Journal of Construction Engineering and Management

System dynamics (SD) applications in high-volume production operations are widely used, helping to define decision rules to reduce costs associated with the variance in planning orders and inventory. The exploitation of SD in engineer-to-order (ETO) project-oriented supply chains—e.g., in construction, shipbuilding, and capital goods—is less well established. Hence, this research reviews the literature that takes a systematic ETO perspective in modeling construction projects, exploiting SD approaches. To comprehensively identify and filter previously published works, we used a keyword searching method using Web of Science and Scopus databases. After applying relevant exclusion criteria, 143 papers were selected. Although previous reviews of ETO literature, more generally, have been done, this work contributes to the body of knowledge by specifically reviewing SD applications in ETO industries and providing insights by creating a categorization system by which to determine existing gaps. Articles are categorized into the classic four phases of a project: aggregated planning, preproject planning, project execution, and postdelivery. Analyses of the methods, attributes, and applications of SD were undertaken for each phase. Findings indicate that SD research covers the range of ETO industries, of which construction is the most dominant, demonstrating SD’s high applicability. The wealth of case-orientated research in the construction field provides a solid foundation for further SD studies in the ETO field. Further research should focus on (1) developing a general ETO archetype for performance benchmarking and strategy development in construction projects; (2) introducing analytical tools, such as control theoretic approaches as found in manufacturing production planning and control design, to improve understanding of the ETO systems’ dynamic behaviors; (3) developing cross-phase, cross-project, design–production integrated, aggregated planning models via hybrid techniques modeling, which can improve understanding of an ETO system’s performance; and (4) improving model fidelity. We also provide a research agenda for each phase of the ETO production.

Despite the well-documented benefits and encouraging policy environment for blockchain technology (BT), its extensive adoption in the construction industry is still unsolved. The interorganizational diffusion of BT is not explored in existing studies, requiring a comprehensive investigation for better government policymaking. Thus, from the technology diffusion perspective, this paper proposes an evolutionary game-based system dynamics (EG-SD) model to describe the complicated relationships between government and construction enterprises in the process of BT adoption. Through theoretical analysis, the main stakeholders, influencing factors, and diffusion channels in the process of BT adoption are identified. Further, a simulation collecting data from China’s construction industry is conducted to evaluate the diffusion performance under different scenarios. This study contributes to the body of knowledge by providing a quantitative perspective for understanding BT adoption behaviors among construction enterprises and revealing the diffusion mechanism of BT from a multilevel perspective. Also, this paper for the first time combines evolutionary game theory with system dynamics into explaining BT adoption, providing practical implications to both government and construction enterprises on policymaking and promoting BT adoption practices.

Work-related musculoskeletal disorders is a serious problem affecting the construction workforce. Pipe workers are subjected to forward bending tasks that cause back injuries. Recent advancements in wearable robotic technologies have led to a growing interest in the use of back-support exoskeletons as a potential solution to reduce the occurrences of back injuries. However, without the willingness of workers to use exoskeletons, the intervention will not be successful in the industry. This study conducted a user assessment of a commercially available passive back-support exoskeleton for pipework in terms of usability, level of perceived discomfort, and subjective perception of the benefits, barriers to adoption, and design modifications. Fourteen pipe workers performed their regular work tasks using a passive back-support exoskeleton and provided feedback on their experience with the device. The results indicate that the exoskeleton is easy to use ($4.13\pm 0.34$) and did not affect workers’ productivity ($2.07\pm 1.22$). Participants reported willingness to use the exoskeleton but raised concerns about the compatibility of the exoskeleton with the safety harness. Reduced perceived discomfort was observed in the lower back. However, there was an increase in discomfort at the chest (20%), thigh (73%), and shoulder (250%). There was a strong correlation ($p<0.05$) between discomfort at the chest, thigh, shoulder, and upper arm and workers’ perception of usability of the exoskeleton. Health benefits such as reduction in stress in the back muscle were reported. Discomfort was experienced while using the exoskeleton in confined spaces. Design modifications, such as the integration of the safety harness and the tool strap with the exoskeleton, were identified. The findings are expected to inspire studies in the area of human-wearable robot interaction and task-specific applications of exoskeletons for construction work.

