Journal of Construction Engineering and Management

In modern architecture, engineering, and construction (AEC) practices, the Iron Triangle of schedule, cost, and quality remains the key measurement to gauge project success. Existing tools do not work well at monitoring all three measures of the Iron Triangle, resulting in a huge cost and time overrun, and the existing functionality of Earned Value Management (EVM) covers only time and cost, but not quality. Thus, this study aims to develop a quality extension to EVM that caters to quality measurement. Firstly, this research introduced a measurable index and developed a new quality assessment method named the Quality Performed Assessment Method (QPAM) through a quantitative approach. Subsequently, a new tool named Earned Quality Value Management (EQVM) was developed to manage the Iron Triangle in a single reporting setting. The QPAM and EQVM were validated through three tiers of validation processes, namely, a pilot run to test its functionality in a conceptual model, a scenario analysis to identify its sensitivity, and a real case study to validate its functionality. QPAM and EQVM performed satisfactorily in systematic reporting and monitoring enhancement. This single reporting tool enables project stakeholders to make cross-comparisons and gain more insight into the project portfolio. Academically, this research minimized the gap among Iron Triangle management, quality management, and project portfolio management. Practically, this research developed a method for Iron Triangle control on a daily basis for both a single project and a portfolio.

Construction researchers have long had difficulties identifying motivational factors and situational and contextual factors affecting crew motivation and performance. The two main objectives of this paper are to define a methodology to identify the factors affecting construction crew motivation and performance and to bridge the gap in construction research by exploring more recent motivational concepts that have been introduced and advanced in nonconstruction domains. This paper presents a general review of motivation literature, applies recent advancements in motivation research from nonconstruction disciplines, and presents a case study to illustrate the proposed methodology and findings from data analysis. Because of the limitation in the sample size of craft survey respondents, the results of the case study should not be generalized to the entire construction industry. This paper makes three contributions: first, it provides a comprehensive set of factors affecting crew motivation and performance; second, it presents a novel methodology for identifying and measuring motivational factors at both the individual and crew levels; and third, it defines a methodology to evaluate and rank critical factors and factors with a high potential for improvement in construction crew motivation and performance and to evaluate the differences between the perspectives of supervisors and craftspeople on the identified critical factors.

Lessons learned systems are vital means for integrating construction knowledge into the various phases of the construction project life cycle. Many such systems are tailored toward the owner-organization’s specific needs and workflows to overcome challenges with information collection, documentation, and retrieval. Previous works have relied on the development of conventional local and network/cloud-based database management systems to store and retrieve lessons gathered on projects. These lessons learned systems operate independently and have not been developed to take full advantage of the benefits of integration with emerging building information modeling (BIM) technology. As such, construction professionals are faced with a lack of efficient and speedy retrieval of context-focused information on lessons learned for appropriate utilization in projects. To tackle this challenge, we propose the integration of lessons learned knowledge management in BIM in addition to existing two-dimensional to eight-dimensional modeling of project information. The integration was implemented through the embedding of nonstructured query system, NoSQL (MongoDB), in a BIM-enabled environment to host lessons learned information linked to model items and four-dimensional modeling project tasks of the digitized model. This is beyond existing conventional text-based queries and is novel. The system is implemented in .NET Frameworks and interfaced with a project management BIM tool, Navisworks Manage. The demonstration with a test case of a federated model from a predesign school project suggests that lessons learned systems can become an integral part of BIM environments and contribute to enhancing knowledge reuse in projects.

Cultural factors are unavoidable in the infrastructure construction process, and understanding relationships between culture and infrastructure sustainability (IS) will help increase IS levels. A qualitative comparative analysis (QCA) method is adopted to find what combinations of culture dimensions with different level of scores affect IS. First, data are collected through the Envision System, comprising scores of 12 projects in five IS aspects. Second, by QCA procedures, specific combinations of culture dimensions that contribute to projects’ behaving well in IS aspects are generated then discussed with the aid of Hofstede’s theory. The findings show that project owners’ attitudes and behaviors influenced by their national culture differ when an infrastructure project is implemented, resulting in different scores in IS aspects for the 12 infrastructure projects. Finally, a framework is established based on QCA results to reveal the relationship between culture and IS. Obtaining information on the relationship is of great importance for helping infrastructure owners change their attitudes and behaviors to increase IS levels.

