Journal of Construction Engineering and Management

Many countries and regions consider off-site construction a modern construction method that facilitates sustainability in the construction industry. Sustainability research in off-site construction (SROSC) is crucial for its development and is a nonnegligible part of its management. However, an in-depth understanding and critical analysis of SROSC to summarize recent research and inform future research directions are lacking. In this study, we address this issue by offering a mixed-review method integrating scientometric analysis and systematic review to explore state-of-the-art SROSC. We aim to uncover sustainability themes and topics, distinguish research trends, and identify gaps in knowledge that can be addressed in future research. Scientometric analysis was used to perform statistical analysis and visual map research on the knowledge landscape formed by 272 related studies of SROSC. The systematic review was used to identify and analyze six major knowledge themes (e.g., environmental, economic, social, decision-making, optimization, and industry management) and 21 knowledge topics for SROSC. We proposed possible future research directions based on the resulting structured body of knowledge. This research contributes to the body of knowledge by visualizing and analyzing the state-of-the-art of SROSC, as well as identifying the future research directions in this area to improve architectural, engineering, and construction practices.

The construction engineering and management (CEM) domain frequently meets complex tasks due to the unavoidable complicated operation environments and the involvement of numerous workers. Being able to simulate these tasks with promised designed goals, reinforcement learning (RL) can help CEM engineers reach enhanced strategies in multi-/single-objective sequential decision-making under various sources of uncertainties. To provide a better understanding of the status quo of the RL application in CEM and its potential benefits with their strengths and limitations, this study systematically reviewed 85 CEM-related RL-based studies as a result of queries from three main scientific databases, namely Scopus, Science Direct, and Web of Science. The results of this review reveal that researchers have been increasingly applying RL methods in CEM domains, such as building energy management, infrastructure management, construction machinery, and even safety in the last few years. Our analysis showed that the reviewed papers are associated with different limitations such as generalizability, justification of selecting the approaches, and validation. This review paper alongside the presented overview of the RL methodology can assist researchers and practitioners in CEM with (1) gaining a high-level and intuitive understanding of RL algorithms, (2) identifying previous and possible future opportunities for applying RL in complex decision-making, and (3) fine tuning, proper validation, and optimizing to-be-developed RL frameworks in their future studies and applications.

Megaproject social responsibility (MSR) has received a great deal of attention from both academics and practitioners. However, as a very broad and complex concept, MSR requires an in-depth investigation of its components, driving forces, and contingent factors. Thus, this study aims to explore the climbing process across different levels of MSR from the perspectives of external stakeholders and project complexity. This study first establishes a pyramid framework for analyzing the different components and levels of MSR by leveraging stakeholder theory. Then, drawing upon attention- and capability-based views, theoretical development and empirical analyses are carried out to validate the influence of external stakeholders on participating organizations’ MSR and the moderating effects of project complexity. Using a set of survey data from Chinese megaprojects, the empirical findings demonstrate that the positive influence of external stakeholders and the negative moderating effect of project complexity become salient when ascending the pyramid of MSR. The value of this study lies in the way in which it considers the climbing process based on a hierarchical framework of MSR. The theoretical framework and empirical findings offer both project managers and policy makers with new insights into how to govern the diverse social responsibility issues in megaproject construction and management.

With increased prefabrication in the construction industry, fabrication workers are tasked to assemble more complicated assemblies with tighter tolerances. However, the existing measurement tools and processes have not changed to accommodate this shift. Lack of advanced measurement tools and existing processes results in increased risk of late detection of geometric errors. To reduce these risks, three-dimensional (3D) quality control systems leveraging scan-vs-BIM methods can be adopted as part of the fabrication process. However, these systems have not been widely adopted yet by fabrication shops, because: (1) fabrication shops often do not have 3D models corresponding to shop drawings; and (2) the cost of integrating accurate 3D scanning equipment into fabrication workflows is assumed to be too high. To remove the first barrier, in this article, a framework for developing 3D digital templates is developed for inspecting received parts. The framework is used for developing a library of 600 3D-models of piping parts. The library is leveraged to deploy a 3D quality control system that was then tested in an industrial scale case study. The results of the case study are used to develop a discrete event simulation model. The simulation results from the model and subsequent cost benefit analysis show that investment in integrating the scan-vs-3D-model quality control systems can have significant cost savings and provide a payback period of less than two years.

