Journal of Construction Engineering and Management

Recently published professional learning outcomes require future engineers to think of the impacts that engineering decisions have on society. History shows that construction and civil engineering projects can exacerbate inequality by ignoring community concerns and failing to consider the impacts on marginalized and vulnerable stakeholders, among other factors. How might construction engineering professors help students meet these standards, and how do construction and civil engineering students respond when construction engineering is framed as inextricably linked to these obligations? We designed and evaluated curriculum modules aimed at helping develop a critical consciousness with construction and civil engineering students ($N=177$) in three construction and civil engineering courses at two universities in the midwestern region between 2020 and 2021. The curriculum builds on a three-phase framework aimed at encouraging students to see social inequities and their impacts, finding social inequities unjust, and enhancing reflective self-awareness. Post-implementation responses to the case study designed to make clear the need for a critical consciousness in engineering found that students responded positively to both the instructional approach of a case study and the content connected to critical consciousness. We discuss implications for the development of further curriculum and the implementation of such an approach.

Abstract

Emerging information and construction management technology (EICMT) has become prevailing in the architecture, engineering, and construction (AEC) industry to achieve digital transformation by applying emerging information technologies (i.e., Internet of things, cloud computing, and mobile Internet) in construction management works (i.e., cost management, stakeholder management, on-site construction management, etc.). This study aims to test the relations between organizational elements and building professional EICMT implementation and explore its practical path by identifying the mediators and moderators. 507 valid responses were obtained using the questionnaire online. Mplus 8.0, a statistical analysis software that prevails in handling complex models, is used for path analysis and bootstrapping tests in this study. Path analyses are conducted to test the relations between organizational elements and EICMT implementation. Bootstrapping tests are used to identify the mediator and moderator. The finding reveals that corporate normative pressure and technology facilitating conditions can directly affect building professional EICMT implementation; the mediating role of perceived ease of use is verified between technology facilitating condition and building professional EICMT implementation, and the moderating effect of top management support is confirmed by remodeling the relations between organizational elements and EICMT implementation, which provides a boundary condition for the building professionals’ EICMT implementation. Our study extends the relations between organizational elements and building professional EICMT implementation by exploring the mediator role of perceived ease of use and moderator of top management support. The findings from this study can enrich the research on the antecedents of EICMT implementation in the AEC industry. Moreover, this study provides guidelines for practitioners to formulate corporate norms, foster corporate culture, and provide technology facilitating conditions to promote EICMT implementation. It also requires top management to engage in digital transformation in the AEC industry.

Practical Applications

Emerging information and construction management technology (EICMT) is applied to improve project management efficiency in the AEC industry. Construction enterprises make several efforts to encourage their building professionals to implement EICMT, such as formal norms and informal culture, but the implementation of EICMT is contentious in practice. This study recommends that corporate normative pressure, technology-facilitating conditions, and top management support are crucial for the EICMT implementation in building professionals. Besides, building professionals prefer to select technologies that are easy to use rather than practical. Therefore, construction enterprises should formulate formal norms related to EICMT implementation to evaluate building professionals’ job performance, which can urge employees to apply EICMT in their daily work and also provide rewards for these employees who use EICMT in their jobs and improve work outcomes. Moreover, construction enterprises can provide convenient conditions on EICMT to encourage building professionals to accept EICMT, such as training, operation manuals, and professional guidance. Construction enterprises’ top managers should actively participate in EICMT promotion by implementing EICMT in daily work, communicating EICMT with their employees, and recognizing its benefits.

Contract flexibility is like a double-edged sword for contractor opportunistic behavior, and it may both inhibit and promote such opportunistic behavior. This study constructs a dual-path integration model in which contract flexibility affects contractor’s opportunistic behavior through trust and conflict. Based on principal-agent theory, building information modeling (BIM) application degree was selected as a moderating variable for the double-edged sword effect of contract flexibility. A partial least-squares structural equation model (SEM) was used to verify hypothesis based on data collected from 271 questionnaires. The results show that contract flexibility has a positive effect on reducing the opportunistic behavior of contractors through trust, and at the same time, it induces the opportunistic behavior of contractors through payment conflicts. Additionally, the BIM application degree does not solve the double-edged sword effect dilemma of contract flexibility, but amplifies the negative effect of contract flexibility, and then induces the contractor’s opportunistic behavior. The findings of this study contribute to the formation mechanism and governance strategy of contract flexibility. It also finds that the use of BIM technology has not achieved the desired effect because the technological development is not synchronized with the development of organizations and people, which provides a new research perspective for the integration of social and technological systems.

