Journal of Construction Engineering and Management

Because of the importance of budget planning and contract bidding, accurate prediction of future movements of the construction cost index (CCI) has been a crucial part of construction cost management. Despite the fact that construction costs can vary widely across locations with different market conditions and environments, the national CCI, the simple average of construction costs for 20 US metropolitan areas, has often been used to forecast CCIs across the nation. This study finds considerable differences across US cities in both the level and growth rates of CCIs and shows that using the national CCI for predicting city-level CCIs can cause nonnegligible forecast errors. Comparing four popular linear forecasting models, including standard autoregressive integrated moving average (ARIMA) models and a multivariate vector error correction model (VECM) drawn on monthly CCI data from January 1995 to December 2019, this study reveals that no single model is dominant in terms of the out-of-sample performance. If any leading indicator is available at the city level, however, it is recommended to choose between the ARIMA model based on the Bayesian information criterion (BIC) rule and the multivariate VECM augmented with the leading indicators. For the leading indicator in multivariate VECM, city consumer price index (CPI) works better than national CPI. In the absence of such leading indicators, it is recommended to use a parsimonious ARIMA model based on the BIC rule. The proposed approach is expected to help decision makers in the construction industry prepare more accurate budgets and biddings for regional construction projects.

Projects with poor noncompete provision design have caused heavy costs for both public and private organizations, primarily due to the embedded monopoly right of the private sector to collect toll revenue. Prior studies have investigated the financial impact of including noncompete provisions in public-private partnership (PPP) contracts to ensure project attractiveness to private entities, although the potential impact of such provisions on the public interest has not been well documented. This study contributes to the literature by incorporating social benefits into an evaluation model to balance the protection of public and private interests. Three contract design alternatives—strict noncompete, compensation for competing facilities, and compete without compensation—are evaluated and compared. This study demonstrates these alternatives with the I-77 Express Lane project in North Carolina and provides several insightful results. First, a strict noncompete provision often worsens congestion, negatively affecting the public interest, and should be avoided. Second, both public and private organizations may benefit from inclusion of a compensation provision that eliminates a prospective monopoly while offering compensation for competing facilities. Third, public benefits from competing facilities present a convex curve over time, suggesting optimal timing for the government to build the competing facility. The results illustrate the significant effect of the noncompete provision on the public interest, and provide governments with a reference to balance protection of the public interest and attractiveness to private investors when designing the noncompete provision.

Quality and safety are important and are the leading concerns in a construction project. Planning a project without properly incorporating these two performance parameters propagates multifarious defects and accidents in construction projects and this ultimately leads to cost overrun and time delay. In order to deal with quality and safety at the planning stage (along with time and cost), a decision-making model considering time, cost, quality, and safety (TCQS) is developed on the basis of a many-objective evolutionary algorithm (nondominated sorting genetic algorithm III) and presented in this study. An example is illustrated to demonstrate the capabilities of the proposed model for generating and visualizing the optimal trade-off among TCQS. The proposed model shows better results compared to earlier reported scheduling models. Additionally, the study has also conducted a correlation analysis to identify the interrelationship, if any, among the TCQS components. Furthermore, the solution obtained from the model was put through sensitivity analysis. The developed model was also checked for two large-scale scheduling cases and found to work efficiently. The inclusion of objectives like quality and safety will provide a holistic scheduling model for the construction professionals. This study is expected to provide an alternative planning strategy and assist project managers in selecting optimal TCQS trade-offs while constructing a facility.

The decision to bid or not bid on international construction project tenders is highly complex, due to the many uncertain factors associated with such projects. This study proposes a multiattribute bid or no-bid decision model based on the evidential reasoning (ER) approach, which effectively deals with the uncertainty arising from qualitative criteria and incomplete information. Evaluation of the bid or no-bid decision is divided into six stages according to the objectives set in the decision framework, as proposed in an earlier study. The ER approach plays a triple role in the model, improving the method for ascertaining the weights of criteria, aggregating the qualitative and incomplete information assessments of experts, and fusing the value of lower attributes to an upper attribute. The decision model is validated as feasible through the successful application of the model in a case project and verified as practical by an experimental case study. Although this decision model has certain limitations, it offers an innovative way of solving the bid or no-bid decision problem within the international construction setting. This study contributes to the body of knowledge in international construction management by providing a validated bid or no-bid decision model tailored to the specific considerations required of overseas projects and enriches the knowledge and application of the ER approach.

