Journal of Construction Engineering and Management

Abstract

Learning from online comments is essential for enhancing understanding and improving online housing reputation (OHR). However, two significant issues require attention. First, analyzing online comments for reputation information extraction is a labor-intensive and time-consuming task. Second, most existing online housing information platforms lack effective visual aids, merely presenting the average comment ratings or listing comment texts without secondary interpretation. To address these challenges, this study proposes an OHR assessment framework based on text mining and visualization technologies. This study first evaluates the performance of eight sentiment analysis models for analyzing housing comments, and the attention-based BiLSTM model achieved the highest accuracy (83.57%). Additionally, a housing attribute ontology is constructed to reveal eight critical attributes influencing OHR. Finally, a reputation visualization scheme is designed to comprehensively present OHR. A case study for analyzing online comments from three construction enterprises reveals the advantages and feasibility of the proposed framework for assessing OHR. This study contributes to the body of knowledge by establishing the connection between housing comments and OHR, greatly advancing the research in the construction domain’s reputation management. Furthermore, OHR analysis can facilitate decision making optimization for both consumers and managers, which has theoretical and practical significance for the healthy and sustainable development of the online housing market.

Practical Applications

Developing a sound housing reputation assessment method is crucial for the development of the online housing market. Emerging information technologies like natural language processing and deep learning appear promising in the comment-based reputation analysis. This study proposes an online housing reputation assessment framework that combines text mining and visualization technologies to automatically analyze housing comments and generate online housing reputations. The findings of this study establish the connection between housing comments and reputation and reveal eight key attributes influencing online housing reputation, contributing to research on reputation management in the construction field. The potential practical applications of this study are elucidated from the perspectives of consumers, construction enterprises, governments, and the construction industry. For consumers, reputation assessment can optimize the decision making process and promote public supervision to force construction enterprises to improve the quality of their housing products. Construction enterprises can timely improve their product strategies based on reputation information to increase revenue. Governments can strengthen the supervision of low-reputation enterprises and low quality housing products, thereby enhancing government credibility. The proposed framework can encourage technological innovation and digital transformation in the construction industry and even can be extended to other industries for online reputation assessment.

Despite the increasing recognition of the potential of Circular Economy (CE) strategies, their effective application to decarbonize the construction value chain faces multiple challenges. Ambiguity surrounding CE, stemming from a lack of clarity on actionable strategies, is a common concern. Existing studies lack a holistic approach for systematically integrating CE principles throughout the construction value chain, resulting in a scarcity of information on how to practically adopt CE within projects. This research aims to accelerate CE adoption by creating a value chain-structured portfolio of CE strategies and analyzing the implementation of such strategies in actual projects. To this end, a portfolio of CE strategies was compiled in a value chain-based-structure, which was evaluated against 71 US-based projects of exemplary sustainability criteria to scrutinize their successful implementation. Additionally, the study examined the interconnections and network dynamics among these strategies through association rule mining, network, and statistical analysis techniques. Results revealed a pronounced focus on strategies within the design, construction, and end-of-life value chain phases, with significant gaps existing in areas related to project financing, planning, logistics, property market, and operations. “Requiring sustainability certifications, standards, or labels” emerged as the most pivotal strategy in terms of frequency and number of associations, highlighting owners’ significant role in CE transition. This study contributes to the body of knowledge by providing practical guidance and insights for industry practitioners in terms of actionable CE strategies and their state of implementation, ultimately fostering a more sustainably built environment.

There has been a plethora of research on the performance of schedule and cost in construction projects. However, current research falls short in investigating the combined contribution of different construction technologies on project performance metrics. Specifically, none of the previous efforts have used integrated earned value management (EVM) parameters and utilized construction technologies to assess project schedule and cost performance. This paper fills this knowledge gap. To this end, a multistep research methodology was employed to (1) explore the utilization ratings of 13 construction technologies based on data collected from 52 industry experts; (2) rank these technologies in terms of their contribution to schedule and cost savings; (3) map the utilization ratings of construction technologies to schedule and cost variances by leveraging data from 48 construction projects with varying levels and scopes of technology integration to gauge the impact of using these technologies; and (4) develop an associated decision-support model that underwent rigorous verification under extreme conditions and sensitivity analysis. This study makes a noteworthy contribution to the existing body knowledge, revealing that the major drivers for schedule savings are building information modeling (BIM), unmanned aerial vehicles (UAVs), and augmented/virtual reality (AR/VR), while additive manufacturing (AM), digital twin (DT), and Internet of Things (IoT) are the most significant contributors to cost savings. Moreover, the study reveals strong correlations between various technologies, such as UAVs and laser scanning, geographic information system (GIS) and GPS, AR/VR and digital twins, as well as robotics/automation and AR/VR. Furthermore, the study introduces a novel model that enhances the prediction of project performance, specifically in terms of schedule and cost variations resulting from the adoption of various construction technologies. Eventually, it improves the decision-making process related to the deployment of the investigated construction technologies.

