Analyzing Safety Risk Imposed by Jobsite Debris to Nearby Built Environments Using Geometric Digital Twins and Vision-Based Deep Learning

Abstract

Extreme wind events can pick up loose and small objects on the ground, and once the objects become airborne, they negatively impact surrounding communities due to the collision impact. In this regard, jobsites and laydown yards that involve construction materials such as gravel piles and crushed rocks could be the main sources of potential windborne debris during extreme wind events. To analyze safety risk and predict the damage imposed by jobsite debris to nearby built environments, a new computer vision-based risk assessment based on geometric digital twins of jobsite debris is proposed for the reliability analysis on glazing systems of dwellings located on nearby jobsites. The impact of a gravel pile in a railroad jobsite on nearby buildings and residential environments was studied based on extreme wind event scenarios, and the failure risk of the building glazing system was computed. The risk associated with jobsite debris during extreme wind events and their impact on neighboring communities are analyzed through three computing modules: (1) satellite imagery-based terrain modeling to study 3D characteristics of the at-risk built environment; (2) analyzing visual data from Google Street View to assess the risk associated with glazing panels of dwellings in the communities; and (3) analyzing visual data from a jobsite to quantify the impact of jobsite debris, to associate its safety risk to neighboring communities. The proposed method can provide an immediate heads up for those who reside nearby jobsites, allowing to take required preemptive actions to protect their habitation against potential windborne debris. Practitioners will also be informed of such jobsite debris-related risk before extreme wind events to better secure their jobsites for the risk mitigation.