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

Yongsheng Bai, Bing Zha, Halil Sezen, Alper Yilmaz

Structural Health Monitoring 2022 article cited by 42

Source Open-access PDF More in Engineering & Materials

importance 6/10 ai assisted Infrastructure Maintenance and Monitoring civil infrastructure stage: research

Problem

Automatically detecting and categorizing structural damage in infrastructure from 2D images after extreme events such as earthquakes.

Why it matters

Rapid, accurate damage assessment after extreme events informs infrastructure safety decisions.

What the AI did

Deep learning networks (ResNet-152 for classification, cascaded ResNet+U-Net for localization, end-to-end networks for cracks/spalling) processed images across four experimental studies.

Results

High accuracy on all eight classification tasks; cascaded network significantly improved detection accuracy versus segmentation network alone (no numbers in abstract).

Impact

Deep learning enabled automated categorization and localization of earthquake damage in image collections.

Record

Venue	Structural Health Monitoring
Published	2022
Work type	article
Authors	Yongsheng Bai, Bing Zha, Halil Sezen, Alper Yilmaz
Citations	42 (OpenAlex, at import time)
References	37
Model or method	ResNet-152, U-Net, cascaded and end-to-end networks
Organization	not stated in source
Primary source	https://doi.org/10.1177/14759217221083649
Open access	green
Keywords	deep learning, structural damage detection, ResNet, U-Net, earthquake, cracks

Importance rating

6 / 10

significant — thousands to millions affected

Automated post-earthquake structural damage detection informs infrastructure safety decisions after extreme events; potentially significant benefit, but abstract gives no scale of affected populations or structures.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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