Having provisions in contracts that incentivize “better” performance has been identified as an effective means to encourage the contractor to perform better. In the construction industry, such incentive-disincentive (I/D) schemes have more widely been used for time, cost, and safety performance goals, they are conspicuous in their absence for quality-related performance. In fact, there is only limited literature available on the latter. This study presents a framework to estimate the quality I/D amounts in construction projects. The relationship between the cost of conformance and a “project defect score” has been used to estimate the I/D amount. The proposed framework for quality I/D has been validated for its effectiveness from the data of three actual recently completed projects. The findings clearly show that the proposed quality I/D scheme helped achieve a win–win situation between client and contractor. Though this study is based on data from the building industry, the proposed framework can be fine-tuned with more data and extended to other areas of construction.

Conflict mitigation is significant for project performance and sustainable relationships in construction subcontracting. The contract, whose design is often characterized by contractual complexity, is among the most important governance mechanisms influencing the behaviors of the general contractor and the subcontractor. However, inconsistent findings still exist in the relationship between contractual complexity (i.e., the explicitness and elaborateness of a contract) and conflict in terms of whether the relationship is “positive” or “negative.” To revisit the relationship in the construction subcontracting context, this study adopts a transaction governance perspective and focuses on the effects of formal rules (reflected as contractual complexity and contractual enforcement) and informal rules (reflected as organizational culture) to propose a research framework. To test the framework, the authors conducted a questionnaire survey, receiving 270 quantitative responses from general contractors and subcontractors. The results show that contractual complexity has U-shaped relationships with task conflict and relationship conflict, respectively. The optimal degrees of contractual complexity for reducing task conflict and relationship conflict are within the moderate range. The increase in contractual enforcement strengthens the curvilinearity of the relationship between contractual complexity and task conflict and moves the optimal degree of contractual complexity to the left. The increase in organizational culture distance weakens the curvilinearity of the respective relationships between contractual complexity and task conflict and relationship conflict, and moves the optimal degrees of contractual complexity to the right. This study contributes to the literature on conflict and transaction governance, and also helps general contractors and subcontractors draft contracts with the optimal degree of complexity for conflict mitigation according to their contractual enforcement and organizational culture distance.

Structural damage detection techniques are gaining widespread attention in construction engineering and management. However, the scarcity of structural damage samples and the cross-task transferability of existing knowledge currently limit this technique in practical applications. Therefore, this paper proposes a novel framework for structural damage detection with large scope of cross-task learning capability that incorporates Bayesian estimation and variational inference into the deep learning backbones and Bayesian weight function into the outer loop process of metalearning. Experimental results demonstrate the superiority of this method for both structural damage image classification and structural damage semantic segmentation. Compared with existing frameworks, the proposed method can alleviate the negative influence of domain bias and reduce computation time and costs due to sample labeling. This paper also discusses how the proposed framework can be used to train a model of the structural damage detection framework in extreme cases. The framework and findings presented in this paper have important theoretical and practical contributions to the literature on vision-based structural damage detection.

Incident reporting has drawn the attention of safety experts in the construction industry to reduce the likelihood of future hazards by learning from previous events. A number of barriers have been identified that inhibit the effective reporting of safety incidents. Adopting an electronic incident reporting system (E-IRS) has immense potential to overcome these barriers and enhance incident reporting frequency in the construction industry. Nonetheless, the successful implementation of E-IRS requires an in-depth understanding of users’ acceptance of the system and the factors influencing its usage. This research employed a cross-sectional design based on the technology acceptance model (TAM) to quantitatively examine the factors influencing the adoption of E-IRS in the construction industry of developing countries. Data were collected using an online questionnaire survey to test the 11 hypothesized relationships. A total of 128 completed responses are retrieved and the results are analyzed using partial least squares-structural equation modeling. The findings indicate that perceived ease of use and trust are critical determinants that influence the acceptance behavior of E-IRS. Facilitating conditions and technological self-efficacy have a significant positive effect on perceived ease of use. In addition, social influence is found to have a significant impact on trust. The outcomes of this study provide a theoretical basis for all the concerned stakeholders to take effective measures to encourage the use of E-IRS in the construction industry, thereby reducing the likelihood of construction-related incidents.

