Journal of Construction Engineering and Management

Recent publications have shown the role that options play in addressing the uncertainty, risk, and fairness in public–private partnership (PPP or P3) agreements in toll road projects. Typically, the literature uses financial market options techniques and applies these by analogy. Each option is presented and analyzed in standalone papers. In contrast, this paper presents an original single unifying approach for analyzing all PPP toll road options, an approach based on discounted probabilistic cash flows. The approach is a straightforward extension of conventional engineering viability analysis of projects, and does not rely on the financial market options literature. It offers a ready way to evaluate multiple options, requires minimal financial and mathematical knowledge, and hence can be readily implemented by practitioners. The paper also serves as a state-of-the-art literature summary as related to options in PPP toll road projects. The paper will be of interest to anyone involved in PPP toll road projects.

Selecting an appropriate joint venture contractor (JVC) is paramount to the success of large-scale infrastructure projects. The purpose of this paper is to develop a unified approach for JVC selection. The proposed approach integrates competitive information and collaborative information, focusing on the hesitant fuzzy variables of the bid evaluators. An optimization model is established based on the maximizing deviation (MD) principle to determine the criterion weights objectively. Then, the weights of the bid evaluators are obtained through calculating the relative closeness coefficients of each bid evaluator and the group from the JVCs to the fuzzy positive-ideal solution (FPIS). All the potential JVCs are ranked through minimizing the deviation model based on the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach. A case study of the JVC selection process for the Hong Kong–Zhuhai–Macau Bridge (a large-scale island and tunnel engineering project) was conducted to illustrate the proposed approach. The results showed that, compared with the existing methods, the proposed method can select the contractors with good collaborative performance. This is important for the practice, because collaborative criteria are reflected in the key criteria currently used in JVC evaluation and selection. The findings from this study can help the JVC manager to identify a JVC with good collaborative performance and control the project risk from the initial stage.

Environmental pollution and resource consumption caused by construction and demolition waste (CDW) have become increasingly serious. As a result, it is urgent to focus on waste recycling, resource conservation, and environmental protection. To reduce environmental pollution and decrease waste disposal costs, this paper employs a reverse logistics network (RLN)-based multiperiod optimization for CDW recycling and disposal. To optimize the CDW disposal process, a dynamic mixed-integer linear programming (MILP) model is proposed to determine the optimal solutions. By making decisions about the disposal process and CDW disposal volumes, the overall network costs are controlled on the premise of protecting the environment. A case study from China is then introduced to demonstrate the effectiveness and efficiency of the proposed MILP model in selecting the disposal processes, determining waste disposal volumes, and controlling total costs. Scenario analyses for the collection and recycling ratios are conducted, and the influence of the government’s green tax is considered. Finally, practical policy suggestions are given to guide CDW disposal. The main contribution of this paper is that the learned CDW disposal network could strictly and efficiently regulate CDW recycling and provide a valid political instrument to dispose of CDW that can not only reduce environmental damage and construction activity resource consumption, but also convert the CDW into new construction materials to economically benefit the construction industries.

Rework is a pervasive problem that adversely impacts the performance of construction projects and has become one of the most common concerns of the industry and academia. The rapid popularization of Building Information Modeling (BIM) is deemed useful for controlling rework and its resulting cost and time overruns. However, little empirical research has been done to show the impact of BIM implementation on rework in construction projects. As a result, by comparing two groups of projects (with and without BIM implementation), this paper first aims to assess and compare the rework status quo as well as the rework magnitude and impact by project type and by rework source. This study also proposes a set of practical strategies that can help prevent rework in projects with BIM implementation using a fuzzy set theory-based model. To achieve these objectives, an extensive literature review was conducted, followed by pre-interviews, a questionnaire survey, statistical analyses, and post-interviews. The results of the analyses and interviews show that the projects with BIM implementation tend to have experienced lower incidence, magnitude, and impact of rework relative to projects without BIM implementation. BIM implementation is also found to have a significant impact on reducing rework arising from owner change, design error/omission, design change, and vendor error/omission. The identified top three strategies to utilize BIM to reduce rework are “use of BIM throughout the design and construction phase,” “design reviews, verifications, and audit to reduce system errors,” and “rework tracking system to prevent future occurrences of rework.” To the best of the authors’ knowledge, this study makes the first attempt to measure the effects of BIM implementation on rework in construction projects using actual project data. The findings of this study enable practitioners to implement strategies to minimize rework with BIM implementation, improve projects’ cost and time performance, and improve the efficiency of project delivery.

