Bridge infrastructure asset management system is a prevailing approach toward having an effective and efficient procedure for monitoring bridges through their different development phases including construction, operation, and maintenance. Damage to any structural component of a bridge will negatively affect its safety, integrity, and longevity. Bridge decks are more susceptible to severe deterioration because they are exposed to harsh conditions including heavy traffic, varying temperatures, road salts, and abrasive forces. The ability to forecast the conditions of bridges in an accurate way has been a great challenge to transportation agencies. Many previous research studies highlighted the need to have a data-driven approach in predicting and evaluating the deterioration conditions of bridges. As such, this paper develops a computational data-driven asset management system to evaluate and predict bridge deck deterioration conditions. A multistep interdependent research methodology was utilized. First, the best set of variables affecting the conditions of bridge decks was identified. Second, two computational machine learning models were developed for the prediction of deck conditions using artificial neural networks (ANNs) and $k$ -nearest neighbors (KNNs). Third, a comparison between the developed models is conducted to select the ultimate model with the highest accuracy. The result is a framework that is able to evaluate and predict deck conditions with a prediction accuracy of 91.44%. While this research is applied to bridges in Missouri, the technique can be used on any similarly available data set nationwide. This study adds to the body of knowledge by devising a computational data-driven framework that is valuable for transportation agencies as it allows them to evaluate and predict deck conditions with high accuracy. Consequently, this will help in ensuring proper and effective distribution of funds allocated for the maintenance, rehabilitation, and repair of bridges. Ultimately, this will result in minimizing efforts, time, and costs associated with site inspection of bridge decks.

Due to the novelty of coronavirus disease 2019 (COVID-19) and the lack of measurable data, no enough research studies have been conducted to provide guidelines for responding to the coronavirus pandemic. This paper addresses this critical knowledge gap through a methodology comprising two main steps. The first step involved reviewing the updated industry best practices developed by various organizations and government entities. The second step involved investigating the impacts of the coronavirus based on the available resources and expert opinions, which also were used to develop a synopsis of emerging research topics. This paper provided various beneficial outcomes and findings. First, the paper presents a concise and integrated resource of COVID-19–related best practices for the construction industry. Second, the paper determined that the pandemic is perceived to have short- and long-term impacts—including negative and positive consequences—on four main facets: (1) workforce-related issues; (2) project and workplace considerations; (3) procurement and supply chain implications; and (4) contractual, legal, and insurance aspects. Third, the paper provides future research streams and directions that could be examined by future studies to help in the transition toward the new normal. Ultimately, this paper adds to the body of knowledge by offering practitioners and researchers guidelines for responding to the COVID-19 pandemic in the construction sector.

Regardless of the different project characteristics in the construction industry, cost and schedule overruns are always regarded as being of paramount importance in the project controls area. Numerous research efforts have been directed to forecast the aforementioned two important project variables using different modeling techniques. However, no prior work is believed to have offered an integrated approach to estimating the performance of construction projects. This critical knowledge gap is compounded even more by the evolving complexities and uncertainties in today’s construction industry. This paper creates a holistic framework to evaluate project progress and to predict its performance by incorporating a broad spectrum of inputs. The objectives are to (1) quantify the impacts of the risks related to the performance of projects in terms of cost and schedule; (2) formulate a holistic assessment model; and (3) correlate the developed system to predict cost and time at project completion. To this end, a multistep research methodology was utilized. First, for data collection, a survey was distributed and filled by 63 construction experts to study the effects of 25 performance risks that have shown to be the most important based on a meta-analysis of the literature in a previous study. Second, mathematical and statistical analysis techniques were used to develop a model that maps the investigated project risks to both cost and schedule performance. Steps included fitting parametric and nonparametric distributions, calculating cost overruns, verifying the model, and providing guidelines for using the proposed model. Third, for application, a hypothetical dataset was used to demonstrate the use of the model, its ability to deduce real-world behavior patterns, and associated limitations. The developed framework contributes to the body of knowledge by providing a novel model that improves project performance in terms of prediction, control, management, analysis, and decision making based on an individualized assessment of different risk indicators. This study is valuable for the construction industry because it allows all stakeholders to evaluate the performance of construction projects based on a list of variables, ultimately ensuring more effective and efficient delivery and execution of projects.

