Journal of Construction Engineering and Management

The application of municipal solid waste incineration tailings lightweight aggregate (MSWI-TLA) for reducing carbon emissions, energy consumption, and environmental pollution of bulk solid waste treatment has received extensive attention. As a new type of green lightweight aggregate, MSWI-TLA exhibits considerably good performance and broad prospects; however, its working performance in cement-based materials is not as good as that of ordinary aggregates. In this study, a standardized municipal solid waste incineration tailings lightweight fine aggregate (MSWI-TLFA) with a fineness modulus of 3.1 and a municipal solid waste of 10 mm were utilized as substitutes for the common aggregate. The incineration tailings lightweight coarse aggregate (MSWI-TLCA) with the particle size ranging from 5 to stirring process and the fluidity and filling properties were improved, and stratification was slowed down. Based on the slump, fluidity, and stratification data of municipal solid waste incineration tailings lightweight aggregate specific density concrete (MSWI-TLA-SDC), the internal and external stacking structure model, matrix density three-phase diagram, and stratification function relationship were established, and the formation mechanism of fluidity and homogeneity under the synergistic effect of MSWI-TLA-SDC matrix density was revealed. Simultaneously, the qualitative analysis of homogeneity was carried out intuitively and visualized using computed tomography (CT) technology, and the accurate quantitative analysis model of stratification degree and aggregate separation factor was established using the principle of fluid mechanics, and the optimization effect was verified. The results show that the fluidity of MSWI-TLA-SDC decreases by 3.3%–30.5% and the delamination degree increases by 4.9%–22.9% compared with ordinary concrete. The main reason for the poor fluidity and homogeneity of MSWI-TLA-SDC is the poor density synergy between tailings lightweight aggregate, ordinary aggregate, and mortar matrix. The preabsorbed gravel-type small-size tailings lightweight aggregate along with technical measures like combining water-reducing agent, fine tailings aggregate, and tailings powder can be used to reduce the difference in matrix density and fully exploit the synergistic effects of matrix density. For the closed-loop absorption of tailings lightweight aggregate as well as the technical advancement and promotion of MSWI-TLA-SDC, it is crucial to increase the fluidity and homogeneity of MSWI-TLA-SDC.

An optimal capital structure can minimize financing cost and maximize project value. Most existing studies have focused on the equity-debt financing mode for public–private partnership (PPP) projects, neglecting the usage of mezzanine financing instruments. To bridge the gap, this study constructs a capital structure for urban water PPP projects that uses mezzanine financing called the Urban Water Project Mezzanine Financing (UWPMF) mode. Based on the requirements of sustainability, qualitative and quantitative methods are comprehensively adopted and validate the model using a PPP sewage treatment project in China. The contributions of the study are twofold. First, the introduction of a mezzanine financing tool in the context of a specific industry enriches theoretical research on PPP project financing. Second, sustainability data is included in the capital structure optimization for specific types of PPP projects, providing reference values for promoting the realization of the sustainability goals of PPP projects.

Working within one of the most stressful industries, construction workers face intensive stress loads that adversely affect safety and productivity. While different factors induce stress, time pressure and mental demand are two industry characteristics that can trigger workers’ occupational stress. Although previous studies demonstrated the adverse effects of time pressure and mental demand on workers’ safety performance, no study has empirically investigated the direct impacts of such factors as task stressors on workers’ arousal and stress levels. Further, little is known about the impacts of imposed stress on workers’ situational awareness and risk decision-making. Accordingly, this study evaluates changes in individuals’ situational awareness and decision dynamics when working under stress imposed by time pressure and mental demand. The study simulated a high-risk electrical activity within a multimodel mixed-reality environment and captured participants’ physiological and cognitive responses to measure their stress levels and cognitive processes while completing the electrical task under normal and stressful conditions. Results showed high stress levels triggered by task stressors adversely affect one’s cognitive processes, including attentional distribution: Excessive stress led subjects to miss surrounding hazards, leading to impaired risk perception and increased risk-taking behaviors. The findings of this study provide valuable insights into how the occupational stress imposed by task stressors can disturb workers’ cognitive processes, reduce their situational awareness, and give rise to high-risk behaviors.

