The United States has more than 615,000 bridges. US national bridge inspection standards developed by the Federal Highway Administration (FHWA) require routine inspections of these bridges every 24 months regardless of bridge characteristics such as age, average daily traffic (ADT), and current deterioration condition of a bridge. Previous studies reported that this routine inspection process is considerably costly and inefficient. If the future condition of a bridge can be predicted accurately, costly routine inspections with uniform intervals can be avoided. The objective of this study is to create a forecasting model that predicts future bridge deterioration conditions based on the bridge characteristics. Historical data of more than 28,000 bridges in the state of Ohio from 1992 to 2017 were used to create an ordinal regression model to statistically examine effects of bridge characteristics on variations in bridge condition and predict future bridge conditions. The outcomes of this study indicate that bridge characteristics such as age, ADT, deck area, structural material, deck material, structure system, maximum length of span, and current condition of the bridge are statistically significant variables that explain variations in bridge deterioration. The results of the forecasting process show that the created ordinal regression model can statistically predict future bridge conditions precisely. This study will help bridge owners and transportation agencies accurately model and predict bridge deterioration and assign inspection and maintenance resources efficiently. The efficient inspection process, customized based on predicted deterioration condition, can result in investing the millions of dollars currently funding unnecessary inspections into much-needed infrastructure development projects.

The 4th industrial revolution and its inherent digitalization are changing every aspect of the construction industry. However, these technological transformations bring new challenges, including cybersecurity. Although a number of studies in recent years have aimed to analyze cybersecurity issues in the construction sector, further research investment in this area is vital. The first step towards identifying needed and promising research directions in this domain is to systematically analyze the existing body of knowledge and detect knowledge gaps. This study addresses this need through a systematic scoping review designed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Review (PRISMA-ScR). To synthesize the existing knowledge, the extracted information is mapped with multiple benchmarks, including the National Institute of Standards and Technology (NIST) framework for cybersecurity management. The outcomes of the scoping review indicated that, from the construction perspective, the existing literature mainly focuses on a few areas, including Building Information Modeling (BIM), while other digital systems such as construction robots and prefabrication platforms need to be considered in future works. In terms of cybersecurity management, most of the existing studies focus on identifying risks and protecting assets. Other aspects of cybersecurity management, including detecting intrusions, responding to threats, and recovering from cyber-attacks need to be addressed in future studies. Regarding the technology-mediated countermeasures, the existing studies have explored only a limited number of potential solutions with a considerable focus on Blockchain-based systems, while the application of other countermeasures such as network virtualization can be a basis for future studies. Finally, the existing studies focused on procedural guidelines lack systematic solutions for areas including integrated cyber risk management and organizational change.

The U.S. petroleum industry is significantly dependent on petroleum infrastructures located in the hurricane-prone Gulf of Mexico. Natural disasters such as Hurricanes Katrina, Rita, Gustav, and Ike have been serious shocks to the U.S. energy supply and energy security. The significant impact of natural disasters on the performance of the petroleum industry emphasizes the importance of analyzing the resilience of petroleum infrastructures against natural disasters. Quantifying the destructive impact of extreme events is the first step toward analyzing the resilience of infrastructure systems. However, currently available hurricane-intensity scales, such as the Saffir–Simpson classification, cannot reflect the impact of hurricanes on critical infrastructures, and there is little knowledge about measuring the magnitude of natural disasters in terms of their destructive impact on petroleum systems. This gap in knowledge makes it difficult to properly quantify the effect of natural disasters, compare the disasters in terms of their destructive impact on petroleum processes, and analyze the resilience of petroleum infrastructures. The objective of this study was to develop a systematic approach to statistically quantify the destructive effect of natural disasters on petroleum infrastructures. A system-monitoring process using cumulative sum control charts combined with time-series analysis was conducted on the historical performance records of the three major components of the petroleum industry (i.e., crude oil production, import of petroleum materials, and oil refining processes) to measure the recovery period after a disaster. The reduction in the average performance of the petroleum process during the recovery period then was estimated. The primary contribution of this study to the existing body of knowledge is its development of well-defined metrics and a systematic approach to quantify the destructive impacts of natural disasters on petroleum infrastructures. The results of this study can help decision makers and the construction management community learn more about vulnerabilities in the infrastructure systems during different natural disasters and, consequently, develop different engineering management strategies to address these issues.

The long history of experiential learning in construction engineering shows the significant potential of cognitive development through direct experience. Recent advancements in gamification, especially digital serious games, can help educators develop novel pedagogical strategies to promote active and experiential learning in controlled settings. Despite considerable attention to this field during the last two decades, game-based educational solutions for construction engineering are far from achieving their full potential, and still little is known about systematic ways to direct research and development works in this domain. The first step toward developing systematic plans to advance research in this area is to analytically understand the extent, range, and nature of the existing research to identify research gaps and potential directions for future studies. This study addresses this need through a scoping review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Review (PRISMA-ScR). Throughout the research process, we identified 103 relevant studies. We extracted two sets of information from each document during the review process. The first set incorporates basic attributes of the publications, including the type of the document, publication year, and author affiliation. The second set of information concentrates on the contents of the documents to analyze their contextual characteristics and contributions to the field. Using the extracted information, we discuss the limitations in the state-of-the-art research in this rapidly growing field and propose a set of potential directions and considerations for future studies.

In the aftermath of a disaster, natural or man-made, successful emergency management operations vitally depend on reliable communication infrastructures. However, large-scale extreme events may dismantle telecommunication infrastructures and impair rescue operations. The current state of practice to maintain communication when infrastructures are not operable mostly focuses on using temporary infrastructures including mobile telecom towers. However, these centralized solutions heavily depend on equipment that may not be readily available. To address this issue, this paper aims to create theoretical foundations for, and empirically examine the performance of, a novel decentralized and infrastructure-less communication system that uses an ad hoc mobile network, distributed ledger and Blockchain technology, and an all-to-all broadcasting routing algorithm. When the proposed communication system is used, mobile devices in the affected area connect with each other through device-to-device Wi-Fi Direct, create a temporary mobile ad hoc network, and share spatial information through the stepwise routing algorithm and distributed ledgers. At the end of this process, each device in the network will have spatial information of all available devices in the area. Therefore, when first responders arrive in the affected area, they can locate all devices and have access to all collected information immediately through a direct connection to only one device in the area. The outcomes of this study will fundamentally transform the emergency communication solutions and can significantly shorten the duration of rescue operations by helping first responders locate citizens in the affected area faster and more efficiently.

The US Department of Transportation and Federal Highway Administration require routine inspections to monitor bridge deterioration. Typically, bridge inspections are conducted every 24 months. This timeframe was determined solely based on engineering judgment. The objective of this study is to develop probabilistic models to forecast bridge deterioration and statistically determine the optimal inspection intervals. A two-dimensional Markov process model that considers the current condition of a bridge, and number of years that the bridge has been in that condition, is created to predict future bridge conditions based on historical data. Using the forecasting model, a statistical process is developed to determine the optimal inspection intervals. The proposed methodology in this study is implemented, utilizing a dataset consisting of information about deterioration conditions of more than 17,500 bridges in the state of New York from 1992 to 2018. The outcomes of the statistical analysis indicate that the typical 24-month inspection interval is considerably pessimistic, and not necessary for all bridges currently in condition 5 or higher. However, the 24-month interval is too optimistic and risky for bridges currently in condition 4 or lower. The outcomes of this study help bridge owners and transportation agencies assign maintenance resources efficiently, and invest the millions of dollars currently allocated for unnecessary inspections in much-needed infrastructure development projects.