Related papers: Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events

Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events

URL: http://arxiv.org/abs/2205.02125v1
Date: Sun, 1 May 2022 19:55:56 GMT
Title: Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events
Authors: Yongsheng Bai, Bing Zha, Halil Sezen and Alper Yilmaz
Abstract summary: This paper presents a few experimental studies for automated Structural Damage Detection (SDD) in extreme events using deep learning methods. In the first study, a 152-layer Residual network (ResNet) is utilized to classify multiple classes in eight SDD tasks. The results show that the accuracy of damage detection is significantly improved compared to only using a segmentation network.
Score: 0.38233569758620045
License: http://creativecommons.org/licenses/by/4.0/
Abstract: This paper presents a few comprehensive experimental studies for automated Structural Damage Detection (SDD) in extreme events using deep learning methods for processing 2D images. In the first study, a 152-layer Residual network (ResNet) is utilized to classify multiple classes in eight SDD tasks, which include identification of scene levels, damage levels, material types, etc. The proposed ResNet achieved high accuracy for each task while the positions of the damage are not identifiable. In the second study, the existing ResNet and a segmentation network (U-Net) are combined into a new pipeline, cascaded networks, for categorizing and locating structural damage. The results show that the accuracy of damage detection is significantly improved compared to only using a segmentation network. In the third and fourth studies, end-to-end networks are developed and tested as a new solution to directly detect cracks and spalling in the image collections of recent large earthquakes. One of the proposed networks can achieve an accuracy above 67.6% for all tested images at various scales and resolutions, and shows its robustness for these human-free detection tasks. As a preliminary field study, we applied the proposed method to detect damage in a concrete structure that was tested to study its progressive collapse performance. The experiments indicate that these solutions for automatic detection of structural damage using deep learning methods are feasible and promising. The training datasets and codes will be made available for the public upon the publication of this paper.

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