Postdisaster inspection and assessment framework.
Rapid post-disaster structural damage inspection and performance evaluation are crucial for building owners and policymakers to make informed risk management decisions. Traditional manual inspection is inefficient, labor intensive, inherently biased, and heavily relies on the proper training of inspectors. To address these challenges, this research project aims to propose a generic digital twin-supported framework for structural damage condition and performance assessment, using AI, computer vision, point clouds, and robotic technologies. The framework is illustrated below.
Postdisaster inspection and assessment framework.
[1]. Pan, X., & Yang, T. Y. (2020). Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 35(5), 495-510.