Cost overrun risk assessment in the preliminary stages of a power plant project is laden with subjectivity, uncertainty, imprecision, and the vagueness of expert judgment. Current fuzzy approaches to risk assessment in such projects specifically require improvements in the reliability and aggregation process of experts’ judgments as well as the incorporation of project phase–based risk assessment to comprehend the sources of the risks and their corresponding effects on cost overruns. In response, this study proposes a modified fuzzy group decision-making approach (FGDMA) and assesses cost overrun risks considering different project phases [(1) design and procurement, and (2) construction and commissioning] of the power plant projects of varying types, sizes/capacities, and contracts. The proposed methodology is applied to 10 different project scenarios to better understand the context of project risks and their profile and identify the critical cost overrun risks involved. This study will aid the comprehensive assessment of risks and increase the accuracy of project budgeting and contingency allocation decisions.

Resilient safety culture is characterized by continuous improvements to safety performance and the capacity to have foresight, recognizing and anticipating the changing shape of safety risks in complex sociotechnical systems. This study aims to conceptualize resilient safety culture in the construction environment by integrating resilience engineering principles into the concept of safety culture. To fulfill this research aim, a correlational research design was used. Data were collected using questionnaire surveys targeting construction project managers involved in the delivery of 78 recently completed building projects in Vietnam. The structural equation modeling (SEM) technique with partial least-squares estimation (PLS) was used to analyze the data. The results confirmed 3 dimensions (i.e., psychological resilience, behavioral resilience, and contextual resilience) with 24 measurable scale items to assess safety culture with respect to resilience. The study also revealed that psychological resilience has a weaker impact on accident prevention under higher contextual resilience and behavioral resilience levels. Theoretically, this study provides the theoretical development and empirical evidence to clarify the concept of resilient safety culture in terms of definition, purpose, and value in the context of construction projects. In practical terms, the study suggests that project hazards, unexpected events, and the risk tolerance of construction workers should be addressed to achieve consistently high safety performance. It also offers construction organizations a framework of safety practices to assess their capabilities in managing on-site safety risks.

Engineering and construction organizations realize that knowledge sharing between employees is essential to be competitive, yet few understand how knowledge sharing is affected by management choices. This paper examines how managerial control systems, the structures and routines used to influence organizational activities, influence knowledge accessibility, defined as the effort that one takes to request and access knowledge from another person. Specifically, this research examines and compares the effects of clan and bureaucratic control on the accessibility of tacit versus codified knowledge. The researchers propose that individuals who perceive greater clan control, or governance through common values and beliefs, will perceive greater accessibility of both tacit and codified knowledge; while individuals who perceive greater bureaucratic control, or governance through rules and procedures, will perceive increase in codified knowledge only. To test the role of alignment between control systems and knowledge types in affecting knowledge accessibility, a questionnaire was administered to all engineers located in North America (855 people) within one engineering organization, and data collected from 298 responses were analyzed using linear regression analysis. The results in this research improve our understanding of knowledge accessibility and is an important step toward integrating control systems, knowledge type, and knowledge accessibility.