Abstract

A major source of risk in green building projects (GBPs) is inaccurate human prediction of the final project cost and duration, which in turn results in cost and schedule overruns (i.e., poor project performance). This paper presents promising new models to mitigate such risk based upon machine learning (ML). Historical data from 198 GBPs in Hong Kong were used to develop and train two fully connected deep neural networks (DNN) models to learn and predict cost and duration, respectively, based on green building rating (GBR) and other project parameters. The models can predict cost and duration with mean absolute percentage error (MAPE) values of 0.07 and 0.09, respectively. They were then integrated with support vector regression (SVR), and results indicated that the integrated DNN-SVR models improve prediction accuracy, decreasing the MAPE from 0.07 to 0.06 (cost) and 0.09 to 0.07 (duration), respectively. The validated models were for the first time deployed as a ML-based web application for automated, fast, and accurate GBP cost and duration prediction. The feature importance analysis results revealed that the most influential parameters on the GBP cost and duration are project area and weather, respectively, not the GBR. Theoretically, the outcomes of this study provide new insights into the impact of GBR on project cost and duration, which are useful for the promotion of GBPs to improve sustainability. Practically, the study provides policymakers and practitioners with novel ML-based models and a web application to improve GBP delivery performance.

Practical Applications

Green building projects help to combat climate change and improve our health, wellbeing, and quality of life, but they face two key challenges in their execution: cost overruns and schedule delays. To address these challenges, there is a need for accurate prediction of the final project cost and duration from the early stages of the project. The practical relevance of this study is in the development of the first data-driven and machine learning-based web application for addressing this need. New integrated optimized machine learning models are developed. For practitioners to have access to and use these models without the need to possess machine learning expertise, a corresponding easy-to-use web application is offered. From the design and construction stages of their project, practitioners only need to input the green building ratings in sustainable site, materials and waste, energy use, water use, health and wellbeing, and innovations and additions they want to achieve for the project. The project type and area (type and size), original budget, planned duration, and start month (SM) and year should also be input. Once this project data input is completed, the web application automatically and instantaneously predicts with a high level of accuracy the total costs and time needed to deliver the project successfully—on time and on budget. These cost and duration prediction outputs are a valuable tool in helping practitioners adjust green building project plans and budgets to develop more realistic and accurate project budgets and programs to avoid cost overruns and schedule delays.

Abstract

State departments of transportation (DOTs) typically use one of three approaches to operate highway rest areas (RAs): in-house workforce, method-based contracting (MBC), and performance-based contracting (PBC). State DOTs spend over $600 million annually to maintain their facilities and often outsource their work to save costs. Although some studies have calculated the annual cost of facilities operated by the three approaches, their findings are not directly comparable. The primary objective of this study is to compute and compare the average annual operating cost of individual RA facilities and the cost of RA data samples (average annual operating cost of a group of facilities in a specific year under a specific approach in a state) produced from these three approaches to achieve a cost-effective alternative. To achieve the objectives, research hypotheses were developed which state there are significant differences in the average annual costs produced by these three approaches. For data analysis, 6,248 cost data were collected from 29 states. From those individual cost data, 198 data samples were generated. Data sets were not normally distributed; thus, nonparametric tests were conducted to determine the group differences. The results show that outsourcing costs under the MBC are significantly less, followed by in-house and the PBC in both cases. The difference between in-house and PBC is also statistically significant. As the primary contribution, this paper investigated the MBC approach as the cost-effective option, and DOT engineers can utilize this approach in operating regular RA facilities to save costs. Caution should be taken when using the study findings in other types of facilities.

Practical Applications

This study suggests that state DOTs can use a cost-effective approach to operate their highway RA facilities for future planning. This study identified MBC as the cost-effective approach when operating regular RA facilities. Transportation professionals can directly implement the study findings to regular RA facilities; however, they should take caution when utilizing the findings in the other two types of RA facilities––welcome center and seasonal facilities––which were not considered in this study because their key features (square footage, annual visitors, and full-time employees) are not comparable to regular facilities. If the agencies have key feature data and they can normalize these features when calculating the average annual cost of facilities, then they can implement the findings for welcome center and seasonal RA facilities as well. In addition, the agencies should be aware of adjusting for inflation and location. Using the cost-effective approach may also reduce RA facility closures in the future, which assists in reducing the shortage of national truck parking issues and decreasing fatigue-related highway accidents.