Physiological status and environmental stressors are known to influence workforce performance at the individual worker level. A previous study, which conducted a cross-sectional comparison in repetitive material-handling construction activities, suggested that a U-shaped relationship existed between physical strain and productivity at the group level. This research revisits those findings to further investigate the U-curve relationship between physical strain and productivity at the group level and validate the concept of diminishing marginal productivity. Heart rates were measured as an indicator of subjects’ physical strain, whereas task productivity was estimated by work sampling. Eighty person-hour data were converted into panel data sets by dividing each subject’s 4-h experimental data into 5-min intervals. These data sets were subsequently used to evaluate the effects of time on physical strain and productivity with 5-min lags. The study found a U-curve relationship between physical strain and task-level productivity at the group level while controlling for individual characteristics. The U-shape relationship was constant in the low-performance and high-performance groups, although the degrees of the polynomials differed. Productive workers will remain more productive than low-productive workers with increased physical strain.

Out-of-sequence (OOS) work during construction is a major cause of productivity losses, cost and schedule overruns, and quality decline, either directly or indirectly. Given the complexity of construction projects, such impacts could be linear, nonlinear, sudden, delayed, direct, or rippled. Despite that realization, the construction literature falls short in studying how OOS is interrelated to the different project complex processes; thus, it is not possible to quantify the impacts of OOS on construction projects. The goal of this research is to develop and test an advanced model for analyzing the dynamics of OOS work. To this end, the authors used a multistep interdependent research methodology that involved (1) forming a dynamic hypothesis that explains the dynamics of the feedback structure of the problem in hand; (2) developing an advanced simulation model that captures the quantitative aspects of OOS and its corresponding relationship with progress, productivity, rework, and staffing, respectively; (3) developing a multistage calibration heuristic that enables users to calibrate the model to their projects to perform tailored and accurate analysis; (4) running verification tests on the model to ensure structural and behavioral validity; and (5) validating the model and its industry application using data from a mega construction project of around $500 million. Among other findings, the model demonstrated that not only the total magnitude of OOS work, but also the timing of such OOS work, has impacts on the project performance. The research has substantial intellectual merits to the construction management body of knowledge as it acts as the first to analyze the dynamic of OOS and capture its complexity. The research also introduces concepts and heuristics that add to the dynamic modeling field. As such, this study helps to better understand the relationship between OOS work and the different project feedback systems, visualize the rippled impacts of disruptions caused by OOS work, and provide informative forensic analysis of the corresponding project overruns. As for its practical merits, the presented model will assist project participants in properly analyzing and forecasting the direct and indirect effects of OOS in their projects and gauge the effectiveness of their mitigation policies.

Building information modeling (BIM) has developed as the definitive technology for managing construction projects. With its rise, the corresponding role of BIM manager has emerged as a necessary adjunct role in coordinating BIM-enabled projects. The ascent of the BIM manager has attracted a significant body of research, investigating the various competencies and responsibilities required of the role. While BIM is here to stay, a recent study, however, asserts that a distinct role oriented around BIM is transitory, which represents a significant departure from accepted assumptions regarding the viability of the BIM manager role. This research sets out to test the likelihood of a long-term market demand for the BIM manager, as a distinct role, based on a robust quantitative analysis of open-source data from a rich empirical dataset of global relevance for North America, Europe, and Australasia. Text mining methods are used. A total of 199 BIM-related jobs were retrieved from 14 of the most relevant job websites, representing the global English speaking job markets. Key knowledge, skills, and abilities that are attributes of BIM jobs were extracted and analyzed. Analysis reveals that there is no significant difference between the roles BIM manager and BIM coordinator. Moreover, the findings highlight that these two BIM roles align with that of project manager. Most importantly, analysis shows that BIM roles supplement the lack of BIM expertise within the role of project manager, and that, as BIM capabilities are increasingly absorbed by project managers, the rationale for an independent BIM expert will fade. The corollary is that BIM roles are a stopgap measure that can be expected to disappear as project managers absorb requisite BIM skills. The practical implication for construction engineering HR departments is that the prevailing policy of retaining dedicated BIM managers into the longer-term future should shift to one in which project managers are retrained to a level at which they possess comprehensive, independent BIM expertise.