Inspection activities play an important role in the operation and maintenance (O&M) strategy in municipalities. Efficient O&M strategies can help in making informed decisions based on the actual condition of pipelines collected from inspection activities. Municipalities are in dire need of optimizing inspection activities due to the competitive needs for the large number of deteriorated sewer pipelines and limited budget allocated for inspections. This paper presents an optimization model for inspection of deteriorated sewer pipelines using multiobjective optimization technique for which time, cost, and number of inspected sections are optimized using mixed integer linear programming (MILP). The general algebraic modeling system (GAMS) is used to reduce the computational complexity of the proposed optimization model. A case study for an existing sewage network in the city of Doha, Qatar, is used to demonstrate the capabilities of the optimization model. The results obtained from implementing the optimization model showed an enhancement of 25.7, 44.4, and 6.5% for time, cost, and number of sections inspected when compared with the results for the same model using the genetic algorithm (GA). Additionally, a cost-saving of 68% could be achieved if the proposed optimization model was deployed instead of the current inspection practices carried out by the municipalities in Doha, Qatar. It is expected that the proposed model could be used to reduce both the cost and time of inspection, especially in cases of limited budget and work forces.

Contract violations have become common problems in construction projects, yet little of the construction contract literature addresses the question of responses to contract violations (i.e., contract enforcement). This research investigates the effects of trust on contract enforcement in a principal–agent relationship and explores the mediating role of risk perception on those effects. The authors distributed 429 electronic questionnaires and received 280 responses. After deleting responses completed in under 100 s and nonmanager responses, we narrowed the total to 253 valid responses from professionals in the Chinese construction industry. Hierarchical regression analyses were conducted to test the hypotheses in this study, and the findings revealed that goodwill-based trust diminishes the severity of contract enforcement, while perceived relational risk and perceived performance risk increase the severity of contract enforcement. Mediation analyses also support the mediating role of perceived relational risk on the effect of goodwill-based trust on contract enforcement. The findings contribute to contract theory by providing a thorough understanding of contract enforcement and developing a conceptual framework consisting of trust, perceived risk, and contract enforcement. Managers from violating parties may benefit from this article through an understanding of the role of trust and perceived risk in dealing with a contract violation and following the strategies recommended for diminishing the severity of contract enforcement.

The Toyota production system, on which lean production is based, emerged as the unplanned result of unrelated improvements and innovations. Although the related practices and principles are now widely reported, the theories and philosophical premises underlying lean production are not commonly known. This also applies to lean construction, which, although it originated as a set of countermeasures to specific problems in construction, has more recently evolved in alignment with lean production. For example, there is a stark but unexplained contradiction between lean and traditional construction management models regarding the importance of learning and improvement. In view of this, the aim here is to determine the epistemological orientation in these two models. It is found that two different starting points for epistemology, Platonism and Aristotelianism, have also played a major role in the formation of the fundamental ideas of engineering and management generally and in construction. An overly Platonic influence on engineering and management has created a number of problems. It is contended that one major explanation for the evident benefits of lean construction is related to its Aristotelian epistemology.

Although the line-of-balance (LOB) method is widely used for the scheduling of repetitive construction projects, there are only a limited number of studies that deal with the issue of how to incorporate uncertainty in repetitive schedules. In this paper, a delay risk assessment method is proposed for projects scheduled by LOB. In the proposed method, a LOB schedule is prepared considering the target rate of delivery, and then risk scenarios are defined considering the sources of uncertainty and vulnerability of activities. Next, probability distributions are determined for the required number of labor-hours for each activity and for learning rates, and finally, the delay risk of the project is quantified using Monte Carlo simulation. The application of the method, its outputs, and its potential benefits are demonstrated by a hypothetical case study. Moreover, the proposed method is applied to a real high-rise building project and the potential benefits of using a probabilistic LOB schedule at the planning stage are investigated. The findings reveal that the outputs of the proposed method may enable decision makers to estimate delay risk under various scenarios, formulate effective risk response strategies, and prepare contingency plans for resource utilization in repetitive tasks. However, the simplifying assumptions made during the development of the proposed model regarding the relationships between activity durations, risk scenarios, and learning rates may limit its applicability for all project conditions. Yet the method is generic, and its assumptions can be revised in consideration of specific project characteristics and objectives.