While public-private partnership (PPP) is not an entirely new development in US procurement, the public and private sectors have adopted and applied it slowly, placing the United States behind the PPP global transportation market. Despite research efforts made in different aspects of the PPP delivery method, it is not yet clear whether the PPP delivery method can lead to better performance compared to the widely used design-build (DB) delivery method, particularly when applied to US highway projects. For this paper, the cost and schedule performance of 22 PPP and 122 DB highway projects were statistically compared to determine whether the performance of PPP highway projects was significantly better than DB highway projects. For comparison purposes, two scenarios were created based on the project’s completion date. The first included highway projects completed between 1995 and 2020 (22 PPPs and 122 DBs), and the second included all PPP projects and DB projects completed by 2007 (22 PPPs and 25 DBs). For the first scenario, the study shows that the mean construction intensity of PPP projects was significantly higher (about fivefold) than that of the DB projects ($1,300,000/day versus $267,000/day). This finding also applies to the second scenario; however, the difference was found to be reduced to threefold. In addition, the cost growth of PPP highway projects was significantly lower than DB highway projects in the second scenario ($-0.04\%$ versus 2.84%). Regarding schedule performance, no significant difference was found in either scenario. This study seeks to determine that the PPP delivery method fast tracks the design and construction of highway projects, as well as saves the overall project costs compared to the DB method. The main contribution of this study to the body of knowledge is that the PPP delivery method outperforms DB in terms of construction intensity and cost growth. This study will also assist US highway agencies and industry practitioners in better selecting an appropriate delivery method based on their key objectives and priorities for future projects.

Being one of the most common dispute causes in construction projects, the delay is extremely important to be resolved promptly. To resolve delay-related disputes, delay analysis that is conducted to determine the amount of the delay that has occurred on a project together with its contractual responsible party is of paramount importance. Notwithstanding the numerous enhancements in the delay analysis domain, delay resolution remains one of the most challenging issues in construction projects due to various drawbacks of existing delay analysis methods. To minimize the delay-related issues in construction projects, this study, therefore, aims to develop a new delay analysis method that overcomes the drawbacks of existing delay analysis methods. The drawbacks of current methods were initially determined via an in-depth literature review and a focus group discussion (FGD) session conducted with seven experts experienced in the delay analysis domain. This step revealed that most of these methods ignore some critical features, such as consideration of critical path (CP), actual progress data, and fluctuations on CP. Moreover, most of the methods lack detailed numerical procedures and consider only the owner’s delay. Furthermore, some types of activity relationships, actual improvements, and further delays made by contractors on the planned excusable compensable delay (ECD) and excusable noncompensable delay (END) are usually ignored by these methods. Then, a new delay analysis method, namely modified schedule versus modified updated schedule (MSvsMUS), was developed. To overcome the detected drawbacks, the proposed method is designed and developed in a way that can consider various activity relationships among the critical activities, include detailed numerical equations, and compute the variations between the modified schedule (MS) including planned fragnet/s and modified updated schedule (MUS) including actualized fragnet/s periodically. This was then followed by a case study conducted to demonstrate the method’s mechanism. Finally, the developed method was evaluated and validated by conducting a second FDG session and a hypothetical case study. Results demonstrated that MSvsMUS has considerable potential to resolve delays and delay-related disputes escalated in construction projects.

The public-private partnership (PPP) model has been widely used in infrastructure projects around the world to alleviate government shortages of funding and practical experience. Although the early termination of PPP projects frequently occurs in different parts of the world and brings huge economic losses, there are few relevant studies on its influencing factors. Most of influencing factor identification models are subjective and do not consider dependencies and feedback relationships between influencing factors. Using the data of 113 PPP projects, this paper proposes a multicase grounded theory and an interpretative structural model (ISM) to identify the key influencing factors of early termination and to analyze the hierarchical structure among influencing factors. The results show that there are 53 factors leading to the early termination of PPP projects, which can be divided into 19 main categories and 7 levels. Among them, political environment and natural environment are the most fundamental factors. In addition, a 3-level coping strategy is developed based on the constructed multilevel hierarchical model. The proposed methodology can be applied to risk analysis of early termination of PPP projects, and can help decision makers formulate strategies by considering the interaction of influencing factors.

Abstract

Classical scheduling techniques are well-known to underestimate the average project duration, yet they remain widely used in practice due to their simplicity. In this paper, the new Critical Duration Index (CDI) is proposed. This index indirectly allows anticipation for the probability of a project ending late, as well as the average project duration extension compared with a deterministic project duration estimate. The accuracy of two simple regression expressions that use the CDI was tested on two representative data sets of 4,100 artificial and 108 empirical (real) projects. Results show that these regression expressions outperformed the only alternative index found in the literature. Besides allowing enhanced forecasting possibilities, calculating the CDI only requires basic scheduling information that is available at the planning stage. It can thus be easily adopted by project managers to improve their project duration estimates over prior deterministic techniques.

