Journal of Construction Engineering and Management

Prefabrication and modularization have attracted much attention in building construction, and they are becoming increasingly important for the betterment of society. To fully benefit from prefabricated and modular construction, the application of artificial intelligence and robotics (AIR) is widely recognized as essential, but it has not yet been systematically studied. This paper aims to explore future research directions on AIR for prefabricated and modular construction through a systematic literature review drawing on a concept–methodology–value philosophical framework. The analysis involves 97 published journal articles carefully identified through the Web of Science and Scopus databases. The review specifically addresses four research questions aligned with the framework to synthesize previous research activities, and the paper proposes five directions that depict future research and practices: integrated AIR for large-scale modularization, multi-dimensional project management, intelligent postconstruction management, interdisciplinarity and interoperability, and moving beyond technical solutions. The findings and the philosophical framework should benefit succeeding exploration and practice in the field.

In the past four decades, remote-sensing data and methodologies have been increasingly used in many fields to improve the efficiency, accuracy, or safety of data collection, and construction management is no exception. In recent years, unoccupied airborne systems (UAS), commonly referred to as drones, have emerged as an important platform for the collection of remote-sensing data. When coupled with increasingly affordable sensors and automated image processing techniques, UAS have made the application of remote-sensing approaches to myriad construction and civil engineering problems practical and attractive to the point that UAS-based airborne imaging is becoming routine for many construction management tasks. Based on our review of literature, a comprehensive discussion on the use of UAS methodologies for collecting and analyzing data to assist with construction research is much needed. This paper was developed to discuss current best practices in UAS aerial imagery collection and processing for construction research, including defining key terms. In addition, this paper discusses a variety of methods for the analysis of UAS collected aerial imagery for the following tasks: preconstruction planning, material tracking, project progress tracking, safety, as-built documentation, and building/structure inspection. This paper also includes a perspective on the future of UAS for a variety of construction management tasks. This work contributes to the Data Management and Data Science Body of Knowledge by informing construction researchers of the state of the art of UAS data collection and analysis methodologies. This study also provides practitioners with a comprehensive guide to the use of UAS for onsite construction management tasks, which is an essential component of e-Construction.

Transportation assets represent a critical element of public infrastructures, and bridge networks are an essential part of these assets. A system that includes several tools for managing the bridges is called a bridge management system (BMS). BMSs are built up to provide long-term health verification for bridges and assure safe serviceability. With the advent of powerful computers in the mid-1980s, BMSs became more electronic and, consequently, capable of processing more data than a paper system. Building information modeling (BIM) is an emerging technology with commanding visualization and informatization ability, making it a perfect tool for generating modern management systems. Bridge information modeling (BrIM) is a developed concept extracted from BIM that concentrates on bridges. In this paper, the use of BrIM in the development of bridge management systems during the recent decade is verified through a scoping review of the state-of-the-art literature. The broad nature of the scoping review provides chances for rapidly reviewing evidence in emerging topics and analyzing knowledge gaps; it is used to capture the cutting-edge concept of information modeling in the body of literature. In this study, search resulted in a final refined list of 78 journal articles published in the recent decade, categorized into six different topic areas. This paper aims to verify different aspects of using information modeling in bridge management and identify the knowledge gaps, limitations, and emergent works for industrial and academic investigators. Descriptive and content analyses are presented to clarify the researchers’ focal points in this review, which shows trending keywords, leading journals, and articles’ frequency in each field. This study indicates that the concentration of BrIM researchers is on new inspection and test methods and maintenance optimization with a particular focus on concrete structures.

International construction arbitration is the main dispute resolution technique for international construction projects. The number of international construction arbitrations has significantly increased during the last decades. This increase has been accompanied by an increased interest in understanding how arbitrators make their decisions on the substance of disputes arising from international construction contracts. This paper constitutes the third publication of research undertaken to develop a conceptual framework to explain this arbitral decision-making process. It aims to assess the extent to which arbitrators take into account commercial norms and international construction law when determining the parties’ disputes. This research follows a grounded theory approach and draws on primary and secondary data. The primary data are collected via semistructured interviews with 28 international construction arbitrators. The secondary data are collected from international construction arbitration awards and scholarly writings. This study finds that international construction arbitrators appear to pay considerable attention to the parties’ contracts and the governing law of the contract when determining their disputes. However, the parties’ contracts and its governing law do not seem to be the only sources of obligations. Some arbitrators are receptive to arguments based on commercial practice and international construction law but do not concur on the authority of these substantive norms. The prevailing opinion seems to give customs and usages the same authority they have under the substantive law and to constrain the invocation of international construction law.

Statistics show that novice and experienced construction workers face different risks of construction accidents. However, there is very little work on this topic. This study conducted empirical experiments to quantifying the effectiveness of immersive virtual-reality-based safety training (IVST) across 6 worker specialties in terms of facing 17 hazardous scenarios at real construction sites. An immersive virtual reality–based safety training system (IVSTS) is proposed to measure the effectiveness of IVST programs with regard to six specialties in 2 controlled groups: (1) novice workers (40 college students), and (2) experienced workers (40 randomly selected construction practitioners). The study results indicate that the proposed IVSTS improved the safety performance index of all 80 participants by 14.12% in terms of correct selection of personal protection equipment, and by 28.95% in terms of hazardous scenario identification. The improvement of safety learning performance of the novice workers was better than that of the experienced workers, especially for hazardous scenario identification, with a 15.90% higher improvement index. The improvement suggestions for the existing IVST program were summarized according to the influence of the individual characteristics of the trainees on the learning effect between the novice and the experienced IVST programs.

