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Rapid Post-Earthquake Structural Damage Assessment Using Convolutional Neural Networks and Transfer Learning.

Peter Damilola Ogunjinmi1, Sung-Sik Park2, Bubryur Kim3

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Summary

Automated deep learning models, particularly a fine-tuned MobileNet, effectively classify earthquake structural damage. This AI approach enhances post-earthquake assessments, aiding rapid decision-making for emergency response.

Keywords:
convolutional neural networkdamage detectionearthquakeimage classificationtransfer learning

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Area of Science:

  • Artificial Intelligence
  • Civil Engineering
  • Computer Vision

Background:

  • Manual post-earthquake inspections are time-consuming and prone to human error.
  • Advancements in artificial intelligence (AI) offer potential solutions for efficient damage assessment.
  • The 2017 Pohang earthquake provides a relevant case study for evaluating AI in structural damage analysis.

Purpose of the Study:

  • To evaluate the effectiveness of automated deep learning models for post-earthquake structural damage assessment.
  • To compare the performance of various convolutional neural network (CNN) models using transfer learning (TL).
  • To develop a web-based application for automated earthquake damage classification.

Main Methods:

  • Implemented six pre-trained CNN models using transfer learning on a dataset of 1780 labeled structural damage images.
  • Trained models using feature extraction and fine-tuning techniques.
  • Compared model performance on a separate testing dataset and developed a web application integrating the best model.

Main Results:

  • The fine-tuned MobileNet model demonstrated superior performance in classifying earthquake-induced structural damage.
  • The developed web application successfully classifies damage severity using CNN and gradient-weighted class activation mapping.
  • Automated classification provides quantifiable damage assessment values.

Conclusions:

  • Automated deep learning, specifically the MobileNet model, significantly enhances the accuracy and efficiency of post-earthquake damage assessment.
  • The developed web-based application offers a practical tool for real-time structural damage classification.
  • This AI-driven approach supports critical decision-making in disaster management, resource allocation, and emergency response.