General contractors outsource increasingly complex and sophisticated specialized tasks to many subcontractors. In doing so, general contractors typically pick the subcontractor that offers the lowest price for the work to be done, but an informed decision can be made only if the general contractor can assess potential subcontractors’ performance before hiring them. The performance of subcontractor candidates is measured in this study by using key performance indicators (KPIs) identified after an extensive review of previous research. These KPIs, the analytic hierarchy process (AHP), and the evidential reasoning (ER) method are combined to create a performance-based subcontractor selection (PBSS) model. The proposed model is generalizable and can help general contractors to assess the performance of subcontractor candidates relative to the general contractor’s critical objectives including subcontractor performance in relation to price, duration, quality, safety, and environmental impact. The model is scalable in that other objectives can be added or can replace existing ones depending on the characteristics of the project and the preferences of the general contractor. A real-world case study was used to show that this model was easily implemented and that it generated practical results. A general contractor can use PBSS confidently to evaluate the overall performance of each subcontractor candidate before they select one for a specific part of the project.

Abstract

Building information modeling (BIM) adoption has received increasing attention over the past two decades due to its potential to revolutionize the built asset industry. Several different characteristics, facilitators, and drivers of BIM adoption have been identified, which has led to the development of BIM adoption frameworks and guidelines. Many of these elements and frameworks have focused on specific organizational-, project-, or industry-level characteristics. Few have taken a competency-based approach to BIM adoption. Thus, this paper aims to empirically evaluate and validate the factors influencing BIM adoption within organizations operating in the built asset industry as understood through the evolution of their BIM competency profiles. The research consists of a mixed-method data collection on a sample set of 368 organizations participating in the Initiative Québécoise pour la Construction 4.0 (IQC 4.0). The research method of analysis is based on a bivariate approach for testing the correlation between the variables concerning the organization’s internal characteristics and their BIM competency development level. The findings show that the factors, which include field of activity, BIM experience, size, financial capacity, and geographical location, are correlated to an organization’s BIM competency development level; in parallel, an organization’s perceived maturity is strongly correlated. The results from this research can be potentially useful to properly guide and structure governmental initiatives supporting competency development for digital transformation.

Practical Applications

Government initiatives have been undertaken to promote the adoption of BIM in the built asset industry and to encourage organizations to undertake their digital transformation. The province of Quebec, Canada, has established a governmental roadmap for BIM adoption that identifies the key actions to be undertaken by all public bodies in the province to ensure a consistent approach to BIM adoption, and a targeted initiative supporting the digitalization of companies called IQC 4.0. The paper discusses the factors impacting BIM adoption through the lens of organizational competency, an approach not yet covered in the literature. The organizational competencies include managerial, administrative, functional, operational, technical, implementation, supportive, research and development, and normative (ISO) ones. Factors such as an organization’s size, field of activity, financial capacity, and location can influence the development of organizational competencies that impact BIM adoption. Moreover, the internal characteristics of an organization may have varying effects depending on the competencies involved. Those results will provide feedback and potentially inform governmental and organizational initiatives.

Multicriteria analysis is widely used to prove the excellence of prefabricated construction compared with conventional construction. However, because previous studies have not presented the results of an integrated analysis, identifying the merits of prefabricated construction is challenging. Furthermore, clients experience difficulty when considering prefabricated construction owing to the complexity of simulations and the lack of data. Therefore, this study aimed to conduct a multicriteria analysis for prefabricated construction considering productivity, safety, environment, and economy, and develop a multi-prediction model. This study was conducted in five stages. Results revealed that prefabricated construction was superior to conventional construction for all variables, with the former scoring 0.0927 on average and the latter scoring 1.863. The multiprediction model utilizing a decision tree and Bayesian optimization has a high performance, achieving over 94%. Using study findings, decision makers can use the multiprediction model to assess the expected performance of prefabricated construction. This enables a comprehensive comparison of various conditions across different aspects through the multicriteria analysis.