External program manager involvement can provide professional and systematic management services to owners. It can help to manage and coordinate different subprojects and ensure the achievement of the overall strategic goals of large projects. The promotion of external program manager involvement is not only efficiency-driven, but the institutional environment also has a significant impact. This study developed a theoretical model to explain how external program manager involvement is influenced by external institutional forces (coercive, mimetic, and normative pressures) and internal top management support. Based on a questionnaire survey, this study tested the research model through partial least-squares structural equation modeling. The empirical results revealed that coercive pressures have no significant impact on external program manager involvement; owners’ top management support acts as a complete mediator between the mimetic pressures and external program manager involvement; normative pressures have the greatest impact on top management support and external program manager involvement, and top management support plays a key but partially mediating role. A direct contribution of this study is to reveal how different types of isomorphic pressures can be better exercised to facilitate the involvement of external program managers. Going further, by incorporating both external institutional pressures and internal top management support into the research model, this study provides insight into how organizations are influenced by external institutions to apply tools or approaches internally.

The project-based nature of construction means that a unique team of designers, contractors, and specialty trade partners must be assembled for the delivery of each new asset. The decisions around how these parties are procured and contracted can have downstream consequences for the success of those delivered assets, both in the timeliness and cost-effectiveness of the project, as well as in meeting the long-term needs of the client. Over the last 20 years, this Journal has published several studies that statistically compare cost and schedule performance between two or more project delivery systems. New and emerging alternative project delivery systems have recently been included in these studies. However, greater consistency in the data collection, analysis, and reporting practices are needed in the published literature around the topic of project delivery systems to allow for comparisons of studies, identifying trends over time, and understanding this body of knowledge. This article presents a review of the research methods used in 30 empirical papers published in leading construction engineering and management journals that studied the performance of different project delivery systems. These papers serve as a basis to identify successful practices and propose a set of recommendations for more consistent quantitative research in this field. The authors argue that greater consistency in project delivery research is critically needed, proposing a path forward that will assist researchers in conducting more reliable project delivery research for years to come. The paper is structured around three main categories of recommendations: (1) overcoming bias in project delivery data collection, (2) making a fair comparison, and (3) standardizing the presentation of results. In each category, the authors identify successful practices from published papers and make specific recommendations that provide a consistent standard for future studies. The vision of the authors is that the recommendations advanced in this paper, derived from exemplary cases of published work, will start a dialogue in the construction, engineering, and management community about establishing a standard of practice for quantitative project delivery system research. This article can be used as a resource for peer-reviewers and editors evaluating a project delivery study’s readiness for publication, and for faculty and graduate students looking for consistent guidance in conducting project delivery studies.

Prefabricated buildings have the advantages of efficiency, green, and sustainability. However, high cost seriously restricts the development and application of prefabricated buildings. The existing literature mainly focuses on a comparison of the incremental cost of prefabricated buildings and conventional buildings, and because most of the studies on the influencing factors of prefabricated cost are static analyses, the guiding role of prefabricated building cost control is inadequately explored. This study presents a fuzzy cognitive map (FCM)-enabled approach to investigate the relationship between influencing factors and prefabricated building cost, considering dynamic interactions. Content analysis and the Delphi method are combined to identify influencing factors, and the degree of influence was determined with the help of domain experts; thus, a cause-effect model consisting of nine concepts is established for analyzing the influencing factors of prefabricated building cost based on FCM. Results indicate that (1) the factors that have the greatest impact on $C_T$ (prefabricated building cost) include $C_1$ (scale effect), $C_6$ (PC PE), $C_4$ (standardization degree), and $C_8$ (construction management level); (2) $C_1$ is the possible root cause that affects the cost of prefabricated buildings, because it displays the strongest negative correlation with $C_T$, and the work in the early decision-making and design stage is of great significance; and (3) when the factors that are difficult to control for construction enterprises are not considered, the cost is most sensitive to changes in $C_6$ and $C_4$. The novelty of the proposed approach is that it can model the dynamic interactions between influencing factors and prefabricated building cost, and perform many kinds of what-if scenario analyses, including predictive, diagnostic, and sensitivity analyses. The proposed approach can be used to identify influencing factors for enhancing the probability of success in cost control of prefabricated buildings.

Because workers’ abnormal psychophysiological responses (e.g., high levels of stress and fatigue) are directly or indirectly linked to disorders and accidents at construction sites, monitoring workers’ abnormal psychophysiological responses during ongoing work enables preventive interventions, thereby improving their health and safety. As such, wearable biosensors (e.g., wristbands) have been extensively applied with machine-learning models in construction fields as a means of continuous and less-invasive psychophysiological monitoring. However, there is a significant knowledge gap in how to validate machine-learning models that monitor human responses from biosignals. Specifically, despite the importance of generalizability across different people and contexts for psychophysiological monitoring tasks, current validation methods do not ensure different subjects and contexts between training and testing data sets, and thus overestimate the generalization performance of models. To address this issue, the authors propose a new independent subject and context testing method, leave-one-subject-and-context-out cross validation (LOSCOCV), which ensures that training and testing data sets are collected from different subjects and contexts. The proposed LOSCOCV method’s generalizability estimation performance was compared with current validation methods through conducting a test wherein machine-learning models were developed to detect construction workers’ stress levels from biosignals collected during their ongoing work. The proposed LOSCOCV method showed statistically lower errors in estimating machine-learning models’ generalizability than other benchmarks. The results indicate that LOSCOCV is more valid than current validation methods in assessing models’ generalizability for tasks that monitor human responses from biosignals. Accurately tracking generalization performance is fundamental to efforts toward advancing the generalizability of models. This study therefore significantly contributes to the field’s use of biosensors and machine learning to monitor construction workers’ psychophysiological responses—ultimately advancing their health and safety.