Quality control for compaction of earth fills is commonly performed by measuring the in situ density using the sand cone method. In situ density measurements from sand cone testing are highly operator-dependent; in addition, the test procedure is tedious and time-consuming. In this study, a dynamic cone penetrometer (DCP) was used to perform quality control (QC) of earthworks by measuring penetration resistance in compacted soil. DCP tests were performed on three test pads specially constructed using different soil types—clayey sand with gravel, clayey sand, and silty sand. The test results were expressed in terms of a dynamic penetration index (DPI), defined as the depth of penetration of the cone per hammer blow. Correlations are developed between DPI and compacted density for the three soil types considered. In order to meet the criterion of compacted density equal to or greater than 98% of the maximum density from a laboratory standard Proctor test, DPI values are found to range from 5 to $8\mathrm{mm}/\mathrm{blow}$, corresponding to 250 mm penetration of cone on tested soil types. The effect of the fall height of the hammer on the measured DPI is also studied by performing DCP tests for two fall heights, 575 and 450 mm. DPI values are found to increase by 11–26% when the height of the fall increases from 450 to 575 mm, for the highest energy level considered in the study. It is also found that DPI is very sensitive to the moisture content and in situ density of compacted layers. The DCP device provided quick test results and was simple to operate on any subgrade layer; hence, the frequency of QC tests can be increased, leading to an improvement in the overall quality of compaction of earthworks.

Construction of the abutment, pier, and pier cap of long cross-sea bridges are challenging in complex marine environments. In the Hong Kong–Zhuhai–Macao Bridge (HZMB) project, an installation process characterized by concrete integral prefabricated abutment and pier (IPAP) with a flexible water stop curtain was invented. Studies showed that the underwater connection of IPAP and foundation piles can be achieved by adopting the steel cover box (SCB) scheme and separate flexible tray water stop technology. Compression bimodal GINA water stops are valid to seal the gap between SCB and IPAP. The horizontal water stops between trays and IPAP underside are achieved by jacking and compressing GINA water stops, whereas the vertical water stops between trays and steel piles casing (SPC) within 20 m deep underwater are achieved by a rubber capsule with 0.4 MPa pressure. After achieving the water stop between reserved holes and SPC, rapidly hardening concrete is cast into reserved holes. This reliable, efficient, and ecologically friendly innovative construction process, especially the water stop method, ensures that HZMB was constructed more safely and efficiently the China White Dolphin habitat.

Although the use of safety-related measures (SRM) has been advocated in the literature, construction companies often rely on reactive indicators instead of SRM to evaluate safety performance. A challenge with using SRM in practice arises when faced with the uniqueness of construction organizations. Distinctive practices and cultures affect the availability and quantity of data, which in turn limits the reliability of conventional SRM-based evaluation methods. This study proposes a case-based reasoning (CBR) approach that makes efficient use of existing SRM available within an organization to reliably evaluate safety performance. The model was validated using statistical tests to compare model output with actual performance. To test model applicability, we conducted a case study: based on information available in an industrial construction organization, including schedule performance and payroll data, 27 measures were identified. The results demonstrated that the proposed approach is able to evaluate safety performance trends in consideration of unique organizational characteristics using SRM. We expect that the model approach and results will support managers in tailoring safety goals and programs to particular contexts.

Work shift system is commonly used in construction projects to meet project deadlines. However, evening and night shifts raise the risk of adverse events and thus must be used to the minimum extent feasible. The three objectives of the work shift problem are to minimize project duration, project cost, and total evening and night shift work hours while effectively handling relevant scheduling constraints. This study proposes a new multiobjective approach that hybridizes dynamic guiding, chaotic search, and particle swarm optimization (PSO) functions, named multiobjective dynamic guiding chaotic search particle swarm optimization (MO-DCPSO). The approach can overcome the drawbacks of PSO in solving discrete domain problems and recruit more nondominated solutions kept in the archive. A real case was employed to verify the robustness and efficiency of the proposed approach. The result also indicated that MO-DCPSO is more fitting for solving practical project control issues.

The critical path method is an essential tool for project planning, control, and scheduling. But when it is applied to analyze the critical path of project networks with fuzzy activity durations, two key issues need to be settled properly: sorting fuzzy numbers and avoiding the fuzzy subtraction operation to generate negative, infeasible solutions. In view of this, this study proposes an improved fuzzy critical path method. The primary contributions this research makes to the body of knowledge are as follows: an integrated vector similarity algorithm is used to resolve the sorting problem of fuzzy durations, while the improved standard algorithm for fuzzy subtraction is to circumvent the negative, infeasible solutions generated in fuzzy subtraction operation; moreover, this method analyzes and depicts the change of critical path under the fuzzy state of activity duration, which fills the knowledge gaps. The empirical analysis indicates that the inference and computation of this method are relatively objective and easy to implement, the effectiveness and practicability of which are further verified in practice.