Despite regular safety inspections and safety planning, numerous fatal accidents related to scaffold take place at construction sites. Current practices relying on human inspection are not only impractical but also ineffective due to dynamic construction activities. Furthermore, a scaffold typically consists of multiple bays and stories, which leads to complexity in its structural behaviors with various modes of failure. However, previous studies considered only a limited number of failure cases for a simple one-bay scaffold while exploring machine-learning (ML) approaches to predict safety conditions. Thus, the authors have proposed an approach to monitor a complicated scaffolding structure in real time. This study explored a method of classifying scaffolding failure cases and reliably predicting safety conditions based on strain data sets from scaffolding columns. Furthermore, the research team successfully enhanced the predicting accuracy of ML classification by the proposed self-multiplication method to increase the number of features such as strain data sets. Implementation of the proposed methodology is expected to enable the monitoring of a large, complex system at construction sites.

Project managers regularly allocate human resources to construction projects. This critical task is usually executed by fulfilling the minimum project staffing requirements, normally based around the quantity and competence of project members. However, research has shown that team performance can increase by up to 10% and 18%, respectively, as a consequence of the group members’ heterogeneity and social cohesion. There is currently no practical quantitative tool that incorporates these aspects, allowing project managers to achieve this task efficiently and objectively. A new quantitative model for the effective allocation of human resources to multiple projects, which takes group heterogeneity and social cohesion into account, is proposed. This model is easy to build, update, and use in real project environments with the use of a spreadsheet and a basic optimization engine (e.g., Excel Solver). A case study is proposed and solved with a genetic algorithm to illustrate the implementation of the model. Finally, a validation example is provided to exemplify how group heterogeneity and social cohesion condition academic achievement in an academic setting.

An innovation management approach for construction projects should take into account the opportunities that different phases of construction create for innovation. This research demonstrates how innovation behavior changes over a project life cycle using a mega-alliance as a case study. This paper shows what happens to innovation behavior when infrastructure projects are set up to generate innovations throughout a project life cycle using a key performance indicator system for measuring innovation. More than 500 innovations in an innovation database were categorized, analyzed, and classified according to their type, novelty, and benefit and then mapped against the project life cycle. Using a classification model developed for this research, a quantitative analysis of the innovations was performed to show the principal types of innovation and the changing trend of each type over construction project phases. The findings of this research verify variations in innovation creation throughout the project life cycle. The results show the importance of the initial stage of a project for novel technologies innovation and the different innovation types that are likely to be generated per phase. The findings will help project managers to design, develop, and apply the best innovation management policies considering both project phases and classified types of innovations. These findings strengthen the argument for developing an innovation management approach in the construction industry that is contingent on the phases of construction projects.

Building energy retrofits aim to reduce the energy consumption of existing facilities. The most significant impact of any energy retrofit project is during the operation phase of a building, when a building’s users are directly impacted. This study focuses on the building operation phase by evaluating the social dimension of sustainability in energy retrofit projects. This study aimed to identify, categorize, and organize the social sustainability criteria of energy retrofit projects in existing buildings. A three-stage methodology was used that selected qualified experts in the targeted population, employed a Delphi technique to identify criteria for measuring social sustainability in energy retrofit projects, and used concept mapping to organize the identified social criteria. The study identified 19 social sustainability criteria that can be categorized in six clusters: occupants’ health and comfort impact, society enhancement, cultural and community education, project stakeholder enhancement, building quality and technology enhancement, and socioeconomic growth. An empirical framework was developed for considering the impact level of social sustainability criteria in energy retrofit projects, including three levels: building, community, and society. The proposed framework can be used by practitioners who seek to consider or evaluate the social impacts of energy retrofits as well as policy makers who investigate the social aspects or social costs of energy reduction programs.