Recent increases in sustainability requirements demand expeditious delivery of construction projects coupled with cost-effective and sustainable systems. In recent years, green lightweight plastic aggregate concrete (LWPAC) has been developed as a sustainable material for construction, highlighting financial, time, and quality attributes rather than material properties. However, most studies in this area are directed toward material properties or lifecycle costs in traditional use. Using lifecycle cost analysis, this research comprehensively examines incorporating LWPAC in different building systems to identify the optimal construction system in terms of cost, time, and quality (LCCA). The analyzed building systems extend to include traditional construction method (TM), insulated concrete forms (ICF), tunnel forms (TF), and insulated precast systems (IPS) using green LWPAC. To this end, we conducted a building case study with multiple scenarios related to the selected construction systems. For each scenario, assessments were conducted on the following aspects: (1) structural design of building elements; (2) construction cost and time assessment; (3) LCCA and sensitivity; and (4) quality. Results revealed that LWPAC using TM saves around 10% and 19% in concrete volume and steel quantities, respectively, compared with conventional concrete. The cost estimation results showed that TM using LWPAC is 2.6% higher than conventional concrete, although the LCC was almost the same value, amounting to $75.8\mathrm{USD}/{\mathrm{m}}^2$. Simultaneously, IPSs maintain the highest savings, at 22%, compared with TM using conventional concrete, with an LCC equal to $64.48\mathrm{USD}/{\mathrm{m}}^2$. Regarding completion time, there were decreases by 34%, 44%, and 52% for the ICF, TF, and the IPSs, respectively, compared with TM using conventional concrete. IPSs’ quality management systems showed the highest mean value of 51.8, followed by ICF, TF, and TM, of 49.9, 49.7, and 48.4, respectively. The findings of this research shed light on the benefits of using LWPAC as an alternative to conventional concrete in different structural systems, which plays a significant role in the overall sustainability of concrete structures.

The influence of institutional context on organizations to systemically change and adopt new technologies has so far received little attention in the architecture, engineering, and construction (AEC) industry. This study investigates the relationships between various institutional pressures, top management support, and the AEC organizational adoption level of building information modeling (BIM). The data were gathered through a survey of 186 BIM managers of AEC organizations in the United States, and the partial least-squares structural equation method (PLS-SEM) was applied to test the research hypotheses. The results show that normative pressure is the prominent institutional pressure affecting the level of BIM adoption in all types of AEC organizations. However, the degree at which and the path through which normative pressures work in these organizations vary based on their business type. For architecture and engineering organizations, the influence of institutional pressures on the organizational level of BIM adoption is mediated through the organization’s top management support. However, in contracting organizations, normative pressure and top management support have direct independent effects on the level of BIM adoption. These findings contribute to construction management knowledge by empirically supporting the idea that institutional pressures can be a powerful means to make systemic change to diffuse BIM in the AEC industry.

Managing change order is a critical area of concern in the construction industry, owing to the resulting disputes, claims, productivity losses, delays, and cost repercussions. There have been various studies proposing both quantitative and qualitative approaches to tackle the effect of change orders on project performance and success. Previous studies have almost solely focused on specific organizational policies, procedures, causes, and recommendations with the goal of capturing the essence of change-order management (CHM) within an area, organization, or kind of contract. However, their contributions are context-specific, limited to simple statistical method and make no reference to an inclusive concept of CHM performance factors. Furthermore, the existing literature urges a need for a realistic strategy to managing change orders, as well as limiting their negative consequences. Therefore, to address the research gap in the literature, this study proposes an operational and systematic CHM framework based on multidimensional performance factors to enhance the overall project success (OPS) factors, utilizing a partial least squares structural equation modeling (PLS-SEM) approach to develop the model. For evaluating CHM’s performance in terms of OPS, a conceptual model was developed. A questionnaire survey was used to conduct an empirical analysis to test the conceptual model. The information was gathered from 334 construction specialists that work in the field. The result of the analysis found that the explanatory power ($R^2$) value of the CHM model is 0.665, which revealed that CHM performance factors in projects is responsible for 66.5% of project performance, which has a significant impact and demonstrating a strong influence. Furthermore, the management of communication and relationships, dispute resolution, and financial issues have a significant impact on CHM based on outer loading values of ($\beta$) 0.433, 0.432, and 0.422, respectively. Documentation, procurement, design, and quality management come next in terms of influence on CHM effectiveness, with outer loading values of ($\beta$) 0.402, 0.367, 0.345, and 0.289, respectively. This study adds to the body of knowledge by providing new insights and assisting in the identification and better understanding of CHM performance factors in construction projects and their impact on OPS, as well as the use of an advanced statistical method, PLS-SEM. The outcomes of this study can be utilized as a starting point for implementing CHM in construction projects. The practical applications of the proposed quantitative approach model can be utilized by the client, the consultant, and the contractor to successfully plan, manage, evaluate, monitor, and control the CHM performance. The findings of this study will aid policymakers and decision makers around the world in focusing on the industry’s most critical difficulties and challenges.