Although research on time–cost tradeoff (TCT) problems has been extensively conducted, there are still few applications of TCT optimization methods in the scheduling of projects with practical size and complexity. As a good attempt, a streamlined optimization algorithm has recently been developed for solving TCT problems with a continuous curvilinear activity time–cost relationship. However, the algorithm encounters the difficulties of high memory requirements and computational demand when it is implemented in complex large-scale projects whose networks may involve tens of millions of paths and hundreds of activities. To overcome such difficulties, this study presents a modified version of the streamlined optimization algorithm. Particularly, a new method for identifying critical paths is proposed in the modified algorithm. Also, a modeling problem has been addressed in order to consolidate the theoretical foundation of the algorithm. As illustrated by the experimental examples, the modified streamlined algorithm outperforms the original algorithm and the commonly used genetic algorithm in terms of optimality, robustness, and computational efficiency. This study thus contributes to the development of commercial scheduling software capable of tackling practical TCT problems.

Freeway maintenance and rehabilitation work usually require closing one or multiple lanes, interrupting traffic flows, and creating queues upstream of the work zone. Public agencies can use queue length as a criterion to determine the maximum duration of lane closures and necessary traffic diversions. Previous studies of estimating queue length due to work zone lane closures are data- and time-intensive. This study presents an efficient approach for estimating queue length estimation due to work zone lane closures by developing various statistical and machine-learning models. The inputs for these queue length estimation models were vehicle demand, lane closure duration, active work zone length, and heavy vehicle percentage. The extent of the queues caused by short-term work zones on freeways for 2-to-1 (one-lane closure on a two-lane freeway), 3-to-1 (one-lane closure on a three-lane freeway), and 3-to-2 (two-lane closure on a three-lane freeway) lane-closure configurations can be estimated with these models. The primary scientific contribution of this study is the applicability of the queue length estimation models in any freeway network with work zone configurations and geometric features such as those used for model development. This research evaluated the efficacy of both statistical and machine-learning models for estimating the queue length considering different work zone scenarios. The accuracy of the queue length estimation models was evaluated for a different network that the original models had not seen previously. Among the statistical models, the quantile regression model had the best accuracy based on mean absolute percentage error (MAPE) for the 2-to-1 lane-closure configuration (88%), and the multiple linear regression had the best accuracy for the 3-to-1 (76%) and 3-to-2 (72%) lane-closure configurations. Among the machine-learning models, the stacking regressor model had the best accuracy for 2-to-1 (95%), 3-to-1 (90%), and 3-to-2 (89%) lane-closure configurations. Based on the analysis, it was observed that machine-learning models performed better than the traditional statistical models in estimating queue lengths.

Build-operate-transfer (BOT) tends to be used in the construction industries for massive transportation infrastructure projects, one of which is highway projects. The compensation for the early termination of BOT highway projects has become the most striking and concerning issue for the enterprise and the government. Although the works of the compensation for early terminating BOT projects are rich in the construction industry, there is an absence of a thorough investigation of bargaining game-theoretic applications in the construction engineering and management (CEM). To develop a reasonable and fair decision mechanism for the compensation for early terminating BOT highway projects in CEM, a valid approach to evaluating compensation is proposed using a bargaining game. An alternating-offer bargaining game model is constructed to analyze the bargaining process, where loss aversion of the enterprise and the government as well as risk of breakdown is considered in the bargaining process. The compensation amount is derived by solving the constructed bargaining game model. It is shown that the compensation amount for the enterprise is related positively to loss aversion of the government and negatively to its own loss aversion. It is shown that the enterprise can obtain more compensation from the risk of breakdown. Finally, the results of the developed bargaining game model are verified by applying them to the Wutong Mountain Tunnel BOT highway project in Shenzhen, China. The constructed bargaining model enables the enterprise and the government to forecast the agreement on compensation amount in CEM. This paper offers an approach to compensation for early terminating BOT highway projects in CEM.

Precast components are a critical part of the life cycle of prefabricated construction. Component joints and connections are essential to structural stability, posing rework problems. The design of precast component joints requires tacit and explicit knowledge. Existing joint design approaches and construction requirements underuse tacit knowledge. This paper proposes a case-based reasoning (CBR) model to support the design of component joints based on this tacit knowledge. A case library of 299 valid precast slabs was used to develop the model. The test cases in the model were used to verify the methodology. Records for 64 test cases with the same building information and 64 test cases from different buildings were used to validate the usability and effectiveness of the model. The results show that the accuracy rates are 100% and 94% when considering the comprehensive performance, and the model is more effective for different buildings than for the same building. The proposed model can improve the efficiency of joint design without expert participation and improve the standardization and coordination of design and construction.

