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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