Practical Applications

Classical scheduling techniques like Gantt charts or the critical path method are known to underestimate the project duration. However, they remain widely used in practice due to their simplicity. In this paper, we have proposed the Critical Duration Index (CDI). This index allows anticipation for the probability of a project ending later than the estimate produced with a Gantt chart or the critical path method. It also allows an estimation for how much longer the project might take to be completed, that is, the extension of the delay. The accuracy of two simple regression expressions that use the CDI was tested on two representative artificial and real project data sets. Our results show that these regression expressions outperformed the only alternative index found in the literature. Besides allowing one to forecast the probability and extension of a project delay, calculating the CDI only requires basic scheduling information that is available at the planning stage. Hence, it can be easily adopted by project managers to improve their project duration estimates over other deterministic techniques.

Methods to collect data in construction engineering and management (CEM) research are evolving, informed by recent technological advancements. One such method is research charrettes that allow effective interactions and knowledge sharing between expert industry practitioners and academic researchers, all colocated in a single venue, enabling rich data collection and live communication. A pivot point in technological evolution occurred with the COVID-19 pandemic, forcing a global shift to remote work. Hence, planned in-person research charrettes had to shift to remote sessions, relying on virtual conferencing platforms and online data collection mechanisms. Technology-enabled charrettes have allowed the authors to collect significantly richer data sets and ensure a more diverse representation of participants, while saving tremendous amounts of time. With the continuing emergence of technological applications, the world might not go back to functioning fully in person. The authors believe remote research charrettes (RRCs) will still be used in a post-COVID-19 world because of their superior performance. This paper builds on a previous publication that described traditional research charrettes as a method to enhance CEM research a decade ago; it offers a significantly updated and improved RRC method based on the knowledge gained from transitioning a dozen in-person charrettes into RRCs. It also presents performance comparisons between RRCs and traditional charrettes by quantifying metrics indicating how RRCs are more time-efficient and cost-saving, harness more participants from more diverse locations, and enable the collection of richer data sets and four times more industry comments and expert feedback. This paper also provides guidance on the integration of technology with traditional research charrettes, hence contributing to the CEM body of knowledge.

Effective handling of project risks is an important part of project risk management. Various methods have been proposed to determine the optimal set of risk-response actions (RRAs) to mitigate the project risks. However, few existing methods incorporate resource planning into their selected RRAs. This paper integrates the risk-response problem with the resource-constrained project scheduling problem (RCPSP) and presents a method for allocating and scheduling the limited risk-related (RR) resources to provide project managers with an effective and feasible project risk-response strategy. Based on the elaboration of the relationships between the risk, the RRA, and the RR resource, a mixed-integer optimization model is developed to obtain the optimal risk-response strategy and the consequent project scheduling. A full-factorial numerical experiment is conducted to validate the optimization model and investigate the effects of risk-response efficiency and risk severity on project make-span and total cost. In addition, a model comparison analysis is presented to highlight the necessity of incorporating resource planning. The computational results show that, as expected, resource utilization can be improved by incorporating allocation and scheduling in the risk-response procedure. Also, the study suggests that project managers should avoid RR resource consumption and cost increases caused by excessive risk response in specific circumstances.

Robotic excavators commonly are used in industrial and construction tasks to improve efficiency and shorten construction periods, but they have high energy consumption and vibration and shock problems, the greatest of which is impact phenomenon in contact with construction environment. A perfect construction task with low vibration and high precision can be achieved only by skilled operators, and requires very intense concentration. Due to the urgent labor shortage caused by the pandemic, tight schedules, and increasingly complex construction projects, it is important for construction management to have an intelligent control method that combines the force and position of excavators in the construction progress. This study developed an adaptive impedance control method to realize coordinated control of force and position. An adaptive impedance control was designed to adjust the impedance parameters indirectly. Three different simulations were conducted to compare the force tracking performance of the classic impedance control and the proposed adaptive impedance control, namely, environmental location changes, environmental stiffness changes, and dynamic force tracking. Blended contact surfaces were designed to validate the performance of the control algorithms. Results show that the proposed adaptive impedance control effectively improved the force tracking accuracy. This paper offers an original contribution to the intelligent control method of excavator construction, and will be of use to operators, especially in large ground, mine, earthquake, and other complex ground construction sites. In addition, the research also provides construction manager and other practitioners with an impedance control method in construction project management that can save labor cost, improve production efficiency, and accelerate the transformation to a digital and intelligent construction industry.