Construction productivity is a major research interest within the construction domain. Because construction is a labor-intensive industry, previous research has often focused on construction labor productivity (CLP). However, equipment is the main driver of productivity for some construction activities, so-called equipment-intensive activities. Existing models of activity-level productivity often predict a single-factor productivity measure—namely CLP—yet determining multifactor productivity, including labor, material, and equipment, provides more comprehensive predictions of productivity. Construction productivity models are often static in nature, or incapable of capturing the subjective uncertainty of some factors influencing productivity. Fuzzy system dynamics is an appropriate technique for modeling construction productivity because it captures the dynamism of construction projects and addresses the subjective and probabilistic uncertainty of factors influencing productivity. The contributions of this paper are threefold: identifying the key factors influencing the productivity of equipment-intensive activities, developing a predictive model of multifactor productivity for equipment-intensive activities using fuzzy system dynamics technique, and developing an approach to reduce uncertainty overestimation in the simulation results of fuzzy system dynamics models.

Sustainable construction has recently been acknowledged as a crucial issue in the construction industry. Developing and using multicriteria evaluation models to prioritize alternative construction projects based on sustainability indicators plays a major role in promoting sustainable construction. Evaluating construction projects according to sustainable development principles is a complicated problem, because the evaluation includes various indicators that have complex interdependences. Moreover, sufficient data is not normally available for the evaluation of construction projects from a sustainability point of view. To overcome these problems, this paper introduces a novel multicriteria group decision-making (MCGDM) model based on bootstrap simulation. The proposed model uses nonparametric bootstrap simulation to address aggregating inadequate experts’ opinions and employs decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive map (FCM) to provide the relative importance of evaluation criteria. Furthermore, based on the positive-ideal, negative-ideal, and closeness coefficient in interval intuitionistic trapezoidal fuzzy sets, a novel ranking score is proposed to compute the sustainability level of the construction projects. The proposed model is employed in a case study for evaluating and prioritizing highway construction projects by considering sustainability indicators. The main contribution of this paper is the development of a novel and applicable mathematical model to assist decision makers (DMs) in evaluating various alternative projects and making the optimum decision based on an integrated consideration of economic, environmental, and social sustainability indicators.

Using the method of stochastic actor-oriented models and a longitudinal data set of the National Quality Award Projects in China during 1998–2017, this study empirically investigates how project-based collaborative networks between contractors and subcontractors in the construction industry evolve across projects, and how related micromechanisms collectively underpin the evolution. The results reveal that the networks become dense over time and exhibit distinct scale-free properties. Furthermore, these macrolevel characteristics of network dynamics significantly relate to the structure-based preferential attachment effect, the ownership similarity effect, the geographic proximity effect, the negative scale similarity effect, and the ownership-based individual covariate effect, which operates at a microlevel. The findings not only provide a network view of how construction organizations select subcontractors in projects, but also contribute to broadened understanding of how project-based collaborative networks among firms in the construction industry evolve as complex adaptive systems whose macrolevel dynamics closely relate to a set of micro-organizing mechanisms.

Construction workers face the possibility of exposure to nanomaterials, which are particles smaller than asbestos. Nanomaterials exposure is expected to increase in coming years because of the advantages and desirable characteristics of nanoconstruction products. The lack of knowledge and preparedness in the construction industry for this technology is apparent. There are few research studies in the United States that have evaluated the potential safety and health risks of construction nanomaterials on the construction workforce. The Center for Construction Research and Training (CPWR) has built a database containing more than 400 construction products in the United States that may contain nanomaterials. This paper investigates the nature of the nanoparticles in these construction products. Additionally, a sample of the possible safety and health hazards affecting construction workers is provided. The findings confirm the industry’s lack of preparedness regarding the potential risks of construction nanomaterials, which may create or exacerbate unsafe conditions. Finally, the paper points out the necessity of communicating the potential hazards of nanoparticles to the construction workforce in contact with these products.