Document Type

Report

Publication Date

Winter 2024

Abstract

Organizing and overseeing a large number of transportation construction and maintenance projects that generally entail several miles of a worksite are a critical burden for each DOT. In addition, it requires manual monitoring of a project or construction managers to identify a progress status, a work activity, and a safety issue in a job site. Because of the projected huge volume, complexity, significant impacts of future transportation infrastructure projects, it is evident that we are now facing a critical need to create a means of improving the results of work zone management and evaluating their impacts on our society. In addition, multiple work zones of a large-scale highway construction project usually have to be managed and monitored by a human effort on site, which is slow, inaccurate, and expensive. One primary problem regarding this concern is that it has been increasingly burdensome for each DOT to consistently monitor all projects and their progress in each state as well as efficiently evaluate performance of work activities. With limited human resources and time, DOTs in Region 6 States have managed large-scale transportation construction and maintenance projects by a human inspection and recovered direct and indirect damages of transportation infrastructure systems caused from the recent natural disasters. Another critical issue is that this problem has prevented urban-level and integrated project management. Since it is not feasible to identify the status and the progress of numerous transportation infrastructure projects in real-time, DOTs remain limited to organize project resources and schedule according to diverse external factors including uncertainties in worksite, mobility, natural disaster, and others. The primary goal of this project is to explore digital twin as a possible solution that can deal with the current problem and to identify the characteristics of the digital twin technology that are applicable to transportation construction by promoting a participatory sensing concept. In addition, this research investigates the application of deep reinforcement learning for improving necessary decision making processes with the digital twin model. Recent research suggests that using only IoT sensors for capturing real-time data may be insufficient for entirely grasping the real-life situation. Involving participatory sensing along with IoT sensors for collecting real-time information can be a promising approach. Therefore, the research team developed a conceptual framework of a digital prototype for managing and monitoring transportation construction projects using sound-based real-time data and participatory sensing along with the IoT sensors. In addition, the technology entails deep reinforcement learning processes that can enable project participants to obtain diverse decision making options with predicted future outcomes.

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Tran-SET Project 22COLSU39

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