Abstract

Setting up workable budgets symbolizes the competence of state highway agencies (SHAs) in fulfilling their responsibilities, and unreliable cost estimates can cause economic and political complications. The unclear scope definition and scarcity of project information available at early stages make it hard to generate reliable preliminary estimates. Hence, based on the 1,249 projects retrieved from the Florida Department of Transportation (FDOT) database, this research aimed to develop a cost estimation model using statistical learning methods for SHAs to forecast preliminary costs during the early stages of a transportation project to fulfill different cost control and managerial functions. However, the currently used methods have serious limitations. This study introduced alternative statistical learning approaches to the currently most used methods: least absolute shrinkage and selection operator (LASSO) and general regression neural network (GRNN). LASSO regression, for instance, has proved in other areas of science to be remarkably better in terms of variable selection, interpretability, and numerical stability. In addition, this study also accounted for economic factors in model development because economic conditions are influential on highway construction costs but have received limited attention. Using the same dataset, LASSO and GRNN models were developed, and then their performances were evaluated based on a set of criteria, e.g., the mean absolute error and mean absolute percentage error. In comparison to the current practice with state DOTs, this research contributes to the body of knowledge by introducing a series of objective modeling approaches that can prevent human errors, requiring no substantial experience in preliminary estimating. Besides the introduction of statistical learning methods, this study took economic indicators into account when developing the models because they are important factors but have been ignored in previous studies. In addition, these statistical learning methods can produce reliable estimates in a much faster and more consistent fashion, which is critical, particularly considering the massive workload faced by most SHAs and the allowable time to make a preliminary estimate.

Practical Applications

The conventionally used cost-based approach, adopted by most departments of transportation (DOTs) in practice, is time consuming and deeply reliant on estimators’ experience and sound judgments to make dependable estimates. However, subjective judgments of estimators are often inconsistent and unreliable, leading to erratic and erroneous estimates. The increasingly worsening labor shortage compounds the issue because the accuracy of preliminary cost estimates largely depends on estimators’ experience in current practice, requiring a long time to obtain. Therefore, the advantages of this research include the circumvention of human errors because they require no substantial experience in estimating. In addition, these proposed statistical learning methods produce reliable estimates in a much faster and more consistent fashion than the conventional estimating methods, which is critical, particularly considering the massive workload faced by most SHAs and the allowable time to make a preliminary estimate. Based on the 1,249 projects retrieved from the FDOT database, this research aimed to develop a cost estimation model using statistical learning methods for SHAs to forecast preliminary costs during the early stages of a transportation project to fulfill different cost control and managerial functions.

This paper presents an innovative and fully automatic solution of generating as-built computer-aided design (CAD) drawings for landscape architecture (LA) with three dimensional (3D) reality data scanned via drone, camera, and LiDAR. To start with the full pipeline, 2D feature images of ortho-image and elevation-map are converted from the reality data. A deep learning-based light convolutional encoder–decoder was developed, and compared with U-Net (a binary segmentation model), for image pixelwise segmentation to realize automatic site surface classification, object detection, and ground control point identification. Then, the proposed elevation clustering and segmentation algorithms can automatically extract contours for each instance from each surface or object category. Experimental results showed that the developed light model achieved comparable results with U-Net in landing pad segmentation with average intersection over union (IoU) of 0.900 versus 0.969. With the proposed data augmentation strategy, the light model had a testing pixel accuracy of 0.9764 and mean IoU of 0.8922 in the six-class segmentation testing task. Additionally, for surfaces with continuous elevation changes (i.e., ground), the developed algorithm created contours only have an average elevation difference of 1.68 cm compared to dense point clouds using drones and image-based reality data. For objects with discrete elevation changes (i.e., stair treads), the generated contours accurately represent objects’ elevations with zero difference using light detection and ranging (LiDAR) data. The contribution of this research is to develop algorithms that automatically transfer the scanned LA scenes to contours with real-world coordinates to create as-built computer-aided design (CAD) drawings, which can further assist building information modeling (BIM) model creation and inspect the scanned LA scenes with augmented reality. The optimized parameters for the developed algorithms are analyzed and recommended for future applications.

