Journal of Construction Engineering and Management

On a regular basis, project managers concentrate their efforts on critical and near-critical activities. However, the concepts of total float and critical path lose their significance after applying resource-constrained scheduling (RCS) techniques. RCS techniques mitigate the resource supply-demand problem but break the critical path. As a result, several algorithms have been developed to identify a continuous critical path in resource-constrained schedules. This study reviews and evaluates the performance of eight RCS-related algorithms with the purpose of identifying the shortcomings that must be addressed so they can be applied for delay analysis. The review shows that a systematic procedure is needed to (1) incorporate and handle dynamic resource links when the schedule is updated, and (2) select a potential resource links configuration. Addressing these limitations will make the algorithms more practical for real construction and engineering projects and will allow a more realistic delay analysis since schedules will reflect the real conditions of the project (resource loaded). This study (1) discusses potential solutions to the shortcomings of the existing algorithms, (2) provides recommendations on the methods that can be used by industry professionals, and (3) proposes a system to facilitate the selection of an algorithm based on the common features (heuristic), constraints (removal of logic links), and project characteristics (resources and calendars).

The advent of wearable sensing technologies has produced unprecedented opportunities for the near real-time collection and analysis of workers’ safety and health data. To encourage the proactive safety management these opportunities present, extensive research efforts have explored using various wearable sensing technologies—including motion sensors (e.g., inertial measurement units) and physiological sensors (e.g., heart-rate sensors, electrodermal-activity sensors, skin-temperature sensors, eye trackers, and brainwave monitors)—to detect potential safety hazards and to continuously monitor a worker’s health on a construction jobsite. However, these efforts tend to be piecemeal or fragmented, which presents a challenge for both the practitioners and the researchers who wish to fully understand the current developments in this area. In this context, this paper provides a critical review of the state of the art of wearable applications in construction safety and health. The review first identifies five general applications within the literature: preventing musculoskeletal disorders, preventing falls, assessing physical workload and fatigue, evaluating hazard-recognition abilities, and monitoring workers’ mental status. Second, this study identifies the challenges impeding further development and deployment of wearable applications, specifically, signal artifacts and noise in wearable-sensors’ field measurements, variable standards for personal safety and health risks in construction, users’ resistance to technology adoption, and uncertainty regarding the return on investment. Lastly, this paper recommends future research opportunities for advancing the field, especially in terms of conducting sensor fusion for wearable applications, developing a business case, and engaging wearables in risk assessment and post-injury compensability assessment.

Production planning and control are critical in guiding projects toward meeting their targets. This paper uses the power of the last planner system (LPS) combined with conventional methods to assess schedule performance. Although previous studies have proposed multiple metrics within LPS, research correlating these various metrics to overall schedule performance has been both challenging and time-consuming. This study proposes new metrics and details a calculation method to measure significant aspects of planning performance that are not yet supported by a measurement metric. Moreover, the paper introduces a framework and a dashboard to employ these metrics to guide project-level and production planning. Results from applying the proposed framework to two construction projects in the United States show that projects tend to run behind the scheduled milestones due to poor performance in (1) making tasks ready and removing constraints, (2) committing to critical tasks, and (3) matching load to capacity. Findings underline major issues in construction planning where teams constantly struggle with aligning near-term planning with long-term planning. The proposed metrics can provide a proactive detection of issues and constant visibility of project status so that planning teams can apply earlier control measures to improve reliable planning and reduce milestone delays.

Change orders grant public-project owners exceptional flexibility to alleviate construction problems and respond promptly to project needs. Recently, in the Middle East, change orders have become common, leading to significant major problems and disputes. The negative impacts of this phenomenon on project cost and time create a critical need for systems that enable public agencies to identify the technical causes of change orders and investigate their impacts in order to prepare relevant response strategies. This study presents the development of a model for change-order data analysis that is capable of quantifying the impact of change orders on project cost and time. The model incorporates newly developed elements that enable (1) identifying the leading technical causes of change orders by project type and instigator; (2) quantifying the influence of project type and size on change-order cost and duration; and (3) automating the development of periodic reports that can be used to mitigate the negative impacts of change orders. To illustrate the viability of the developed model, a computerized system was developed and populated with data collected from 38 public projects that included 67 change orders. The leading technical causes of change orders identified through record analysis were poor definition of work scope, lack of coordination with authorities, and unplanned interruptions. An inverse exponential relationship is observed between the project size and both the percent time change and percent cost change of change orders. In projects that experienced scope-of-work changes, the percent time and cost changes were substantially higher compared with projects without scope-of-work changes. The paper’s primary contributions to the overall body of knowledge are (1) the development of a standard model to facilitate and automate the comparative analysis of change-order data across public-project types and sizes; and (2) the empirical demonstration of its viability for the analysis of change-order data.