Abstract

Effective knowledge management (KM) and knowledge sharing (KS) are critical for the success of international construction projects (ICPs), but they are often hindered by knowledge-hiding (KH) behavior. This study examines the complex and multifaceted factors that affect KH in ICPs, informed by role identity theory (RIT) and social exchange theory (SET). In particular, we investigate how ethnic minorities (EMs), contingent workers (CWs), and interpersonal justice (IJ) influence KH in these projects. Using structural equation modeling (SEM) and artificial neural networks (ANNs), we reveal the significant effects of EMs and CWs on KH and the partial mediation of IJ between EMs and KH. We also find that control variables such as gender and age have significant impacts on KH. The findings underscore the pivotal role of inclusive practices in mitigating KH and enhancing KM in ICPs. This research provides actionable insights for creating a transparent and inclusive knowledge-sharing culture in multicultural project teams, which contributes to improved project outcomes and organizational success. This study also paves the way for future research on power asymmetries, contextual factors, and the influence of organizational cultures, leadership styles, and team dynamics on KM in ICPs.

Practical Applications

To address the challenges of knowledge hiding in international construction projects, this study proposes a comprehensive strategy for effective knowledge management in international construction projects, based on established research and best practices. The strategy is designed to fit the unique dynamics of international construction projects and includes the following components: (1) implementing a knowledge-sharing framework that values the contributions of each team member, regardless of their demographic backgrounds; (2) supporting leadership with a focus on inclusivity and equity, and a reward system acknowledging knowledge-sharing efforts irrespective of demographic backgrounds; (3) investing in construction-specific knowledge-sharing technologies that are accessible to all team members; (4) fostering inclusive communities of practice and incorporating performance metrics for inclusive knowledge sharing; and (5) encouraging open communication, developing knowledge transfer plans, and ensuring continuous improvement in overall knowledge management. This multifaceted approach aims to create a transparent and inclusive culture of knowledge sharing in international construction projects, which contributes to improved project outcomes and organizational success.

Civil engineering design problems are inherently complex, characterized by iterative processes, multiple criteria, and time-consuming manual design work. Traditional methods often struggle to rapidly reach optimal designs, lacking guarantees of achieving optimality. With the advent of recent advances in artificial intelligence (AI), this study attempts to answer the research question: How AI algorithms can expedite the civil engineering design process, enhancing efficiency and accuracy in reaching optimal solutions with fewer resources. The research employs a Markov decision process-based AI framework, integrating configuration design and refinement in a unified approach. The methodology begins with the Markov decision-making process to mathematically model the design process, followed by reinforcement learning for automatic design and refinement of solutions. Applied to a planar truss bridge design problem, the AI design agent produced feasible truss designs under various constraints efficiently, demonstrating superior capability and flexibility. The results indicate an average improvement of 12% in accuracy and 88% in computational efficiency over traditional methods. The meaning and significance of the results lie in the innovative integration of Markov decision-making and reinforcement learning into a unified two-stage design framework, significantly advancing the body of knowledge in civil engineering design automation. The speed and accuracy of the AI design agent validate the feasibility of the proposed model and highlight its potential in effectively solving complex civil engineering design problems. The directions for follow-up research are suggested to extend this framework to a wider array of design challenges and to refine the AI agent’s adaptability in more diverse design contexts.

Damage detection in structurally reinforced concrete elements is a vital topic for structural health monitoring and for assessing the capacity of reinforced concrete members. In this regard, many destructive tests exist that allow technical experts to evaluate the damage to a structural member. Such techniques are often employed for damage assessment after a natural disaster or a man-made event to assess the structural integrity and prioritize the locations that require urgent repair work. The researcher was successful in developing a testing methodology using nondestructive testing to identify internal damage in reinforced concrete elements by linking the delay in ultrasonic wave propagation to the initiation, development, and progression of cracks in the concrete surrounding the steel reinforcement. It was observed during experimentation that using the proposed methodology of gradual loading and comparing the speed of travel of the ultrasonic pulse velocity to the undamaged elements, the researcher was successful in identifying and localizing the internal cracked portions in the structural concrete member. Twelve reinforced concrete elements with full-size tension, compression, and shear reinforcements were tested to validate the proposed nondestructive test methodology. Direct and indirect methods of investigation were employed for testing purposes. From the performed experiments on reinforced concrete members, it was concluded that the proposed nondestructive testing methodology can be successfully applied in structural capacity assessment. The data collected from on-site investigations can be used for minimizing repair and strengthening work. In-addition an in-depth sensitivity analysis was conducted using the Latin hypercube sampling method to understand the influence of each variable on the ultrasonic pulse velocity test results. A purely random combination of parameters was adopted for the sensitivity analysis. Through the analysis, it was concluded that concrete strength played the most influential role in the ultrasonic pulse velocity testing followed by concrete cover, bar diameter and wave path length.