The lighting system as a crucial urban infrastructure needs to be maintained to uphold good performance. The planning and scheduling of maintenance for geographically distributed lighting systems is a complex research problem as well as a challenge in practice due to resource constraints and the complexity of route planning. This study proposes a multiple-objective decision-support model (MODSM) for lighting maintenance routing, which considers both the carbon emissions generated during the maintenance process and the loss of efficiency due to workload imbalance among maintenance crews. In the proposed model, carbon emissions are converted into a carbon tax index as part of the total cost index (along with the pure cost index), while the workload balance is measured by the standard deviation of workload indexes among the crews. To address the identified problem, an improved genetic algorithm is used to identify the optimal solution for group relamping. A numerical example is provided to demonstrate the suitability of the developed model to the selection of an optimal maintenance scheme, including the number of maintenance crews and their corresponding maintenance routes. Finally, the performance of the proposed model is assessed by sensitivity analysis and comparison with the traditional method. Integrating the interests of multiple stakeholders and environmental protection, the MODSM for lighting maintenance routing developed in this study can provide a helpful tool for researchers and practitioners in the construction engineering and management community to formulate a more comprehensive decision into maintenance management.

Abstract

Effective multiskill resource scheduling requires comprehensive workforce assignments. Drawing on the multiskill project scheduling problem perspective, this study investigates the integrated problem of project scheduling and multiskilled staffing based on a more realistic perspective that human skill level is dynamic and has a variable activity duration caused by the learning effect. An integer linear programming model is innovatively constructed to solve the problem directly. Moreover, an adapted tabu search algorithm with six resource-oriented priority strategies is designed to solve large-scale problems efficiently. Based on a realistic construction project case and numerical instances, comprehensive computational experiments are conducted to validate the proposed algorithm. The findings reveal the impact of different resource strategies on project duration and personal skill growth, particularly through the optimization of project scheduling according to the dynamic scenarios. The research extends conventional project scheduling knowledge and its methods by solving complex scheduling problems for improved project objectives and employee value. In practice, the model and the algorithm can be integrated into the project management information system for strategizing a plan of project scheduling and staff assignment proactively.

Practical Applications

This study develops a linear optimizing model and an efficient tabu search algorithm for the integrated problem of project scheduling and staffing with multiskilled personnel in construction project management. The model and the proposed algorithm can be applied to advance any digitalized scheduling platforms; further, project managers can be supported to flexibly select appropriate methods to deal with resource problems under different situations, workforce types, and time–skill development trade-off. For small-scale problems, users can simply input the model parameters, including information of project, activity, and employees; then, the optimal integrated scheme of activity executing time, staffing, and applied skill type can be obtained automatically through IBM ILOG CPLEX Optimizer software methods. For large-scale problems, after imputing problem parameters and setting the algorithm parameters, such as the resource allocation strategy and iteration number, the tabu search algorithm can be implemented to yield a satisfactory and efficient integrated scheme. In summary, this work provides project construction planners or managers with an innovative decision-support tool for solving complex but practical scheduling problems.

Abstract

Ensuring material availability and reducing material waste are two crucial aspects of material management to improve construction project performance and sustainability. Past studies have illustrated several material management practices without revealing their effect on material availability and waste reduction. This study fills these research gaps and aims to explore important material management practices by examining their impact on material availability and waste reduction in construction projects. This study identified 23 material management practices from a literature review and collected 107 responses from a questionnaire survey. An underlying structure of the practices was established, and factors were extracted from the exploratory factor analysis. A structural equation model was then developed to explore their influence on material availability and waste reduction. The results revealed six critical factors and associated practices that significantly and positively affect material availability and waste reduction: System for procurement and storage was found to have the highest impact, followed by on-site material control, procurement quality, procurement costing, central procurement database, and material planning systems. By revealing the underlying structure of the practices and establishing their positive influence on material availability and waste reduction, the present study adds value to existing knowledge. Construction practitioners can focus on implementing these crucial practices to ensure material availability and reduce material waste, which will enhance the project’s success and sustainability.