Individuals vary widely in their ability to translate complex spatial information into performing physical tasks. Much of the variability can be explained by their experience in using different information formats and differences in individual spatial cognition. Traditional delivery of mechanical, electrical, and plumbing (MEP) designs rely on two-dimensional isometric drawings. Advancements in three-dimensional (3D) computer-aided design (CAD), augmented reality, virtual reality, and 3D printing have provided new format options for delivering engineering information; however, providing them to crafts for use at the construction workface remains relatively rare. The objective of this research is to understand how eye-gaze patterns of construction craft workers is influenced by information formats and spatial cognition when building a complex spatial task. A series of field trials with MEP workers was conducted to examine the influence of information format and spatial cognition on their eye-gaze patterns in building a scale-model pipe assembly. Participants were provided eye-tracking glasses along with one of three information formats: two-dimensional (2D) isometric drawings, 2D isometric drawings supplemented with a 3D image of the assembly, and 2D isometric drawings supplemented with a 3D physical model of the assembly. Card rotation and cube comparison tests were administered to measure spatial cognition. The results of this paper reveal that the information format and spatial cognition significantly influenced workers’ eye-gaze patterns; there are different gaze patterns for different information formats, and these differences in gaze patterns were associated with differences in spatial cognition abilities. Additionally, the improvement in performance when using different engineering information formats is associated with different eye-gaze patterns.

Previous research has identified 15 important drivers of project marketing, which are again verified through selected case examples. This study has been undertaken to develop a hierarchical flow of drivers of project marketing in a model using interpretive structural modeling (ISM). The adoption of such a ISM-based model on the drivers of project marketing would help managers, decision makers, and practitioners in ensuring the continuity of both relational and functional development in their organizations. Relational development helps project marketing organizations to approach clients before competitors. Functional development helps to maintain the potential offer by matching resources and capabilities. In addition to establishing structural relationship of linkages among the drivers of project marketing, the application of ISM followed by Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis contributes to extant literature by showing that only two groups of such drivers exist, one having high driving power (such as strategic priorities) and the other having high dependence power (such as contract). The presentation of these drivers as a logical consistent ISM model provides a simple theoretical understanding of project marketing.

Predicting business failure of construction contractors is critical for both contractors and other stakeholders such as project owners, surety underwriters, investors, and government entities. To identify a new model with better prediction of business failure of the construction contractors, this study utilized long short-term memory (LSTM) recurrent neural network (RNN). The financial ratios of the construction contractors in the United States were collected, and synthetic minority oversampling technique (SMOTE) and Tomek links were employed to obtain a balanced data set. The proposed LSTM RNN model was evaluated by comparing its accuracy and F1-score with feedforward neural network (FNN) and support vector machine (SVM) models for the optimized parameters selected from a grid search with five-fold cross-validation. The results successfully demonstrate that the prediction performance of the proposed LSTM RNN model outperforms FNN and SVM models for both test and original data set. Therefore, the proposed LSTM RNN model is a promising alternative to assist managers, investors, auditors, and government entities in predicting business failure of construction contractors, and can also be adapted to other industry cases.

Conventional valuation approaches—discounted cash flow—consider project alternatives as preset scenarios of predefined activities and ignore uncertainty and managerial flexibilities. The real options (RO) approach has been recently used by a large number of researchers to overcome the shortcomings of conventional approaches. This research contributes to the existing body of knowledge by presenting an innovative RO method which models flexible strategies in highway projects valuation under uncertainty through four advances over established studies. First, as well as traffic demand and time value of money, uncertainties about unit rates of agency and user costs are accounted for by a fuzzy approach. Second, flexible strategies are developed by considering the options embedded in the decision-making procedure for initial construction and rehabilitation of pavement. Third, a method is used to develop pavement performance curves based on traffic demand and corresponding effects of each construction activity. Fourth, user costs as well as agency costs are considered in analysis. Results of applying the model in a case study in Iran reveal that conventional valuation approaches may overestimate project life-cycle costs and that, in many cases, the flexibilities modeled by the developed fuzzy RO approach help managers enhance project value.

Resilient safety culture is an organization’s psychological, behavioral, and contextual capabilities to anticipate, monitor, respond, and learn in order to manage safety risks and create an ultrasafe organization. This study aims to identify the drivers of resilient safety culture in the construction environment. Data were collected using questionnaires from 78 completed building projects in Vietnam. The structural equation modeling technique was used to analyze the collected data. The results indicate that construction organizations can develop a resilient safety culture by systematically responding to regular threats, irregular threats, and unexampled events in the construction environment. Hazard prevention practice has a positive impact on contextual and behavioral resilience, error management practice has a positive impact on psychological resilience, and mindful organizing practice has a positive impact on contextual resilience. The findings of this study may advance the theories of resilient safety culture by providing insights into the mechanisms by which resilient safety culture can be created in a construction organization. It offers a framework that may guide the design of safety interventions in order to achieve a sustained improvement of safety performance of construction projects.