Abstract

To reduce the risk of unexpected cost of rework (COR), a variety of predictive models have been developed in the construction management literature. However, they primarily focus on prediction accuracy, and rather less attention has been paid to the trustworthiness of prediction models. This increases operational risk and hinders its integration in related decision-making. Aiming to reduce the utilization risk and increase the reliability of COR prediction models, this study exploits the graph convolutional network (GCN) model, which enhances representativeness by accommodating interrelationships among the root causes of nonconformances. The GCN can process a more representative input network that provides COR records while factoring in the shared root causes of nonconformance in the resulting COR. The proposed approach achieved a COR prediction accuracy as high as 85%, which is significantly higher than that of any existing cost prediction model. The demonstrated accuracy and lower risk of the proposed GCN model thus enhance the reliability of the prediction and trust in its outcome, facilitating its integration into developing rework prevention strategies and relevant resource allocation for construction professionals. The study contributes to construction project management by proposing a novel COR prediction model that embodies accuracy, representativeness, and interpretability. Whereas we tailored the GCN model to predict COR with a focus on nonconformance root causes, it is noted that rework costs can also be influenced by other project factors, such as site safety.

Practical Applications

Managing rework costs is a critical challenge in the construction sector, necessitating predictive models that are not only accurate but also trustworthy. This study leverages graph convolutional network (GCN) to offer a prediction model that is both accurate and reliably interpretable. The GCN model enriches cost of rework (COR) predictions by analyzing how different project elements, such as design flaws or material issues, are interconnected, providing a more holistic view of potential rework triggers. The customization of the GCN model is crucial, requiring specific adjustments based on individual project requirements and decision-making goals, and remains relevant across diverse construction projects. The proposed predictive tool processes complex data and provides insight into how various project factors interplay to affect rework costs. By providing a clear rationale behind its predictions, it supports more informed decision-making in allocating resources and developing strategies to prevent rework, ultimately leading to more efficient and cost-effective project management. Having COR prediction based on a comprehensive understanding of all related project factors equips professionals with insight about the predicted COR impact on the overall construction budget and its related root causes. This, in turn, fosters greater confidence among project stakeholders and sets a stronger basis for strategic planning, resource allocation, and risk management.

Abstract

Engineering design and construction teams commonly use decision tools, such as rating systems, to manage the complexity of infrastructure projects. However, these systems often focus predominantly on environmental dimensions, potentially overlooking the holistic economic and social aspects of sustainability. This can hinder decision-makers from recognizing the benefits, thereby inadvertently impeding optimal sustainability performance. This research explores a potential solution: reframing the goals of credits on rating systems to explicitly highlight the interconnectedness of environmental, social, and financial dimensions. The aim was to amplify engineers’ motivation towards higher levels of sustainability performance. The study examined the effect of the goal-framed credits by comparing the sustainability scores between engineering professionals ($n=42$) who used the original Envision system and the reframed version when evaluating a case project. The control group participants averaged a score of 95.4 Envision points (37%), while the intervention group averaged 121.8 Envision points (48%). The reframed credits significantly increased engineering professionals’ sustainability goal setting. Emphasizing financial and social objectives on credits from the Envision rating system, rather than solely focusing on the existing environmental goals, had a favorable influence on professionals’ sustainable design choices. This reframing, by emphasizing the interconnectedness of environmental, social, and financial dimensions of each decision, appears to amplify the awareness, motivation, and selection of higher levels of sustainability achievement, thereby increasing their perceived value and leading to a shift in sustainability-oriented decision-making. The findings suggest that reframing rating systems to better emphasize the interconnectedness of the environmental, social, and financial dimensions of each decision can serve to help engineering teams set higher goals for sustainable performance.

Practical Applications

For infrastructure project managers, architects, and designers, this research highlights the significance of considering economic and social aspects alongside environmental concerns. By doing so, it can further empower stakeholders to make more well-rounded and sustainable decisions that benefit both their projects and the communities they serve. Small changes in how decision tools are worded, or framed, can have a significant effect on project goal setting. A better understanding of how framing affects design cognition can also help inform how project managers, architects, and designers present information to their clients. Subtle changes in the decision tools and the way information about projects is expressed cost little compared to the actual cost of infrastructure, offering inexpensive and easy to implement strategies to influence and nudge project teams toward setting higher sustainability goals during the design and construction of infrastructure projects.

