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Structural Response Prediction for Damage Identification Using Wavelet Spectra in Convolutional Neural Network.

Edisson Alberto Moscoso Alcantara1, Michelle Diana Bong1, Taiki Saito1

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

Sensors (Basel, Switzerland)
|October 26, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using accelerometer data and a CNN model to quickly assess earthquake building damage. This enables faster decisions for safety and resuming activities post-disaster.

Keywords:
convolutional neural networkdamage identificationsparse accelerometersstructural health monitoringwavelet spectrum

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Area of Science:

  • Structural Engineering
  • Seismic Engineering
  • Artificial Intelligence

Background:

  • Timely detection of earthquake-induced building damage is crucial for immediate safety measures and economic recovery.
  • Current methods may not provide rapid assessments, delaying critical post-earthquake decisions.
  • Developing advanced technologies for rapid structural safety evaluation is essential.

Purpose of the Study:

  • To propose a novel methodology for rapid damage identification in buildings following seismic events.
  • To leverage machine learning, specifically Convolutional Neural Networks (CNNs), for earthquake damage prediction.
  • To enable prompt decision-making for building evacuation and post-earthquake recovery.

Main Methods:

  • Utilizing the wavelet spectrum of absolute acceleration records from a single accelerometer on the upper floor as input data.
  • Training a CNN model to predict key damage indicators.
  • Predicting the maximum ductility factor, inter-story drift ratio, and maximum response acceleration for each floor.

Main Results:

  • The CNN model accurately predicts building damage information, including ductility factor and drift ratio.
  • Verification against seismic response analysis using actual earthquake data confirms the methodology's accuracy.
  • The proposed approach provides immediate insights into structural integrity from accelerometer data.

Conclusions:

  • The developed methodology enables rapid damage assessment of buildings immediately after an earthquake.
  • This technology facilitates timely decision-making, enhancing safety and mitigating risks from aftershocks.
  • Accelerated resumption of economic and social activities is possible with quick damage status evaluation.