Cognitive biases would jeopardize the quality of decisions, yet people are reluctant to admit to being affected. This study explores the types of cognitive bias that may have influences on construction project dispute resolution (CPDR) decisions. Effects of cognitive biases are first operationalized as biased behaviors. Third-party neutrals, including arbitrators, adjudicators, and mediators, were asked to report their observations of disputing parties’ biased practices. The use of third-party neutrals aims to improve the objectivity of the data on disputing parties’ biased behaviors. Principal component factor analysis (PCFA) on the collected responses suggested four types of cognitive bias: preconception, self-affirmation, optimism, and interest-oriented biases. Among these, interest-oriented bias was found to be the most prominent. This study contributes to CPDR management by proposing bias-minimizing measures. Fuzzy-set linguistic evaluations were employed to assess the usefulness of the measures in view of the nondiscrete nature of the assessments. The findings were further validated through semistructured interviews with experienced dispute management experts. The contributions of the study include: (1) raising the awareness of the potential types of cognitive bias that would affect decisions in CPDR, (2) enhancing the role of third-party neutrals in directing disputing parties away from influences of biases, (3) reminding disputing parties not to undervalue the proposals of the counterparts, and (4) improving dispute management by suggesting a review of all options available at each round of dispute negotiation. Having experienced third-party neutrals as respondents is also a major breakthrough in the study of behaviors of disputing parties. The findings are invaluable to construction professional communities in general and to disputing parties and third-party neutrals in particular.

The 2017 report card for America’s infrastructure assigned a grade D to transit systems indicating they are in a poor condition with strong risk of failure. A possible solution proposed is adopting a comprehensive asset management system to maximize investments in light of the fund scarcity dilemma. This research develops a risk-based asset management framework for subway networks. A generic subway hierarchy is proposed, and risk is assessed using three submodels: failure index, consequences of failure, and criticality index (CI). The Failure Index is predicted using inspection reports and the Weibull reliability function. Consequences of failure are assessed based on seven criteria along financial, social, and, operational perspectives. The CI is introduced to assess the functional importance of a station in its location using seven attributes along three main criteria. The Fuzzy Analytical Network Process is employed to analyze experts’ knowledge used in the two functional submodels. The real case study assessment indicates two stations with high risk indices showing the necessity of an intervention action. This research presents a basis for evaluating subway infrastructure on a structural and functional basis. It assists authorities to derive an informed rehabilitation decision using a generic and consistent framework.

Estimate schedule to completion (ESTC) is a difficult variable to determine accurately during the various phases of construction projects due to the complex, uncertain, and limited nature of currently available information. Currently, most construction managers estimate this variable using their subjective prior experience with budget planning. However, this method is costly and highly prone to inaccuracy. The study developed a novel inference model, the neural network–long short-term memory (NN-LSTM) model, to estimate ESTC accurately by factoring in the sequence and nonsequence factors that significantly influence project duration and that capture the inherent uncertainties in the construction field. NN-LSTM fuses two characteristics of artificial neural networks (ANNs): feedforward neural networks (FNNs) for nonsequential issue, and recurrent neural networks (RNNs) for sequential issue. After a systematic review of the literature, brainstorming, and in-depth interviews with domain experts, a database of 226 historical cases collected from 11 construction projects with 14 influencing factors was created for learning purposes, with 10-fold cross validation used to partition the training and testing data sets. NN-LSTM was then applied to estimate the schedule to completion for the historical cases, with FNN capturing the impact of time-independent factors on project duration and LSTM capturing the long temporal dependency for sequential inputs. The learning results indicated good performance, with a mean absolute percentage error (MAPE) of less than 5% and a mean absolute error (MAE) of 2%, proving the NN-LSTM model to be more reliable than the currently prevailing earned value management (EVM) formulas. Moreover, in subsequent comparison testing NN-LSTM proved to be superior to other artificial intelligence (AI) prediction models. The model has the ability to generate reliable scheduling estimates for project managers in order to facilitate their accurate planning and the monitoring of the time performance of projects, allowing timely action to be taken to remedy foreseen delays and facilitating informed decision-making.

Variations are inevitable in public–private partnerships (PPP), due to the extensive duration of these contracts and the dynamic environment in which PPPs are usually implemented. Changes may lead to variations in the contract and consequently adverse reactions by partners. For the social and financial viability of the project, it is important to have mechanisms that can actively deal with variations that occur during the project life cycle. However, formal contracts with standardized project procedures do not always offer effective ways to cope with potential project dynamism. This paper presents the results of an in-depth case study into the modus operandi of a large-scale PPP infrastructure project with regard to variations in the realization phase. It was found that variations in infrastructure projects could not be dealt with solely through the formal contract rules, but that additional social mechanisms between the public commissioners and contracted companies were needed. Changes and contractual mechanisms are extensively discussed in project management literature. Also, the use of social mechanisms—complementary to the formal contract rules—is stressed. However, the literature offers little evidence about how these dealing mechanisms work in practice. This study reflects a real-life practice in responding to variations in a PPP and presents different ways in which various mechanisms, such as contract provisions, human relationships, organizational structure, digital tools, professional knowledge, and actor competences are interactively employed by project managers to deal with variation. General conclusions are drawn about the mechanisms observed, which may be applicable in the coordination of future interaction in public and private collaborations.