Collaborative robots are increasingly recognized as potential assistants to relieve workers from repetitive and physically demanding tasks on construction jobsites. Despite the great potential, most efforts have focused on developing various artificial intelligence (AI) and robotic technologies to achieve specific human–robot collaboration (HRC) functions. However, there is a significant lack of research regarding the impacts of such collaboration on construction work performance and workers’ perception and acceptance of collaborative robots, which could be a critical influence factor on the feasibility and effectiveness of HRC on construction jobsites. To this end, this study aims to evaluate the multidimensional impacts of collaborative robots on work efficiency, quality, and workload as well as workers’ perception and acceptance. HRC experiments on sample construction tasks (i.e., wood assembly) were conducted in conjunction with quantitative measurements and subject surveys. Through comparison between HRC experiments and human–human collaboration (HHC) experiments based on this case study, it was found that HRC could improve up to 29.3% and 88.6% in work efficiency and assembly accuracy, respectively, and reduce workers’ workload by up to 20.3%. Furthermore, workers’ perception of HRC is found to be positive overall with higher acceptance after HRC experience, characterized by questionnaires designed based on the technology acceptance model. Through physical experiments, this research is expected to produce more reliable results compared with conventional approaches where participants are simply provided with imaginary scenarios. The findings will also guide the development of robotic technologies to enhance the practical application of HRC in construction.

Abstract

When deciding which project to fund, the comparison of project-required resources and monetary or financial benefits is relatively straightforward (as these can be presented in dollar values). In contrast, comparing the nonmonetary benefits of projects can be challenging. The literature often overlooks this problem and only provides high-level approaches (e.g., comparing a project’s contributions to the organization’s strategic goals). Such approaches are inefficient as they are highly subjective and may be impacted by decision-makers’ bias. To this point in time, the literature has not effectively addressed this problem. This literature gap and the practical importance of the problem motivate the development of detailed step-by-step methodological processes to reduce subjectivity and enable a quantitative comparison of the nonmonetary benefits of projects. This paper develops such a novel straightforward methodology and illustrates its application through a case study where nonmonetary benefits of a railway construction project are compared against a hospital expansion project. In addition to this methodological contribution, this paper also contributes to the current literature on construction engineering and management by showing how to perform an effective comparison of the nonmonetary benefits in the project investment decision. The paper also sheds light on how the reasoning structures of different project decision makers may be impacted by their degree of optimism.

Practical Applications

Construction projects may deliver a range of dissimilar benefits. Unlike monetary values (such as required resources and financial or monetary benefits), the comparison of nonmonetary benefits (e.g., social values) of different proposals can be challenging. This paper provides a two-phased methodology to facilitate this comparison. In the first phase, two straightforward questions are asked to draw benefit satisfaction graphs, and in the second phase, different future scenarios are integrated to make the comparison process more reliable. Ultimately, a methodology is developed to show how the nonmonetary benefits of different projects can be compared. It is then illustrated on a case from the Australian government where the nonmonetary benefits of a railway construction project are compared against those of a hospital expansion project to assist with the decision as to which project should be given a higher priority to be funded.

Public–private partnership (PPP) stakeholders have gained traction for their potential to create long-term value. Extant literature emphasizes the importance of suitable stakeholder management strategies, yet the analysis of such strategies has traditionally been conducted from the viewpoint of the responsible parties (i.e., the PPP consortium and the contracting agency) because these are the stakeholders in charge of developing the project and delivering its much-expected benefits. While extant literature often focuses on the interests of either responsible stakeholders or impacted stakeholders, this study addresses the critical but underexplored dynamics involving the wide arrangement of responsible, impacted, and interested stakeholders encompassing diverse groups, such as local communities, interest groups, advisors, local governments, users, and debt providers, who interact and shape project outcomes. Drawing lessons from a multiple-case study involving two mature toll road markets composed of 49 projects, this research reveals the main stakeholders’ issues at three different levels: institutional, organizational, and project. To address these challenges, a relational governance model is proposed where each identified stakeholder should play a specific role in key decisions made in specific project phases. This study provides insights into stakeholder dynamics in user-pay transport PPPs, offering solutions for enhanced engagement and project outcomes. This research offers relevant theoretical contributions to rethinking the roles of impacted and interested stakeholders in PPPs, and these insights have practical implications for improving stakeholder engagement, risk management, and project outcomes in user-pay transportation PPPs.