Practical Applications

Construction projects often suffer from nonavailability of materials and waste of materials at construction sites. It is vital to know the important material management practices that can reduce these problems. Hypotheses are developed in this study to explore the effect of material management practices on material availability and waste reduction. This study identified six critical material management factors (systems for procurement and storage, on-site material control, procurement quality, procurement costing, central procurement database, and material planning) that can be managed to improve material management in construction. Implementing these factors and associated practices will enhance material availability and reduce material waste in construction projects which will ultimately support completing the projects within the scheduled time and budgeted cost and promote sustainable construction.

Abstract

The Belt and Road (B&R) initiative creates a highly competitive and dynamic environment and cooperation network for construction projects in specific regions. By using the experience of other countries, construction professionals can be well-equipped with management skills for solving intractable problems during the project management process of a construction project. Value management (VM) is a systematic team decision-making methodology used to achieve the best project value, but it is rarely applied in developing regions along the B&R. This study aims to identify critical success factors for the implementation of VM in construction projects in both developed and developing regions along the B&R. Six focus groups targeting three groups of regions were conducted, including advanced VM developed regions (Regions 1), advanced VM developing regions (Regions 2), and unadvanced VM developing regions (Regions 3). Three main groups of critical success factors were identified by using contextual analysis: technical process, team behaviors, and outcomes. By comparing VM in advanced VM regions (Regions 1 and Regions 2) and traditional team decision-making in unadvanced VM regions (Regions 3), the study revealed that (1) practitioners in Regions 1 applied VM to achieve project success with a logical process and positive team behaviors, although several problems were also encountered; (2) surprisingly, in some technical respects, VM practitioners in Region 2 performed better than those in Regions 1, but positive team behaviors were still not emphasized; and (3) inefficient traditional decision-making activities with illogical technical processes and negative team behaviors in Regions 3 often led to poor project outcomes. A propositional model is established based on the identified critical success factors and comparison results among the three groups of regions. Finally, several practical recommendations are proposed to ensure effective VM application and promotion in both developed and developing regions along the B&R.

Practical Applications

In the developing countries that participated in the Belt and Road initiative, poor performance is not uncommon for megaconstruction projects with multiple stakeholders due to poor problem-solving strategies. This research revealed that value management practitioners in developed regions could achieve good project outcomes by applying value management with a logical framework and positive team behaviors, but they have not yet found a good way to resolve problems in terms of time constraints, no follow-up actions, and duplicate information from the public. VM practitioners in some of the developing regions put much effort into VM application, but the poor function analysis and insufficient conflict management, commitment, and transparency still impacted the final project outcomes negatively. Various problems such as no information analysis, serious corruption, and limited participation were encountered in illogical traditional team decision-making activities in developing regions, which significantly destroyed stakeholder outcomes and project performance. This research explores the insufficient value management practices in both developed and developing regions along the B&R, which provides empirical evidence to constriction professionals to update their VM/traditional team decision-making strategies and find out the best ways to conduct VM logically and effectively for improving final project outcomes.

Abstract

The growing prevalence of modular construction, while it offers benefits in terms of productivity and sustainability, has led to increased use of heavy mobile cranes and related resources on construction sites. One significant resource associated with crane use is the crane mat, which offers practical mobile crane ground support against poor soil-bearing capacity. Due to increased use of crane mats, crane mat layout plans/drawings have become increasingly significant in today’s construction industry. The present work describes an automated crane mat optimization framework for preparing crane mat layout plans/drawings built on an agent-based greedy algorithm and reinforcement learning. The proposed framework employs these approaches to achieve the maximum area with the minimum number of crane mats. The proposed framework is found to decrease the time required for preparing a crane mat layout plan/drawing (approximately 97% time saving) with more uniform and efficient crane mat planning outcomes (approximately 63% crane mat material reduction).

Practical Applications

The research presented in this manuscript examined the largely unexplored topics of crane mat layout optimization and layout preparation, proposing an agent-based greedy algorithm and reinforcement learning (RL) approach for automated crane mat layout optimization as an innovative approach to developing sustainable crane mat layouts. This approach takes into account the site constraints and can be applied to mitigate crane mat crowding on construction sites. Crane mat optimization is applied using both methods (greedy and RL) to achieve the maximum area covered with the minimum number of crane mats used. The results demonstrate that the practitioner time spent preparing a crane mat layout plan/drawing can be reduced considerably, with more uniform and cost-effective crane mat optimization outcomes. Another outcome of this research is that developed approaches reduce the crane mat requirements compared with the outputs generated by the manual approach, thereby reducing the ${\mathrm{CO}}_2$ emissions associated with crane mat manufacturing and usage.

