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Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings.

Sensors (Basel, Switzerland)·2023

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Convolutional Neural Network-Based Rapid Post-Earthquake Structural Damage Detection: Case Study.

Edisson Alberto Moscoso Alcantara¹, Taiki Saito¹

¹Department of Architecture and Civil Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Aichi, Japan.

Sensors (Basel, Switzerland)

|September 9, 2022

Summary

This summary is machine-generated.

This study enhances a CNN method to assess earthquake-induced structural damage in essential buildings. The improved technique accurately identifies building safety and damage conditions, crucial for post-seismic response.

Keywords:

convolutional neural network damage detection power wavelet spectrum structural health monitoring

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Data Acquisition Protocol for Determining Embedded Sensitivity Functions

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Published on: April 20, 2016

Area of Science:

Structural Engineering
Earthquake Engineering
Artificial Intelligence

Background:

Rapid assessment of structural damage in essential buildings post-earthquake is critical for safety and operational continuity.
Existing methods require improvement for immediate and accurate damage detection.

Purpose of the Study:

To improve and validate a Convolutional Neural Network (CNN) methodology for detecting structural damage in essential buildings after earthquakes.
To enhance the accuracy and reliability of seismic damage assessment.

Main Methods:

Utilized three-dimensional frame models for essential buildings (Tahara City Hall, Toyohashi Fire Station).
Developed a record selection methodology to minimize variability in structural responses.
Employed maximum inter-storey drift and absolute storey acceleration as key damage indicators.

Main Results:

Achieved high accuracy in damage condition detection for both buildings.
Tahara City Hall: 90.0% accuracy and R² of 0.825.
Toyohashi Fire Station: 100% accuracy and R² of 0.909.

Conclusions:

The improved CNN methodology provides a highly accurate and reliable tool for post-earthquake structural damage assessment.
The validated approach supports immediate decision-making regarding building safety, evacuation, and resumption of critical activities.