Unsafe behavior is the main cause of accidents in the construction industry. Considering the fact that accidents occur more frequently in small projects and on more hazardous works, this study tests the hypotheses that workers’ safety behavior varies (1) with project size, and (2) with the risk level of work. Survey data were collected from a total of 313 construction workers at 21 sites using questionnaires, and the data were analyzed using a series of factor analyses and structural equation modeling. Multigroup factor analysis revealed that the mean safety behavior value for the workers in large projects was greater than that for the workers in small projects, with a statistically significant difference. This indicates that large-project workers tend to behave more safely than small-project workers. However, no statistically significant difference was found for the workers’ safety behavior with regards to the risk level of work. In addition, the differences in the impacts of three factors (management commitment, supervisor’s role, and workers’ perception of safety) on safety behavior depending on the project size and risk level of work were investigated using multigroup structural equation modeling. Management commitment had the greatest impact on workers’ safety behavior in large projects, whereas workers’ perception of safety had the greatest impact in small projects. Regarding the risk level, management commitment had the greatest impact on safety behavior with low-risk work, and workers’ perception of safety had the greatest impact with high-risk work. These results suggest that management commitment is more important to control worker’s safety behavior to prevent accidents for large projects and low-risk works, whereas the workers’ safety perception is more important to prevent accidents for small projects and high-risk works. This study provides a new theoretical basis for explaining the variations in workers’ safety behavior group to group, and facilitates the development of future safety policies and systems. This study also contributes to the establishment of strategic planning and the justification for spending relevant resources to improve workers’ safety behavior.

High initial cost, mainly due to inefficient construction processes, remains the greatest barrier for residential adoption of solar plus storage (SPS) systems. Modular construction, with a factory-controlled environment and control over the home design, is well suited to address these issues (e.g., efficiencies, waste, inventory control, and quality). Furthermore, these areas have great potential in modular construction to address the high initial cost issue. This study focused on the integration of solar-plus-storage installation upstream in modular housing. Although moving the solar-plus-storage installation process into the factory has many barriers (e.g., change of the scope of work at some workstations without affecting the whole production line, change of quality control, warehousing of solar-plus-storage in correct conditions, change in the supply chain, and required certification of workers), the great potential presented by modular construction methods warrants the study. This study analyzed on-site installation cost and in-factory installation cost through factory information modeling, as well as job safety. Results reveal that integrating solar-plus-storage installation into the production line of modular construction achieved a 37% reduction in installation time compared with onsite installation. Overall, a 21% reduction of total cost could be achieved if solar-plus-storage is installed in-factory. Furthermore, with the in-factory approach, 28% of safety hazards related to solar-plus-storage installation activities are removed. Such an approach could increase solar-plus-storage installation productivity, reduce inspection time, reduce costly on-site rework, and address the solar-plus-storage system affordability issue overall.

The detailed practices for smart contract implementation can be complicated when working under building information modeling (BIM)–enabled projects, especially from the perspective of contract administration. This research aims to explore and determine effective contractual practices and their associated contract provisions for smart contract implementation in BIM-enabled projects. A mixed-methods approach was adopted based on an exploratory case study and a questionnaire survey. The qualitative findings were used to initially identify the core criteria of the contractual aspects associated with smart contracts. Quantitative analysis was then applied to determine the critical contract provisions. This research provides new insights and practical knowledge toward implementing legally sound smart contracts in BIM-enabled projects through the contractual aspects of (1) contractual relationships and obligations, (2) security and privacy, and (3) other specific terms and overall contract structure. A contract framework is also developed for future research and practice of smart contract implementation in BIM-enabled projects.

Reconstruction during the recent conflict in Afghanistan involved extensive construction of public infrastructure alongside an ongoing civil war. This article sheds light on some practical effects on construction projects of political instability, violence, and associated economic and governance conditions in conflict-affected settings in general. It contributes to knowledge on construction in conflict settings by identifying generic relationships that connect prevailing conflict-affected conditions with responding dysfunctional construction project practices. Through interviews with 32 practitioners engaged in construction projects during the recent war in Afghanistan, nine widespread dysfunctional practices were identified: fraudulent testing of materials, corrupt certification, excessive underpricing, fraudulent bidding, partial and complete tender subversion, contract reassignment, inadequate superintendence, and intimidation of superintendents. While these practices that were common in Afghanistan are not exclusive to conflict scenarios, they appear to increase in prevalence amid weakened regulatory systems that characterize conflict-affected situations in general. The expanded prevalence of these practices amidst conflict can crowd-out diligent contracting practices from construction markets. In addition to undermining project outcomes and associated political objectives in the short- and medium-term, potential persistence of such dysfunctional practices has long-term implications for construction industry development and infrastructure investment in places emerging from conflict.