Multiple tower crane layout planning (MTCLP) refers to the selection of the types, quantities, and positions of tower cranes and material storage positions. In a construction project, choosing tower cranes with a larger covering radius and lifting capacity can reduce the transportation time of materials. However, it can also simultaneously raise the rental cost and the risk of crane crashes. Determining an optimal multiple tower crane layout plan to achieve high construction efficiency with low cost is still a challenge. Mathematically, MTCLP is a complex combinatorial problem controlled by a number of variables, such as site configuration, building layout, material storage positions, and workload. To precisely describe irregular shaped sites and buildings, this research partitioned the site into unit cells and proposed a cell-based optimization model. For the optimal crane layout plan, the problem was formulated as a mixed integer linear problem (MILP). The model’s mathematical constraints comprised coverage of the given positions, the crane’s safety distance, sufficient crane capacity, and the utilization ratio. The objectives were to increase the crane coverage ratio while reducing the cost and the working areas overlapped by more than two cranes. Two case studies are presented to demonstrate the effectiveness of the model. The first construction project consisted of seven buildings with irregular shapes. By strictly forbidding the areas to be overlapped by more than three tower cranes, the model successfully obtained the optimal crane layout plan using the Gurobi Optimizer. Compared with the contractor’s layout plan, the optimal plan reduced the rental cost by 10.7% and the area overlapped by more than three tower cranes by 4.56%. In the second project, alternative plans were identified by the Gurobi Optimizer and $\epsilon$-constraint method to enhance confidence in selecting the ultimate crane layout plan. The optimization procedure involved (1) optimizing the layout plan with minimum crane cost and maximum coverage ratio, (2) identifying all possible layouts as the Pareto fronts when the coverage ratio could not be raised without increasing the crane cost, and (3) minimizing the areas of crane overlap covered by more than three tower cranes. The effectiveness of the proposed model is also proved by comparing the differences in layout plans caused by “regular” and “urgent” lifting demands.

State highway agencies (SHAs) often need to cluster or bundle projects in accordance with their scope similarity for various construction management tasks, including historical data-driven time, cost estimating, and project bundling. Conventionally, SHAs categorize similar projects into work types based on subjective judgment about the similarity between major pay items. A few quantitative methods for project similarity determination are found in the literature, but they mostly use one single source of information, either the cost contribution of pay items or the keywords of pay items descriptions, for measuring project similarity. This paper presents the first attempt to integrate multiple information sources for project similarity measurement. This research proposes a novel cost-weighted term frequency-inverse document frequency (CW-TF-IDF) method that incorporates the cost information of pay items into the traditional TF-IDF word embedding method to measure project similarity. The effectiveness of the proposed method in supporting project clustering and bundling was tested using the historical bid data collected from an SHA. The findings showed that the CW-TF-IDF method significantly improves project clustering performance compared to the most recent state-of-the-art method. The CW-TF-IDF method also showed its outperformance in project bundling as it yielded a cosine similarity of over 0.9 for most of the bundled projects in the testing data. This proposed method is expected to help SHAs accurately identify similar projects and eventually improve their project management effectiveness.

The digital revolution has swept the world, but the construction industry, the backbone of the global economy, has lagged. Government policy is expected to promote the development of digital technologies centered around building information modeling (BIM) in the construction industry. However, how digital technology–related policies diffuse within a country and influence innovation regionally remains unknown. Using 2011–2019 panel data from 31 provinces in mainland China, we found that BIM policy diffuses not only vertically among national and regional governments, but also horizontally among neighboring regional governments, both of which accelerate the adoption of BIM policy in various regions across the country. We also found that there is a causal relationship between regional BIM policies and related BIM innovation. Further, the positive influence of policies on innovation is more significant in regions with low economic development and high openness levels. This study not only contributes to understanding the impact of policy diffusion on BIM innovation, but also offers insights into the research on policies and innovation of digital technology in the construction industry from a macro perspective. The empirical evidence provides a practical reference for policymakers to accelerate the adoption of digital technology policy within the country and promote technological innovation.