The success of a project depends on the importance of investigating the impact of contract administration implementation parameters such as response time, approval process, resolving issues, coordination, and documentation. To this end, this paper covers the development of a new multidimensional fuzzy model to quantify the performance of construction contract administration (CCA) processes at the project level. The proposed model contains 93 key factors and 11 project management process groups related to contract administration performance. An online questionnaire was used to rate the importance of each factor and group, and 223 responses were collected. The collected data were analyzed for normality, reliability, and intergroup differences. A two-part weighted fuzzy logic model was then developed to measure the CCA performance. The first part represented the group assessment, while the second part assessed the overall performance. The model, which was then implemented in two construction projects in Qatar, illustrated that the proposed model reasonably captured the CCA performance; and thereby a low level of implementing contractual risk management was concluded. The questionnaire results indicated that change control, financial, and claims and disputes resolution management were the top three groups affecting the CCA performance. This study is limited to design-bid-build contracts. Nevertheless, the model might be restudied for other project delivery methods as well.

Physically demanding and repetitive tasks expose workers to work-related musculoskeletal disorders (WMSDs). Over the last few decades, various rule-based postural assessment systems have been developed and widely used to facilitate the measurement and evaluation of risks related to WMSDs in many industries. However, the applicability of rule-based assessment to tasks involving heavy material handling has not yet been examined. This study investigated the applicability of three rule-based assessment systems (RULA, REBA, and OWAS) to a bricklaying task. To achieve this goal, an automated assessment tool was developed to implement those systems on whole-body data sets consisting of static postures captured by wearable inertial measurement unit suits. The study demonstrates the use of this tool in assessing risk levels (grand scores) encountered by 43 masons during the laying of 16.6-kg concrete masonry units (CMUs) in a standard wall. Furthermore, the biomechanical analysis of the same data set was carried out and utilized as ground truth to evaluate those results. It was found that rule-based assessment may lead to erroneously inflated risk evaluation in heavy manual handling tasks. In contrast, biomechanical analysis provided sensitive risk evaluations that distinguish the different degrees of risk arising from different motion patterns while participants performed the same tasks. These findings suggest using biomechanical analysis as an objective and robust method to evaluate risks encountered in tasks involving heavy material handling.

An increasing number of construction megaprojects have been invested in and built worldwide over the past decades, yet effective indicators for assessing construction megaproject success (CMS) are not validated, which leads to an ineffective assessment of megaprojects. Therefore, the current study attempts to identify a series of key performance indicators (KPIs) to assess the success of construction megaprojects. By conducting a questionnaire survey, research data were collected, which contained 129 valid replies from three groups of respondents from the Chinese construction industries: owners, contractors, and designers. IBM SPSS version 25 software was employed to analyze five underlying dimensions for CMS: (1) project efficiency, (2) key stakeholders’ satisfaction, (3) organizational strategic goals, (4) innovation and development of the construction industry, and (5) comprehensive impact on society. Afterward, the fuzzy set theory was utilized to evaluate the KPIs’ effectiveness. The results of this research can contribute to the body of knowledge in the field of megaproject management and serve as a theoretical foundation for the enhanced performance assessment of construction megaprojects and related management success.

Although data modeling methods for conceptual cost estimation are proven to be effective in academia, they are not adopted by construction practitioners as expected. To understand this fact and find solutions to the challenge of implementing modeling methods, a review of the modeling process is needed. Fifty-one most relevant studies were filtered out from the Web of Science and ASCE. Referencing two established data mining frameworks, namely, CRISP-DM and KDD, this paper identifies the key tasks of implementing conceptual cost estimation models. The results of reviewing key tasks show that the literature did not provide sufficient solutions to data preparation and model evaluation. Critical judgments on the accomplishment and deficiencies of the current conceptual cost estimation studies, from the perspective of data modeling process for the first time, is the main contribution of this paper. Other contributions include the elaboration of the body of knowledge to guide practitioners to implement advanced cost estimation, as well as recommendations on future studies of improving data quality and integration with data management systems to achieve the data models’ best capacity.

Given the complexity of overseas infrastructure projects (OIPs), difficulty in managing country-related risks commonly causes cost overruns. Thus, there is a pressing need for proactively predicting the impact of country-related risks to enable reasonable decision making in the early proposal stage. Different from the previous studies that heavily relied on a large number of sample points, this research proposes a case-based reasoning (CBR) method capable of reliably predicting the impact of country-related risks on cost overruns for OIPs from a small size of sample points. Grey relational analysis (GRA) is applied twice to address the challenges associated with many variables. Finally, a case study is conducted to validate the reliability of the proposed method. Importantly, this research includes a structured process to develop CBR methods for prediction tailored to different construction companies, hence increasing the adaptability.