Effective use of alternative dispute resolution (ADR) methods is critical because disputes are one of the biggest concerns in public–private partnership (PPP) projects. However, decision makers still struggle to implement ADR effectively due to limited knowledge about selecting the most appropriate ADR method that meets the needs of their PPP projects. Although some research has been conducted to develop approaches for ADR selection, most of these studies ignored critical points such as PPP projects and inherent characteristics of disputes. Moreover, fuzzy set logic and integrated multicriteria decision-making (MCDM) methods have not usually been chosen in these studies, posing various limitations. This study aimed to bridge these gaps by proposing a decision support framework for decision makers in selecting the most appropriate ADR method to settle disputes in PPP projects. Initially, an in-depth literature review was conducted to design the initial version of the framework. Then, the focus group discussion (FGD) sessions were organized and subject matter experts refined the draft framework. In total, a list of 16 selection factors and 30 dispute types were identified through literature review and the FGD sessions. At the final step, fuzzy analytic hierarchy process (AHP) and integrated fuzzy AHP and fuzzy TOPSIS were performed to determine the priority of each ADR method for each dispute scenario. Results showed that ADR selection is strongly influenced by inherent characteristics of dispute types. In other words, an ADR method cannot settle all dispute types, and thus, inherent characteristics of dispute types should also be considered during the ADR selection. Second, dispute management in PPP projects necessitates project-specific approaches. Thus, decision makers should not rely fully on standard contract forms such as those of the International Federation of Consulting Engineers (FIDIC). By developing a specific ADR selection framework for PPP projects by considering dispute types, this study significantly contributes to the theory of ADR domain and has important managerial implications.

Prefabricated buildings, as the development center of architectural industrialization, will produce a lot of point-cloud data in the process of information detection and management. These point-cloud data can be used for reverse modeling to restore the physical characteristics of prefabricated concrete components, which can provide a basis for prefabricated building informatics. However, traditional point-cloud data processing methods have limitations in the high-precision and high-efficiency restoration of physical entities. To solve this problem, this study assumes the reconstruction of the prefabricated point-cloud geometric model as the research object and builds a prefabricated concrete component model in industry foundation class (IFC) format using the equal-interval segmentation slice mapping method. The geometric surface quality of the prefabricated concrete component was determined by comparing the as-built and as-designed models. This study automated the steps in the data processing process through code and established the framework of a three-dimensional (3D) as-built model reconstruction platform on the assembly construction site. The feasibility of this method was verified using the prefabricated concrete component point-cloud data collected on site. This study solves the problems of easy loss of local details, noise point interference, and manual processing in the quality inspection process of prefabricated buildings. It is conducive to the construction quality management process of prefabricated buildings. The experimental results showed that this method is efficient, the code running time is less than 0.12 s, and the accuracy satisfies standard requirements.

It is crucial to extract knowledge from past accidents to prevent future ones. To this end, narrative classification is required in text mining. This autocoding process can be seen as a multiclass classification problem with an imbalanced data set. We evaluated the performance of several state-of-the-art machine learning methods, including 10 shallow learning methods (Rocchio, $k$-nearest neighbors, linear regression, naive Bayes, decision tree, random forest, gradient boosting, bootstrap aggregating, support vector machine (SVM), and shallow neural network), and five deep learning methods [deep neural network, convolutional neural network (CNN), recurrent neural network with long short-term memory, and a gated recurrent unit, and recurrent CNN]. The input data set contained 4,770 construction accident reports from the Occupational Safety and Health Administration (OSHA). After the narratives were relabeled based on the Occupational Injury and Illness Classification System (OIICS), the accuracy of all shallow classifiers was significantly improved compared with that reported in previous studies. SVM and CNN achieved the highest accuracy of 0.91 and 0.90 among the shallow and deep learning methods, respectively. Misclassifications occur because training data sets lack rich diversity for minority classes, some cases belong to multiple classes, and some divisions have the same key feature words. In the future, when a new data set is available, we can use learned patterns to classify them with high accuracy in practice.

The importance of reliability in conducting empirical research has been emphasized by construction engineering and management (CEM) scholars. Survey data commonly are used in empirical CEM studies. However, the use of such data in empirical analysis is susceptible to common method bias (CMB) issues, which can generate false correlations between dependent and explanatory variables, leading to spurious and unreliable results. To facilitate recent discussion about the rigorous application of research methodologies in CEM research, we provide a comprehensive overview of major CMB sources and examine articles published in the last 5 years to identify potential CMB issues in the Journal of Construction Engineering and Management. We found that approximately 90% of articles that used survey data in the empirical analysis were likely to be affected by CMB. The major source is the common rater effect, followed by the item characteristics effect and the measurement context effect. Because only a handful of studies tested or controlled for CMB, we propose effective and targeted ex ante procedural controls and ex post statistical controls to mitigate its negative impact. This study sheds light on an important and pervasive methodological issue in empirical CEM studies, and provides guidelines for improving the reliability of future empirical research in the CEM field.