The construction industry is increasingly digital and dependent on extensive use of information technologies. However, data exchange in a digital environment makes construction data more vulnerable to cyber risks. For instance, construction videos contain various site information (such as worker privacy, innovative techniques, and infrastructures status), the loss of which may cause financial and safety issues. To ensure the cybersecurity of visual data in construction, this research proposes a deep learning-based image steganography method, which can cover the secret image with an irrelevant image by using a hidden neural network and retrieve the secret image with a reveal neural network. In experiments, a dataset containing 7,000 construction images was used for validating the feasibility of the proposed method. Three evaluation metrics were used to test the performance of proposed method in visual information hiding and recovery. Specifically, the proposed method achieved a peak signal-to-noise ratio of 36.58, a structural similarity index of 97.29%, and a visual information fidelity of 82.57% on average. The test results demonstrate the reliable performance of the proposed method in protecting construction visual data. This research provides a novel way to ensure the cybersecurity of visual data in construction, other than simple password encryptions.

The rise of robot use in the construction industry underscores the need to prepare the next generation of construction professionals for this technological shift. While knowledge about these robots is vital, understanding their operational mechanisms and the safety challenges they pose on construction sites is equally essential. However, implementing an effective robot-related curriculum in construction education is hindered by logistical and financial obstacles of bringing physical robots to classrooms. While traditional lecture-based instruction or videos may offer some assistance, their limited interactive capabilities severely constrain the extent to which students can be effectively trained in working with robots on real job sites. This research introduces RoboSite—a virtual site visit interface utilizing the immersive power of virtual reality, aiming to provide a safe and cost-effective learning platform. In this project, RoboSite was designed to facilitate trust and positive perceptions regarding four-legged robots while concurrently enhancing students’ understanding of the applications, safety challenges, and preventive measures associated with such technologies in construction. The effectiveness of RoboSite was evaluated using repeated-measure experiment design and the results indicate that RoboSite offers a promising avenue to effectively enrich students’ understanding and reduce their negative perceptions about diverse and unfamiliar scenarios.

Accurate identification of structural displacements is important for structural state assessment and performance evaluation. This paper proposes a real-time structural displacement detection model based on computer vision and deep learning. The model consists of three stages: identification, tracking, and displacement resolution. First, the displacement target is identified and tracked by the improved YOLO v7 algorithm and the improved DeepSORT algorithm. Then, the Euclidean distance method based on inverse perspective mapping (IPM-ED) is proposed for the analytical conversion of the displacement. Next, the accuracy and effectiveness of this displacement detection model are evaluated through four groups of bamboo axial compression tests. A comparative analysis is conducted between the IPM-ED displacement analysis method and the commonly used ED displacement analysis method. Finally, the robustness of this method is tested by using a cable breakage test of a cable dome structure as an application case. The research results demonstrate that the maximum average error of the four groups of bamboo displacement tests is only 3.10 mm, and the maximum relative error of peak displacement is only 6.54 mm. The RMSE basically stays around 3.5 mm. The maximum displacement error in the application case is only 4.91 mm, with a maximum MAPE of 4.94%. In addition, the error percentage under the IPM-ED algorithm is basically within 5%, while the error percentage of the ED algorithm is more than 10%. The method in this paper achieves efficient and intelligent identification of structural displacements in a non-contact manner. The proposed method is suitable for environments where the contact displacement sensor is easily affected by vibration, the site is complex and requires additional displacement sensor fixing equipment, the displacement sensor with super-high structure is unsafe to deploy, and the contact displacement sensor in narrow space is inconvenient to deploy, so it has broad application prospects.

In public–private partnership (PPP) projects, private sponsors collaborate to manage and implement project tasks through shareholder agreements and subcontracts. It is important to recognize that relying exclusively on contractual arrangements may not guarantee effective collaboration. Additional informal contractual mechanisms, such as trust and reputation, which are embedded in social connections among sponsors, should be considered. However, the existing literature lacks sufficient information on the relationship between social connections and informal contracts. This study employs social network analysis (SNA) and binomial logistic regression analyses to investigate how highway PPP sponsors form ties in dynamic social networks. Specially, it examines four distinct tie-formation mechanisms, including expertise difference, repeated collaboration, preferential attachment, and multiconnectivity. The findings indicate that the sponsor networks exhibited typical characteristics of small-world networks, such as high clustering and short average distance. Moreover, small-world networks showed an evolutionary trajectory characterized by an inverted U-shaped trend. Binomial logistic regression analyses reveal that repeated collaboration and multiconnectivity significantly impact formation of ties between sponsors in both the growth and decline phases. Preferential attachment has limited effects, whereas the impacts of expertise difference vary during growth and decline phases. This study contributes to the current body of knowledge on PPPs by examining the effects of the dynamic network structure of PPP sponsors on their tie formation strategies during different phases. Particularly, these strategies provide new insight into the origins of informal contractual mechanisms. The findings offer valuable evidence for private sponsors in partner selection and strategy development for long-term market viability. Furthermore, these findings provide direction for governmental intervention in regulating PPP markets.