Due to the narrow construction site, intensive construction, and constant strong winds, some concurrent activities during concrete dam construction are proven to lead to space conflicts and increase the frequency of exposure to hazards. Despite some valuable contributions, the current methods for quantifying exposure frequency are still subjective and inadequate for reliable safety evaluation. Therefore, this study provides a formal approach to identify exposure under dynamic wind loads and to estimate exposure frequency to scrutinize dynamic interactions at construction sites. In this study, first, the force of hazard with wind loads is analyzed to identify the impact of potential hazards from vertical concrete transport in an undesirable event. Second, combined with the concrete dam construction cycle process and the operating time characteristics, the probability of hazards and victims concurrently appearing in an overlapping space is calculated to measure the possibility of disastrous events from the perspective of spatial and temporal attributes. Then, the exposure frequency is expressed using statistical information on wind conditions, location information on the site layout, and mechanical motion parameters. In contrast to traditional risk-assessment methods, the proposed model combines the uncertainty of wind and the probability of hazards and victims simultaneously appearing in an overlap into a more rigorous safety perspective. Finally, the model’s capability of hazard exposure assessment is demonstrated and tested by applying it to a case study of a major real-world construction project. The results show that the novel methodology is able not only to quantify the exposure frequency with the contribution of the influence of strong winds but also to assess the risk of dam construction projects. Furthermore, this method will provide an important objective and precise evaluation tool for hazard exposure.

This study pivots around the relationship between claimants’ contract enforcement (i.e., the degree of pursuing contractual rights when resolving construction disputes) and their satisfaction with the resolution outcome. Little is known about how this relationship is contingent upon informal elements that may replenish contracts—such as the shadow of the future, fairness perception, and trust. To answer these questions, the authors collected data from contractors as claimants. The results show that claimants’ satisfaction is positively associated with rigid contract enforcement, with fairness perception, and with trust strengthening this relationship (but trust merely functions in cross-border interorganizational relationships). Flexible contract enforcement regarding obliging behaviors undermines claimants’ satisfaction, and this relationship is negatively moderated by the shadow of the future. Hence, fairness perception and trust play a complementary role in the effectiveness of rigid contract enforcement, while the shadow of the future exacerbates the detrimental impact of flexible contract enforcement. This study not only advances our understanding of contractual governance from the reactive perspective and construction dispute management but also enhances practitioners’ capability of coping with disputes regarding how to enforce formal contracts and proactively alter informal elements.

With the networks of sophisticated sensors and devices, building systems have the potential to serve as the infrastructure that provides essential data for the Internet of Things (IoT)-enabled smart city paradigm. However, current building systems lack intersystem connectivity or exposure to the more extensive networks of IoT devices. In this paper, the authors propose an IoT-enabled data acquisition framework that utilizes low-cost computers, sensors modules, developed software agents, and the existing building Wi-Fi network to establish a central facility database. A system prototype is developed for collecting and integrating facility data, and a case study on a university campus is conducted to demonstrate the proposed framework. The potential use cases enabled by the central facility database, the integration of building information modeling (BIM) standards and building system data protocols, a vision for future smart cities, and the challenges are also discussed. This research concludes that the proposed framework is effective in using IoT devices and networks to establish a cost-effective, platform-neutral, scalable, and portable building data acquisition system for smart building innovations.

This paper introduces a new practical scheduling problem called the resource-constrained project scheduling problem under multiple time constraints, which involves a duration constraint of activity, temporal constraint, and resource calendar constraint. The duration constraint of the activity exists widely in real-life projects, and it is first proposed as a resource-constrained project scheduling problem in this paper. We prove the defects of the traditional temporal constraint and improve it. The new problem combines three types of time constraints for the first time, which makes it closer to the actual scheduling problem. We developed a constraint programming optimization model for the new problem and used the IBM ILOG CPLEX-CP version 12.9.0 optimizer to solve it. Computational experiments are carried out to show that the CP optimizer is fast and provides a near-optimum solution to the new problem for projects with hundreds of activities within minutes compared to other metaheuristic methods. The results reported in this paper can be used as a benchmark for other researchers to compare and improve. The new problem contributes to developing a practical decision support system for resolving real-life constraints in projects.

Disaster restoration professionals operate in an inherently stressful niche of the construction industry. Their work often demands being on call and working long hours under stressful and dangerous conditions following fires, floods, earthquakes, hurricanes, tornadoes, and a variety of other events that are catastrophic to structures. The purpose of this study is to examine the predictors and consequences of burnout among disaster restoration professionals. In this study, the researchers used the Maslach Burnout Inventory (MBI) and the Areas of Worklife Survey (AWS) to investigate factors contributing to burnout for professionals in the disaster restoration industry. Two additional questionnaires were developed to record participant demographic information and whether participants were considering leaving their jobs (turnover intentions). A between-subjects multivariate analysis was used to determine whether significant differences existed in the reported roles for respondents, company types (franchise or independently owned), and turnover intentions. Internal consistency was tested for each dimension of the MBI and AWS. Structural equation modeling was applied to analyze the structural relationships between the dimensions of the MBI and AWS. The results indicated that respondents experienced heavy workloads and, subsequently, increased exhaustion, increased cynicism, and decreased professional efficacy. This study was the first to examine worklife factors, burnout, and turnover in the disaster restoration industry.