Although vibration monitoring systems have been widely implemented on construction sites, most monitoring data cannot be efficiently used to establish an empirical vibration model because the information of the corresponding construction activities is usually not recorded. Identifying various construction activities from collected vibration data will bring new and unexpected benefits in practical applications. This study aims to fill this knowledge gap by proposing an accurate and efficient construction activity recognition model that combines the deep learning network [i.e., convolutional neural network (CNN)] and state-of-the-art RandAugment algorithm. The optimal number and strength of transformations in RandAugment were obtained through a parametric study. Vibration monitoring data sets, which were collected on various construction sites and generated by five different construction activities, were employed in performance validations. Results show that a well-trained CNN with RandAugment can classify construction activities with extremely high accuracy of 99.21%. Although RandAugment also improves the performance of another machine learning network [i.e., multilayer perceptron (MLP)], the CNN model still outperforms the MLP model in terms of classification accuracy. The proposed CNN with time-series RandAugment provides an accurate and promising tool to classify a tremendous amount of historical construction vibration data, thereby enabling the establishment of an informative database for future research.

Construction and demolition (C&D) waste generated from construction projects impacts the environment if not managed properly; appropriate C&D waste management is therefore necessary. The first step in this practice is the estimation of the quantity of C&D waste generated. The literature recommends numerous approaches for the quantification of C&D waste in a region based on the availability of data pertaining to construction and demolition activity and the accuracy of the estimate required. The major obstacle in this process is the nonavailability of data in developing countries such as India. A novel approach for gathering such information and processing it further to quantify and manage C&D waste is presented in this study. The study was conducted in the Indian city of Hyderabad during the January 2017 to December 2020 period. The C&D waste data were ascertained based on the building permits issued during this period. Quantitative analysis was conducted to identify the proportion of different types of buildings for which permits were issued, number of buildings demolished before the construction of new buildings, types of buildings demolished, and C&D waste generated using a random sampling technique. Furthermore, regression analysis was performed to determine the relationship between the built-up area and the building permit fee collected by the town planning department. The results of this analysis allowed for the calculation of the total construction area of buildings based on the fee collected for issuing the permit. The suggested approach is beneficial, especially in developing countries where data pertaining to construction activity in a region are scarcely available. The findings of this study will help waste governors and policy makers formulate policies for effectively and efficiently managing C&D waste.

In public–private partnership (PPP) projects, state-owned investors, with their administrative attributes, tend to be more concerned with their own face-saving (self-face) concerns, whereas private investors that partner with the government pay more attention to the government’s face-saving (other-face) concerns than their own. The two kinds of face-saving concerns both aggravate escalation of commitment (EOC). Accordingly, a multigroup model based on questionnaire data was built to analyze the differences in face-saving concerns of the two types of investors and the cross-enterprise differences that influence investors’ EOC. The main findings are as follows: state-owned investors prefer self-face concerns; the influence of face-saving concerns on investors’ EOC does not differ across enterprises; and self-face concerns and other-face concerns each have significant influence on both kinds of investors’ EOC, but the latter plays a larger role. The study enriches the understanding of EOC and emphasizes the advantages of private capital in reducing investors’ behavioral risk. The findings are conducive to improving the mechanism and control of multistakeholder EOC in PPP projects.

Many human errors occur in hydraulic engineering construction, and these errors may lead to huge financial losses. A systematic and comprehensive accident analysis is required to reduce the probability of human error. Human error analysis is a lengthy and challenging process because the tendency is for accident data to be presented in text format. In addition, construction human error management requires an intelligent and efficient analysis system to ensure the timeliness of accident prevention and control. Thus, this study proposes a human error intelligent analysis system on the basis of text mining to automatically extract text knowledge and reveal the accident evolution process. Using hydraulic engineering construction text, a topic feature extraction model is built to extract words and improve the human factors analysis and classification system (HFACS) model. Then, a human error causation network that integrates text topic features, the improved HFACS model, and Bayesian theory is developed to intelligently identify human factors and quantify the human error evolution process. The analysis system proposed in this paper provides an effective way to mine and apply the experience-based knowledge available in hydraulic engineering construction text for the intelligent analysis and prediction of human error, thus improving the efficiency of human error management.

Passage of overweighted commercial vehicles is one of the significant causes of damage to the pavement and structural components of bridges. Weigh-in-motion (WIM) systems can currently detect real-time traffic data; however, these data are stored in standalone databases. Building information modeling (BIM) has transformed the construction industry by injecting “information” into the building model and integrating different databases. BIM capabilities for bilateral exchange of data led to the inception of digital twin. This research investigates the feasibility of developing a digital twin of a mock-up bridge by integrating WIM data into a bridge information model (BrIM). The system was validated by first creating a mock-up bridge with affixed weight sensors attached to microcomputers and then developing a BrIM model and passing scaled vehicles over in real time with varying weight capacities. This study showed the feasibility of creating digital twins, ultimately enabling future research.