With a compound annual growth rate of 5.69%, the modular construction market is forecasted to increase to a market value of $154.8 million by the end of 2023. Traditional stick-built construction and modular construction differ in many aspects including design, engineering, collaboration requirements, logistics, and others. Available research efforts tried to study and model the complex modular construction processes. However, they seemed to fall short in investigating modular construction processes in a comprehensive and holistic manner. In effect, the currently used decision-making models and processes for modular construction do not necessarily reflect the diverse and multiple interconnected and associated factors. This paper develops a comprehensive list of decision factors as well as determines future research needs for modular construction. As such, the authors (1) conducted a systematic literature review and analysis of modular construction studies that were conducted between 2009 and 2019, (2) highlighted the broad factors affecting decision-making in modular construction projects, and (3) utilized social network analysis (SNA) to quantitatively identify the literature gaps and strengths and create a roadmap for the factors that need further investigation. This paper identified 50 decision factors affecting the various modular construction operations in the construction industry. Consequently, it was revealed that there are some understudied decision factors, including those related to legal and regulatory considerations, material and construction waste management, green practices, as well as commissioning and testing. Further, it was shown that there is an explicit absence of the decision models and frameworks that simultaneously incorporate all the identified decision factors. Accordingly, this study provides a substantiated foundation for comprehensive decision-making processes for modular construction.

Modular construction has been documented to be superior over traditional construction methods in terms of schedule, quality, predictability, and other project objectives. However, the lack of understanding and proper management of unique modular risks have been documented to result in suboptimal performance in modular construction projects. Although many previous research efforts have focused on the barriers and drivers related to the adoption of modular construction in the industry, no previous research work has addressed the key risks impacting cost and schedule of modular construction projects. This paper fills this knowledge gap. The authors utilized a multistep research methodology. First, a survey was distributed to and answered by 48 construction professionals to examine the effects of 50 modular risk factors that were identified based on a systematic literature review in a previous study. Second, a Cronbach’s alpha test was conducted to check survey validity and reliability. Finally, Kendall’s concordance analysis, one-way ANOVA, and Kruskal–Wallis tests were conducted to examine the agreement of responses within each as well as among the various stakeholders of modular construction projects. Results showed that the most critical factors affecting both cost and schedule of modular projects are (1) shortage of skilled and experienced laborers, (2) late design changes, (3) poor site attributes and logistics, (4) unsuitability of design for modularization, (5) contractual risks and disputes, (6) lack of adequate collaboration and coordination, (7) challenges related to tolerances and interfaces, and (8) poor construction activity sequencing. This study adds to the body of knowledge by helping practitioners to better understand the key risk factors that should be considered to enhance the performance of their modular construction projects. The outcomes provide insight into the alignment of stakeholders on the different risk factors affecting cost and schedule in modular construction projects. This should help practitioners to establish mitigation plans during the early stages of a project.

In the construction industry, competitive bidding has long been used as a method for contractor selection. Because the true cost of construction is not known until the completion of the project, adverse selection is a major concern. Adverse selection is when the winner of the contract has underestimated the project’s true cost. Thus, the winning contractor will most likely earn negative or at least below normal profits. The winner’s curse is when the winning bidder submits an underestimated bid and is thus cursed by being selected to undertake the project. In the multistage bidding environment, where subcontractors are hired by a general contractor, the winner’s curse may be compounded. In general, contractors suffer from the winner’s curse for a variety of reasons including inaccurate estimates of project cost; new contractors entering the construction market; minimizing losses in case of recession of the construction industry; strong competition within the construction market; differential oppor...