The aim of this study was to identify the barriers to the adoption of reverse logistics (RL) and to develop action plans to mitigate them as a way to minimize the construction and demolition waste (CDW) produced by the large multinational construction industries (CI), considering the impact these barriers have on the global construction community. A systematic literature review was conducted to identify barriers and RL practices with regard to CDW. The parameters under question were validated by six experts, and a survey methodology was applied and evaluated using structural equation modeling. These findings contribute to the literature and organizational practice because understanding these barriers is necessary to address the barriers that negatively influence the implementation of RL in the CI.

Bangkok clay is a soft marine clay with high water content and low bearing capacity. Bored piles installed by the dry process commonly are used for low- to medium-rise buildings in the metropolis. However, the installation of bored piles is time consuming and labor intensive, and requires skillful supervision, quality control, and the disposal of excavated spoils. The soil–cement screw pile (SCSP), a composite piling system which incorporates a soil–cement column (SCC) and a screw pile (SP), is an innovative alternative piling solution. This paper presents the ultimate load, time and cost analysis, and suggested effective design method for this piling system in soft Bangkok clay. The load capacity predictive equations for the SCSP were proposed and validated based on the field static pile load test results. These equations were used successfully for the installation of this piling system in construction projects on soft Bangkok clay. The unit cost of the SP was found to be the highest, and the unit cost of the SCSP and bored pile was found to be the lowest and almost similar. As a result, the application of the full SCSP and bored pile is more economical than the partial SCSP and SP under the same ultimate load design. However, the partial SCSP and SP have more advantages in terms of construction time and are suitable for a time-constrained project. The SCSP has higher efficiency, productivity, and competitiveness than the traditional dry-process bored pile. The outcome of this research will lead to the development of guidelines and a code of practice for SCSP in soft clay, which will be useful for the construction industry, particularly pertaining to the scheduling and cost performance of SCSPs in construction projects.

Building projects have become increasingly complex in recent decades for various reasons, including continuously improving technology, ongoing demand for more sophisticated design, and larger numbers of project stakeholders. Consequently, project teams find it increasingly difficult to attain design quality without errors or omissions. To provide a proactive approach to design quality management of building projects, this study focused on developing a design quality assessment tool based on the contributing causal factors and impacts of high-priority design defects. To this end, the authors first conducted interviews with 66 industry professionals to identify the contributing causal factors and impacts of quality-problematic design defects. Based on the results of these interviews, the authors generated 160 leading indicator questions to incorporate into their design quality assessment tool. The developed implementation tool enables practitioners to assess project design quality and prevent undesirable risks in a timely manner. This study deepens understanding of building design defects and supports design quality improvement through an effective design quality assessment tool.

Studies have estimated that infrastructure investment in the US will fall short by USD $13–20\mathrm{trillion}$ over the next decade due to growing urbanization. Public entities should seek to maximize the use of their available funds by increasing efficiency, supporting collaborative environments, and promoting value over initial low cost. This comprehensive study provides public entities with factual evidence of the performance of their projects under various delivery methods and bidding systems, as well as guidance toward practical implementations to increase their projects’ success using a unique data-driven mathematical model, the Project Performance Index (PPI), introduced in this paper. This index is a multivariate performance assessment metric that incorporates schedule, communication, change management, and spending metrics and is validated using confirmatory factor analysis. The results concluded that design–build (DB) projects outperformed each of hybrid single prime (HSP) and multiple prime (MP) projects (by 39% on average), construction manager at risk (CMAR) outperformed each of HSP and MP (by 32% on average), and also HSP outperformed MP (by 7.8% on average). Additionally, the study found that a 40%–45% enhancement in performance can be achieved by employing alternative delivery methods like DB and CMAR.

Gathering knowledge from expert practitioners is a hallmark of construction engineering and management research. However, the knowledge gathered is dependent on the knowledge of the people we talk to. Accordingly, this paper used a content analysis of 12 months of recent technical articles and case study publications in the Journal of Construction Engineering and Management to explore characteristics of research respondents that authors publishing in this journal feel are important to document. Authors report some subset of research respondents’ job type or role, years of experience, subject matter expertise, geography or nationality, organization and sector details, project type, professional qualifications, sex, race and ethnicity, ability, and language. Accordingly, this paper recommends that researchers should report at least these categories, and that researchers also should discuss research limitations that may result from the types of people they collect knowledge from. This change will make visible voices that are dominant or underrepresented in construction engineering and management research. In addition, by eliminating an otherwise unstated limitation, this change will result in improved construction engineering and management science.