Analogous to Industry 4.0, we have the advent of Construction 4.0, a transformative process wherein digital technology and digitalization affect every phase of the life cycle of construction projects, from the design and contracting phase to the construction, operation, maintenance, upgrade, and decommissioning ones. The progress of digitalization has been uneven, as has been the recognition of the new security environment in which the architecture, engineering, construction, and operation (AECO) industry operates. Hackers for profit, disloyal competitors, disgruntled insiders, or even state agents employing hybrid warfare all utilize cyber means to achieve goals that can disrupt processes, corrupt information, interrupt operations, and cause significant material damage or even loss of human life. Starting from recognizing the paucity of studies in this field, this paper analyzes the results from a survey on cybersecurity awareness in the AECO industry. The analyses employed statistical methods such as $t\text{-}\text{test}$, Spearman’s rank correlation test, and ANOVA to test the hypotheses formulated to answer the research questions of the study. The resulting data are commented upon and placed in the context of an evolving industry confronted with the shock of the new security environment, while also moving forward on Construction 4.0 as a creator of added value, new efficiencies, and new capabilities.

Waste foundry sand (WFS) is one of the largest industrial solid waste in the world. WFS can be an excellent alternative to make green concrete, but the properties of this type of concrete have always been a controversial issue. Previous studies indicated that WFS could replace with sand up to 20% in concrete. This study presents an experimental investigation on the possible increasing the WFS replacement with sand by micronano bubbles water (MNBW). For this purpose, tap water was replaced by MNBW in the samples, which contained WFS. The results revealed that the concrete workability was reduced considerably by MNBW, and we recommended using superplasticizers to maintain the concrete workability. Also, the addition of 25%, 50%, 75%, and 100% MNBW accelerated cement hydration process and significantly improved compressive strength by 23.9%, 31.5%, 11%, and 39.9% in comparison to the mixtures contained 10%, 20%, 30%, and 40% WFS at 28 days. The test results demonstrated that MNBW had no adverse impact on water absorption and rapid chloride migration. Scanning electron microscopy analysis also confirmed the experimental results. The 40% WFS and 100% MNBW (W40-MN100 sample) were selected as the optimal proportion of the WFS-MNBW concrete mixture. The compressive strength of the W40-MN100 sample was 45.1%, 30.9%, and 12.7% higher than the control sample (without WFS and MNBW) and 28.2%, 39.9%, and 38.3% more than the W40 sample (40% WFS) at 7, 28, and 90 days, respectively. The research findings suggest that using 100% of MNBW can increase WFS recycling up to 40% in concrete, which can conserve natural sand resources, reduce environmental pollution, decline WFS disposal and disposal costs.

Generally, multiple risk factors are involved in tunnel construction accidents. Therefore, it is of particular importance to evaluate the mechanisms of multifactor risks and measure coupling risks in tunnel construction safety. Tunnel construction risk components under complex geology and construction factors were investigated using 101 tunnel construction accidents as examples. N-K and coupling degree models were introduced for tunnel construction risk analyses to calculate the coupling degrees of each component of single-factor and two-factor risk coupling models, respectively. Meanwhile, the correlation mechanism between tunnel construction risk value and each risk component was analyzed. The obtained results indicated that the coupling risk of complex geology and construction factors was greater than that of two factors independently, and a strong coupling relationship was witnessed between complex geology and construction factors. Strong coupling resulted in a sharp risk increase or sudden coupling change, which caused risk expansion and even generated new risks. This method provided a theoretical basis for the tunnel construction risk coupling mechanism.

Uncertainty is the general state of modern project scheduling, and fuzzy numbers are the main theme to resolve uncertain project scheduling problems. Traditional fuzzy numbers can represent uncertain information values of construction projects, but cannot reflect the accuracy and reliability of such information. The $Z$-number can express the uncertain construction project information reasonably via the first component, and use the second component to describe objectively the accuracy and reliability of information values, thus effectively overcoming the existing disadvantages of traditional fuzzy numbers. This study used historical statistical data and expert opinions to construct the $Z$-number, which effectively overcomes the drawbacks of conventional fuzzy numbers in disposing the uncertain activity information. A $Z$-number quantization scheme based on the fuzzy expected value and agreement index algorithm effectively eliminates a large amount of information loss caused by the quantization or defuzzification of the fuzzy number, and achieves the evolution and optimization from $Z$-numbers to AI fuzzy numbers. The objectivity of the research was ensured by fully considering the probability of completion of the activity and the risk preference of the decision makers. Numerical experimental results verified the effectiveness of the proposed method. Taken together, this study provides a new framework for exploring fuzzy logic–based solutions to the uncertain project scheduling problem, and fills the gap of $Z$-number-based quantitative expression of activity information in uncertain project scheduling.

Construction projects often face various uncertainties during their execution. Even if planned project schedules are optimal with respect to time or cost, their implementation may be affected by these uncertainties, resulting in project delays, cost overruns, or both. To address this challenge, this study presents a multiobjective robust optimization model for scheduling repetitive projects under the line-of-balance (LOB) framework, with the objective of minimizing the project cost while maximizing the schedule robustness without exceeding a given deadline. The ability of an LOB schedule to protect against unexpected events caused by various uncertainties was quantified using a new robustness measure. The proposed model was then extended to consider the constraints of available cash with the objective of devising financially feasible and stable schedules. The practicability of the proposed robust optimization model and the effectiveness of the robustness measure were verified by a highway project. The results show that the model can construct the optimal trade-off curve between project cost and schedule robustness, of which the most suitable schedule can be determined once additional information about the level of uncertainty and its impact are provided.

