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Updated: Jul 1, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Deep learning-based classification of earthquake-damaged buildings using terrestrial images.

Hamed Kashani¹, Amirmohammad Sahebzadeh², Mahdi Naimi Jamal²

¹Center for Infrastructure Sustainability and Resilience Research, Department of Civil Engineering, Sharif University of Technology, Iran.

|June 30, 2026

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This study developed a deep learning model using ground-level images to quickly classify earthquake building damage. The model achieved 93.5% accuracy, aiding rapid disaster response and urban resilience.

Area of Science:

Computer Science
Civil Engineering
Disaster Management

Background:

Effective post-earthquake building damage assessment is crucial for rescue and resource allocation.
Ground-level terrestrial images offer detailed insights often missed by aerial or satellite imagery.

Purpose of the Study:

To develop and evaluate a deep learning classifier for rapid, reliable assessment of earthquake-induced building damage using terrestrial images.
To categorize buildings into 'not damaged,' 'damaged,' or 'collapsed' for immediate post-event triage.

Main Methods:

A ResNet50-based deep learning model was trained on a curated dataset of post-earthquake building images.
The model utilized terrestrial imagery to capture ground-level damage details.

Keywords:

building damage assessment convolutional neural network damage classification post‐earthquake reconnaissance rapid disaster response terrestrial imagery

Related Experiment Videos

Last Updated: Jul 1, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Main Results:

The deep learning classifier achieved an overall accuracy of 93.5% in validation.
Demonstrated the feasibility of fast, building-scale damage assessment.

Conclusions:

This terrestrial image-based deep learning approach complements existing remote sensing methods for post-earthquake analysis.
Accelerates decision support for disaster response, improving urban resilience and potentially saving lives.