Poor data quality in pavement construction life-cycle inventory (LCI) causes uncertainty in quantifying the associated environmental impact through life-cycle assessment (LCA). To reduce such LCA uncertainty while enhancing the reliability, several studies have been conducted on a screening procedure based on a quality assessment of the LCI input data to identify main sources of the resulting uncertainty. However, they often create additional uncertainty in the screening process and thus result in erroneous outcomes in identifying main uncertainty sources. This paper proposes a new system-level approach that enables the identification of main uncertainty sources through input data quality assessment upon reducing additional uncertainty. Based on the proposed preset criteria and by leveraging environmental emission quantities associated with each process, the authors first propose to achieve a consistent weighting process and then derive the system-level aggregated data quality indicator (ADQI). By utilizing the ADQI, system-level LCA uncertainty information is obtained through a modified beta distribution. The proposed method was evaluated through case studies on real-world pavement construction projects of the Illinois Tollway, and the main uncertainty sources, named key processes, were identified through sensitivity analyses. In the case studies, the plant operation, cement production, and binder production were identified as key processes in the given pavement construction project, contributing more than half of the total uncertainty resulting from poor data quality. Based on these findings, the proposed work is expected to help practitioners improve the reliability of pavement construction LCA through uncertainty-informed decision making to better reflect real project characteristics in the identified key processes.

Visual management (VM) is a strategy for information management that is widely used in the manufacturing industry, which is strongly related to increasing process transparency, one of the core principles of the lean production philosophy. However, the use of VM on construction sites is still relatively limited, and there is scant literature on the implementation of this principle in construction projects. This article proposes a model for devising visual management systems, understood as sets of visual practices that should be integrated in managerial processes. It is based on a set of benchmarking studies and two empirical studies carried out at different construction firms. As secondary contributions, this investigation proposes a set of guidelines for designing and implementing visual management systems and a taxonomy of VM practices according to the degree of integration in managerial processes. Some concepts proposed in other fields of knowledge, such as visual thinking, visual literacy, collaboration rituals, and boundary objects, were considered in the development of the model.

Planning, monitoring, and evaluating materials management practices are important for enhancing construction productivity. This study is designed to develop a tool for scoring materials management practices for building projects and, on that basis, build a tool for predicting productivity. The research was carried out in two phases. During Phase I, in-depth interviews were conducted with 19 experts and context-specific materials management practices were identified. During Phase II, questionnaires were used to collect quantitative data from 39 contractors. To prioritize the practices that were identified during Phase I, the quantitative data were analyzed. Based on the analysis, tools for measuring and planning the materials management practices and probability-based regression models were developed. Procurement plans for materials, long-lead materials identification, and materials delivery schedule are the three most significant practices. Contractors can use the scoring tool to measure the levels of implementation of the practices and assess the risk of having low productivity using the predictive models. This research contributes to the body of knowledge by developing construction materials management practices measuring, planning, monitoring, and evaluating tools in the context of building projects. In addition, the logistic and linear regression models can be used to assess whether a certain level of implementation of the construction materials management practice might be associated with higher or lower labor productivity.

Risks capable of impacting project objectives are intrinsic to the construction industry. Effective risk analysis has key importance to professionals concerned with construction goals, especially in public enterprises, given the amount of money invested and the demand for modern infrastructure. Therefore, this study aims to identify the main public construction risks and present a practical fuzzy analytic hierarchy process (AHP) model to prioritize these risks in Brazilian public enterprises. The model is divided into three steps: (1) risk identification and development of the risk breakdown structure, (2) pairwise comparisons, and (3) risk prioritization. Throughout the case study, 54 risks were identified, which were organized into eight categories. The major risk in the Brazilian scenario was “difficulty in environmental licensing.” Although this study focused on a specific context, its methodology provides a reliable and practical framework that can be adjusted to various construction projects worldwide to assess risks. Additionally, the identified risks can provide sound guidance to practitioners globally, because their elicitation was based on studies concerning different countries.