Abstract

Two key building characteristics, namely the number of stories and typology, is vital across various domains such as construction management and architectural design. These aspects are particularly critical for disaster risk assessment and infrastructure planning. Although deep learning models are adept at extracting this information from Street view images (SVIs), their success is contingent upon the availability of large and diverse data sets with high accuracy. Image augmentation presents an alternative method to artificially broaden data set diversity. However, the impact of image augmentation techniques on identifying building stories and typologies from SVIs has not been adequately explored. This study proposes a methodology employing eight distinct image augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, and translation augmentations—as well as a combined approach using all these methods. The study evaluates the efficacy of models trained with these techniques by comparing the accuracy of different classes and architectures for each task, both with and without the application of augmentation. The findings revealed that while most augmentation methods enhance model accuracy, their effectiveness is task-dependent. Furthermore, it was observed that the most effective augmentation techniques differ among building classes and architectures within each task. This suggests that augmentation strategies need to be custom-designed to align with the unique features of each class and architectures for precise estimation of the number of stories and building typologies. While the focus of this research is on specific tasks, the evaluated augmentation techniques could also extend to related areas, such as ascertaining the age of buildings or identifying window types.

Practical Applications

In this study, the efficacy of augmentation techniques is explored within the framework of identifying the number of stories and building typologies. The models were assessed for average accuracy and class-specific accuracy across various architectures, comparing outcomes with and without the implementation of the proposed augmentation methods. A key finding is that the most effective augmentation method varies between architectures and individual classes. Contrary to common practice in deep learning, where applying multiple augmentation techniques is standard for accuracy enhancement, this study observed that such a strategy did not uniformly improve performance. Specifically, while combining augmentation methods generally resulted in higher average accuracy, this was not the case for some classes within MobileNetV3 when detecting the number of stories. Similarly, for ResNet-152, employing all augmentation techniques together led to the lowest accuracy in certain classes for building typology classification. These results indicate that augmentation strategies may require customization to cater to the distinct characteristics of each class and architecture for accurate estimation of number of stories and building typologies.

Abstract

The operation and maintenance (O&M) period is a critical and resource-intensive phase of the life cycle of railway line infrastructure. The O&M of a railway line during this period requires a large amount of investment and labor. Thus, there is an urgent need for railway managers to seek more efficient management tools and methods to improve management efficiency and reduce maintenance costs. Infrastructure–building information model (I-BIM) technology, characterized by its intuitive representation and high information-integration capabilities, has gained considerable traction with infrastructure industry researchers. In the context of existing railway lines, the absence of corresponding I-BIMs or similar technology owing to the immaturity of technical development at the time led to the use of manual modeling processes, which have limitations such as high cost, slow speed, and low efficiency. This paper presents an innovative, high-speed, semiautomated method for generating I-BIMs for existing railway lines and leveraging asset management data without incurring additional data collection costs. This approach leverages key modules such as line trajectory computation, element model generation, the linking of O&M semantic information, and the visualization of asset health indexes, which can enable the swift and semiautomated creation of an O&M-oriented I-BIM for railway lines. This process elevates traditional two-dimensional database tables into more intuitive, operation-oriented, and semantically rich models. This results in the provision of more efficient support for maintenance management decisions related to railway lines. The proposed method addresses the limitations of manual modeling processes and can pave the way for enhanced O&M efficiency in the railway infrastructure sector.