Increasingly demanding project requirements, stringent regulatory environments, and the urgent need to create a positive industry climate all exert great pressure on construction companies. Compared with other factors, some pressures have been proven to have a significant positive or negative impact on company compliance. Thus, while previous studies have identified the causes of rule violations in the construction industry, whether the specific pressures faced by these companies as representatives of project-based organizations drive them to violate the rules remains to be further investigated. In response, this study investigated the effects of three types of pressure—project, regulatory, and reputational—on the compliance attitudes of construction companies. The study also explored the moderating effects of three organizational ethical climates (OECs)—caring, law and code, and instrumental OECs—within these relationships. The results of a partial least-squares structural equation model (PLS-SEM) analysis of data provided by 209 construction practitioners indicate that project pressure negatively affects construction company compliance attitudes and that instrumental OEC strengthens this relationship. In addition, regulatory pressure has a positive effect on compliance attitudes, and this relationship is positively moderated by law and code OEC. Moreover, reputational pressure positively affects compliance attitudes, and this relationship becomes more pronounced with increasing levels of caring OEC. This study offers a novel perspective on pressure in the construction sector which helps explain why construction companies violate the rules and which provides new insights for future compliance governance systems. The authors also offer suggestions for construction companies for addressing possible motivations for rule violations that stem from pressure by establishing appropriate OECs.

The dynamic nature of construction projects has made teams the best approach for responding to changing project needs. As a result, the performance of the project engineering team, which has the responsibility to manage and supervise the construction process at the site, has an undeniable effect on achieving project goals. Recent studies showed that the level of fit individuals perceive between themselves and their team highly affects their performance. However, it has remained underresearched, especially in developing team selection models, due to the complexity of quantifying influential qualitative factors. A system-dynamics approach is employed in this paper to gain a better insight into the effects of person–team fit (PTF) on project performance (PP) due to the complexity of the parameters engaged in this process. The results indicate that team members’ low levels of fit significantly increase schedule delays and errors, leading to severe cost overruns and failure in project cost performance. The model also demonstrates how policies like overtime work or rewards are helpful to compensate for delays and improve project performance. This study adds to the body of knowledge by offering new insights and contributes toward a greater understanding of failure in team selection and its impact on PP. Also, using the system-dynamics approach to simulate this process, which is previously missing in the literature, supports the project manager’s decisions by considering multiple qualitative and quantitative factors affecting the project performance. The model suggests that adjusting PTF would be an alternative fundamental policy to prevent severe deviations from the baselines. It helps a project manager (PM) understand how each aspect of PTF would affect PP. Also, it highlights the importance of supplementary fit (SF) and discourages managers from traditional competency-based team selection.

Extreme wind speeds pose a serious threat to tower crane stability and can lead to excessive deflections or overturning of the mast. However, wind effects might not be considered directly in either the tower crane stability analysis or in the selection of a tower crane and use location. This problem may lead to crane failures or incidents due to extreme weather or improper selection of the crane foundation. To address these limitations, the present study proposes an automated system that includes: (1) mathematical models to determine the locations of the tower crane and the material supply, seeking to minimize not only lifting moments but also tower crane travel times; and (2) a static wind-based tower crane stability analysis to support the selection of a tower crane, which is the dimensions of its foundation that produce the most stable crane foundation system. A case study including 514 crane and material supply locations is adopted to validate the effectiveness of the proposed methodology.

Predicting the productivity of microtunneling construction projects is challenging, due to the complexities of this trenchless excavation method. One of these complexities is estimating the microtunnel boring machine (MTBM) penetration rate due to the complex nature of the interactions between the MTBM and the ground. In the present study, a novel mechanistic approach based on the theory of contact mechanics is proposed to determine the underlying mechanics of the MTBM penetration rate. Using the proposed mechanistic approach, an analytical model of the MTBM penetration rate is developed, and a mechanistic relationship between the MTBM penetration rate and its influential factors, namely soil properties, operational loads, and cutterhead characteristics, is established. The proposed approach is expected to provide substantial mechanistic insight with respect to MTBM penetration rates by (1) modeling penetration rates of MTBMs into soils, (2) identifying the factors that influence penetration rates based on a fundamental theoretical approach, and (3) providing a useful tool for evaluating MTBM penetration rates based on the combined influences of ground properties, operational loads, and cutterhead characteristics.