project managers but also for further studies on the risk management of PPP projects.

This article seeks to improve the understanding of applying case studies in qualitative research in construction culture. Being a soft and method-complex research strategy, the case study approach is challenging to implement in practice. This issue is exacerbated when the case study approach is incorporated with the inherited complexity of the construction culture. Thus, when studying construction culture through a case study approach, it is critical to establish concise guidelines that can be referred to and followed. The proposed methodology is described in detail, from conceptualizing the research problem to reporting the findings, and is illustrated with an example application to a building information modeling (BIM)-enabled construction organization. The ensuing discourse elucidates upon how the philosophical and methodological issues can be addressed by systematically designing the case study to secure optimal research outcomes. From the study, it was found that beyond the scope of typical case studies, a cultural case study must establish its cultural philosophical position, whether it sees culture as a variable or a root metaphor. Given the necessity of indirect questioning in culture research, the data collection protocols should be developed to allow for a more in-depth exploration of the core construction culture. Consequently, questions about difficult and critical situations are essential for eliciting appropriate responses from interviewees and making the process more effective. Furthermore, asking interviewees for examples to back up their answers adds justifications and credibility to their responses. The paper contributes to the existing body of knowledge by proposing a systematic approach to identify hidden cultural beliefs, and by demonstrating the process of developing coding levels using NVivo software. The proposed methodology can be applied to other cultural domains such as safety and lean and can help organizations to identify the underlying cultural beliefs that may impact their performance and develop targeted interventions. The guidelines help novice researchers identify critical steps in designing qualitative case studies in cultural research, and future research could deductively and quantitatively test the theories developed in this paper to confirm validity.

Abstract

Building information modeling (BIM) involves the use of collaborative digital mock-ups of an asset to streamline design, building, and operation processes. Collaborative work and the use of an integrated digital mock-up offers many advantages but raises several problems regarding the liability of stakeholders in construction projects. Practitioners involved in the design process of a building (engineers and architects) practice very high-liability professions for which the use of a digital mock-up implies potentially high stakes. Although liability issues have been identified in the literature as a hindrance to BIM implementation, practitioners’ concerns toward their liability have only barely been investigated. In this paper, we propose to explore engineers’ concerns about their liability toward using BIM collaborative digital mock-ups with a case study in civil engineering. We documented these concerns through an exploratory study consisting of semi-structured interviews. The main contribution of the paper is therefore an organized list of concerns. These include: the alignment between their way of working and professional rules, the clarity of the assignment of liabilities, and the reliability of the digital mock-up. These stem from a liability risk that practitioners perceive because of uncertainty about liability allocation and uncertainty regarding the reliability of digital mock-ups. Our research work is part of an overall effort to understand the problems faced by practitioners when implementing new practices associated with BIM and to provide solutions. The results are therefore extensively discussed in order to identify hypotheses and avenues of work to address the identified concerns. The specific context (engineers, in Quebec) and the exploratory nature of the study implies that the results are not generalizable to a wider population. However, the identified concerns may be likely to emerge in similar context like high-liability professions involved in design stages of BIM projects. This paper is a very first step toward identifying these concerns in the construction sector and must be subject to future work.

Practical Applications

Our research work is part of an overall effort to understand the problems faced by practitioners when implementing new practices associated with the use of digital mock-ups and to provide solutions. Building information modeling (BIM) and product lifecycle management (PLM) involve the use of collaborative digital mock-ups of an asset to streamline design, building, and operation processes. They offer many advantages but raise several problems regarding the liability of stakeholders in construction projects. Practitioners involved in the design process of an infrastructure practice very high-liability professions for which the use of a digital mock-up implies potentially high stakes. In this paper, we propose to explore and document engineers’ concerns about their liability toward using and signing BIM-PLM collaborative digital mock-ups with a case study in civil engineering. Our results show that engineers are concerned about the alignment between their way of working and professional rules, the clarity of the assignment of liabilities, and the reliability of the digital mock-up. These identified concerns are extensively discussed in order to provide hypotheses and avenues of work to overcome them, including technical solutions, organizational solutions, and legal solutions.

Abstract

Offsite construction, also known as industrialized/prefabricated construction, is an alternative approach to delivering construction projects, compared to the on-site stick-built/built-on-site construction approach. Today’s offsite construction processes often use technology systems (e.g., digital design tools and automated machinery) to increase productivity and improve product quality. These systems operate collectively and rely on information generated in different operating environments. Therefore, information interoperability is critical to achieving overall construction efficiency and economics. This creates a need to study the impacts of information loss and transfer due to information interoperability, specifically for offsite construction processes. Given this, the paper uses a qualitative (case study) approach to document the offsite construction processes and the information requirements for each process. The efforts spent on information generation and transfer are taken as inputs for calculating the information loss and transfer impact using a quantitative (Monte Carlo simulation) approach. It contributes to the body of knowledge with (1) documentation of the current offsite construction processes based on wood panelized construction and the information requirements for all involved technology systems; (2) a case study approach that can be generically applied in other offsite construction companies to capture the information efficiency due to information interoperability; and (3) a generic simulation-based approach to measure the impacts of information loss and transfer between processes. As a result, the paper proposes phases of technology adoption and strategies for offsite construction companies.

Practical Applications

This research proposes some practical implications specifically for the offsite construction industry that intends to adopt new technology systems and/or evaluate the efficiency of information handling in their existing practice, through a case study. For example, offsite construction firms can develop their own evaluation metrics to assess the information loss and transfer before investing in new technology systems, by following a similar approach to this case study. This may include comparing different technology systems from the perspective of return on investment. The approach can also be used to evaluate existing technology systems and set a baseline/benchmark. This will help management to understand the potential losses in effort due to poor information interoperability and set targets for enhancing these systems. Lastly, the proposed phases of technology adoption and strategies can help the offsite construction industry to set targets for progressive technology transformation and serve as a reference for setting expectations from a business perspective. It also helps the company to identify the required talents and know-how in the technology transformation process.