Practical Applications

The absence of BIMs during the design and construction phase of existing railway lines, coupled with the high costs and error rates associated with manual modeling, constitute substantial barriers to the implementation of BIMs in operation and management. By leveraging the proposed semiautomated modeling approach, which relies on management data, site managers and researchers can rapidly establish basic I-BIMs. Although the asset ledgers and station equipment plans used in this approach originate from National Railway Administration, similar data sets should serve as fundamental inputs for railway line operation and maintenance worldwide. By deploying this method, field managers can enhance traditional two-dimensional (2D) asset ledgers into a three-dimensional (3D) I-BIM, thereby offering a more intuitive understanding of the assets within their jurisdiction. Owing to the integration of geographic information system maps into the model, personnel can directly query asset-related geographic information, potentially reducing the frequency and cost of off-site surveys. For researchers, using this method to establish a real-world model rapidly offers convenient access for further BIM-based applications, such as the integration of real-time data from on-site sensors to formulate digital twins.

There are different interactions and physiological patterns in coordination meetings between non-lean and last planner system (LPS)-based project teams. This phenomenon is the result of the top-down workflow control in the LPS that seems to influence group interaction and physiological processes differently compared to non-lean production planning and control approaches that use a bottom-up workflow control. The current LPS management practices focus on developing performance indicators instead of establishing process-oriented initiatives that optimize the temporal patterns of social interaction and physiological processes. Understanding the differences in behavioral and physiological patterns among non-lean and LPS-based project teams can support non-lean project teams in transitioning to LPS-based social and physiological patterns. Incorporating video recordings and physiological sensors such as electrodermal activity (EDA) can capture group interactions and concurrent physiological responses. The multiuser immersive virtual reality-based LPS simulation game (MILPS) is an innovative simulation tool to investigate these patterns in a controlled environment. This study uses MILPS, EDA sensors, and video recordings to investigate the impact of LPS on social interactions and physiological patterns during coordination meetings. A total of 90 participants, forming 30 groups, participated in a two-round experiment consisting of both non-lean and LPS-based project planning and working tasks. The video and EDA data were analyzed to assess the temporal pattern of interactions and associated shared physiological arousals (SPA) in both rounds. The results indicate that more iterative patterns were observed between interaction phases in LPS-based rounds, whereas more sequential patterns were observed in non-lean rounds. A higher level of SPA was found during the negotiation, identification in LPS-based round compared with the non-lean round. The contribution of this study is twofold: (1) the behavioral and physiological patterns during coordination meetings for both non-lean and LPS-based project teams are characterized; and (2) insights are generated into management and training strategies to facilitate LPS implementation.

Abstract

To enable off-site construction (OSC), architects must finalize construction details in their design deliverables as early as possible, particularly in a building information modeling (BIM) environment, to enhance design constructability and project efficiency. However, most architects lack construction knowledge and practical experience, which impedes their ability to incorporate design-specific construction input (i.e., manufacturing, logistics, assembly, and installation demands) into BIM processes at the early stages. This deficiency can lead to errors, rework, and ultimately increased construction costs. To address this challenge, this study introduces an innovative approach to incorporate constructability knowledge into architectural design processes for BIM-enabled OSC projects. The methodology involved retrospective case studies of two completed OSC projects utilizing a component-centric analysis framework. This analysis resulted in an ontology of construction-specific design features that identified 11 types of constructability issues and the characterization of nine design feature classes that were categorized into three groups: substance; intersection; and composition. These feature classes and their 29 attributes were rigorously characterized from the perspective of BIM-enabled architectural design. This feature-based approach not only extends the BIM vocabulary and semantics from an architectural standpoint but also encapsulates construction knowledge as an integral input to architectural design. Additionally, three strategies were identified, analyzed, and summarized to inform these design features and attributes and to evaluate their effectiveness of addressing constructability issues. The study’s ontological approach assists architects in acquiring a comprehensive understanding of design-specific construction knowledge, enabling them to differentiate, locate, and integrate critical types of constructability information into BIM-enabled architectural design deliverables early in the project delivery process. This paper is a foundational step in the development of automated modeling, algorithms, and constructability assessment for BIM-enabled architectural design deliverables, aimed at achieving a more streamlined construction process.

Practical Applications

The study presents several practical implications for the OSC industry, aimed at bridging the gap between architectural design and actual construction through BIM-enabled architectural design. First, the proposed construction-specific ontology of design features can assist architects in identifying and incorporating essential constructability information into BIM. Second, these design features and their attributes cover a broad spectrum of construction-specific design information, going beyond geometric details to include the semantics associated with constructability constraints and design considerations while exploring alternative design strategies and tools. Third, these design features and attributes act as a structured “construction information checklist,” which systematically guides architects in the early design stages to ensure comprehensive inclusion of all necessary construction-specific design information. Essentially, this checklist offers architects a preliminary method for incorporating constructability knowledge within a BIM environment at the initial phases of the architectural design process for OSC.