Abstract

The construction industry in many countries is faced with skill shortages. Attracting more female workers in the male-dominated construction industry can help alleviate this issue. However, perceived concerns of increased or specific Occupational Health and Safety (OHS) risks for females could be a significant deterrent. Due to their marginal representation in various trade roles, the overall analyses of OHS incident data fail to capture the specific OHS concerns that apply to female construction workers. This study presents an empirical analyses of workers’ compensation data obtained from Safe Work Australia for 12 years (2008–2019) to investigate specific OHS risks for female construction workers. The annual average incident trend does not present a steady increase or decrease in permanent and temporary incapacities among female construction workers. Moreover, the incident number, severity, and employment data together reveal that female workers in both professional and manual or trade occupations have lower incident rates than their male counterparts. However, the findings also reveal that: (1) building and plumbing women laborers and earthmoving plant operators are more prone to injuries that result in permanent incapacities; (2) fractures, soft tissue injuries, deafness, anxiety, stress, and depression lead to permanent incapacities among female workers; and (3) falls on the same level, muscular stress while handling objects, long-term exposure to sounds, work pressure, workplace harassment, and bullying result in permanently incapacitating injuries. The study also noted the differences in the occupations, mechanisms of incidents, and injury types that contribute to most incidents among male and female construction workers.

Abstract

The findings of this study can help training providers, construction organizations, and practitioners identify specific OHS concerns for developing more comprehensive OHS management plans, training, and awareness programs for female construction workers. The existing OHS management and training programs should be remodeled accordingly to incorporate gender-specific OHS risks and injury mechanisms rather than a one-size-fits-all approach. Clients, specifically in public projects, may stipulate that the contractor provides gender-specific OHS management plans to ensure the OHS of workers of different genders. It is vital to educate female construction workers from various occupations about specific OHS risks, incident mechanisms, and preventive actions. The higher occurrence of musculoskeletal disorders due to handling objects demands redesigning work processes to reduce female construction workers’ manual handling of objects. The present study also shows that work pressure, anxiety, and depression are prevalent among female construction workers, taxing their mental health and causing permanent incapacities. The psychosocial conditions and work pressure resulting from these health conditions must be managed at both the project and organizational levels to attract and retain more female workers in the construction industry. Such efforts are expected to improve female representation and their OHS, well-being, and job satisfaction and reduce skill shortages in the long run.

Many companies in the construction industry have recently expressed a particular interest in undertaking digital transformation by adopting new technologies in an attempt to increase productivity and quality. In responding to this new reality, these companies, particularly small and medium-sized enterprises (SMEs), face multiple challenges. Software vendors have recently been developing a large range of digital solutions, such as project planning and control software. Nevertheless, target companies are not generally guided in their choices and cannot find the support they need. The delays seen in these companies’ adoption of new technologies can be attributed squarely to a lack of references allowing them to understand their real needs and manage the main challenges they face. The project reported in this article was carried out with an industry partner specializing in construction mechanics. The first step was to understand and characterize the partner’s current project planning and control processes. Then, different information technology (IT) solutions were compared through a number of criteria defined with the company in order to choose the most appropriate software solution. Finally, new processes were proposed, and a prototype was developed and validated with the company in order to ensure the effectiveness of proposed changes. Using research action methods, the research brings a new perspective on the needs of specialized contractors, highlighting the challenges related to the characterization of the needs due to the difference of perspective of the different practitioners in the company. It also showed how it is important to define new processes to map the complementarity between the selected tools and the existing ones. The research finally highlighted the importance of an active involvement of the practitioners in order to validate the proposals and avoid resistance to change.

Underground piping remains one of the key uncertain infrastructures in urban development. Because they are buried underground without a clear vision, the management of the underground piping system is more complicated than other infrastructure. Their records are not usually fully available or accurate, and excavation to validate underground structures is often needed in infrastructure projects. Detection of unexpected obstacles may lead to project delay, increased time spent, and risk of damaging existing piping. The traditional underground piping system management relies on computer-aided design (CAD) drawings and historical empiricism. Still, it cannot meet the strict requirements of infrastructure management nowadays or make the management efficient and sustainable because of the technical limitations and conceptual obsolescence. Therefore, to address this issue, this paper proposes an innovative method that aims to manage the underground piping life cycle. This method is to develop a building information model (BIM) in a geographic information system (GIS) environment by integrating heterogeneous data from both BIM and GIS, which not only achieves three-dimensional (3D) visualization but also seamlessly connects to portfolios, programs, projects, and assets throughout the life cycle of urban underground piping. This is a critical development to integrate and analyze geometric and nongeometric information of aboveground and subsurface building assets and the surrounding environment to stimulate interaction with real-world context. This paper also introduces a case study in China to further prove the feasibility of this method and the format based on it in practice. In the case study, the proposed method is implemented together with a piping sensor to further illustrate the effectiveness of the method over trenchless detection and real-time monitoring for daily management of underground piping. Because the model is visible and the monitoring is in real time, the management would be straightforward. At the same time, this method can analyze, plan, and monitor activity, so the management of urban underground piping systems would become smart. Therefore, an efficient and sustainable management performance can be achieved. This method is meaningful for managing the urban underground piping system, especially for those with expansion, refurbishment, or maintenance for an urban underground piping system.

Many scholars have denounced innovation in construction as problematic. Existing work processes and routines may resist or even block the adoption of new technologies. Unravelling how new technology interferes with organizational processes could facilitate a more mindful innovation process. This study, therefore, conceptualizes how technology pilots influence early change of existing practices. Five utility localization projects were studied, in which ground-penetrating radar (GPR) technology was introduced. The researchers observed existing practices onsite, demonstrated and moderated the use of GPR, and conducted semistructured reflective interviews. Based on the concept of routine dynamics, selective and axial coding resulted in the identification of two types of mechanisms: (1) change triggers occurred when routines fell short and practitioners started favoring the GPR, and (2) stabilization occurred when routines proceeded as expected and shielded GPR from being considered. Objecting linear innovation adoption, the findings contribute an empirical conceptual model of early-stage innovation adoption dynamics. This model aids decision makers in timely identifying (1) whether routines are receptive to the uptake of new technologies, and (2) how new technologies may advance these routines. Additionally, this study demonstrates the merit of using practice-based studies to conceptualize in rich detail how innovation processes are shaped in situated construction contexts.

Abstract

Construction project delays are costly to stakeholders. Therefore, extensive research has been carried out in various countries covering different types of construction projects to identify delay factors and mitigation strategies. However, the literature has not documented the impact of a once-in-a-century event such as the COVID-19 pandemic on construction projects’ schedules and costs. The present study uses a case study approach to examine the effect of the COVID-19 pandemic on three lift irrigation projects’ construction programs and the financial implications for the contractor in the Indian construction industry. The findings reveal that the main factors responsible for delays due to the COVID-19 pandemic were fear of an outbreak, imposition of a nationwide lockdown, late return of workers, resource shortages, and changes in operating procedures. Furthermore, the study found that the contract conditions and government directives allowed for time extensions in construction projects by recognizing the pandemic as a force majeure. However, such provisions did not protect contractors’ financial interests. This study evaluates the financial implications of the COVID-19 pandemic for the contractor in terms of worker compensation during the lockdown, idling of resources, overhead costs, cost escalation, and liquidity.

Practical Applications

The public health crisis from the COVID-19 pandemic led to a series of preventive measures from the government and organizations worldwide that caused severe disruptions in construction projects and disturbed the construction supply chain. This study recommends rethinking the scope and coverage of force majeure and excusable delay clauses in standard construction contracts beyond provisions for an extension of time to protect the financial interests of different stakeholders. The vagueness in assessing and compensating for the losses from unprecedented events such as the COVID-19 pandemic could expose construction organizations to significant financial risks and threaten their existence in a highly competitive industry. Therefore, construction organizations should carefully review the contract provisions of future projects for proper risk allocations to accommodate unprecedented events and pandemics to help them recover successfully from various disruptions and associated financial losses. Moreover, proper communication and negotiation, a supportive and collaborative project culture, and early government interventions are crucial in dealing with such events and avoiding disputes among project stakeholders.

The objective of this study was to investigate the economic and environmental appeal of the world’s largest glass fiber–reinforced polymer (GFRP) RC flood mitigation channel, which was built in Jazan (Saudi Arabia). The Jazan flood mitigation channel (JFMC) is a 21.3-km-long channel completed recently in the southwest area of Saudi Arabia on the outskirts of the new Jazan Economic City (JEC), which is an advanced industrial zone and encompasses an important network of high-end facilities. A deterministic approach is applied to a comparative life-cycle cost (LCC) analysis, and sensitivity analyses are used to predict the impact of uncertain factors, such as discount rate and maintenance periods, on the LCC analysis of JFMC. Additionally, the environmental credentials of the GFRP-RC channel are investigated through a life-cycle assessment (LCA) analysis. The LCA analysis is performed from cradle to gate and from cradle to grave, specifically from product stage (or resource extraction) to end of life (EoL). The JFMC is designed for a 100-year service life, and the LCC and LCA analysis performed are in compliance with the international standards ISO 15686-5, ISO 14040:2006, and ISO 14044:2006. The obtained economic and environmental results are compared to the results obtained from an epoxy-coated steel (ECS)-RC channel alternative that was originally designed. The results show the significant life-cycle benefits of the preferred GFRP-RC alternative from both an economic and environmental perspective, advancing the body of knowledge available for LCC and LCA of flood control and mitigation concrete channels reinforced with FRP materials.

The method of onsite pouring is mostly adopted in current construction of traditional frame beam (TFB) structures, bringing adverse effects like poor supporting timeliness, low concrete quality, hard-coordinated deformation, complex working procedure, high construction risks, and so on. In order to solve these problems existing in the structure and construction of TFB, a new type of prefabricated frame beam (PFB) structure was given a brief introduction of its design and feasibility. Based on a slope supporting project in Guizhou Province, China, main points and techniques in the construction of PFB structure were summarized. Differences as well as pros and cons in the process of onsite construction of two kinds of supporting structure were discussed and analyzed. Results and monitoring data showed that the PFB structure has advantages over TFB structures in terms of the construction period, personnel and machinery, safety management, economic costs, ${\mathrm{CO}}_2$ emissions, supporting effects, and so on. The feasibility and stability of PFB structures in high slope supporting projects were verified, which lays a foundation for the popularization and application of prefabricated structures in similar slope supporting projects.