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A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention

Vikash Singh1, Anuj Baral1, Roshan Kumar2

  • 1Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Udupi 576104, India.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
Summary

A new hybrid deep learning model significantly improves structural damage detection accuracy. This AI approach offers a faster, more reliable alternative to manual inspections for infrastructure safety and disaster response.

Keywords:
CBAMCNNGoogLeNetResNet-50damage detectiondeep learning

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Infrastructure safety is critical, especially post-disaster.
  • Manual damage assessment is time-consuming and prone to errors.
  • Advanced computational methods are needed for efficient structural integrity monitoring.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate structural damage detection.
  • To enhance the performance of existing deep learning architectures for this task.
  • To provide a robust solution for infrastructure maintenance and disaster response.

Main Methods:

  • A hybrid deep learning model combining ResNet50 and GoogLeNet was developed.
  • A convolutional block attention module (CBAM) was integrated to boost performance.
  • A diverse image dataset with data augmentation was used for training and validation.

Main Results:

  • The hybrid model demonstrated superior performance compared to standalone ResNet50 and GoogLeNet.
  • Key metrics including precision, recall, F1-score, and accuracy were significantly improved.
  • The model proved effective in distinguishing between damaged and undamaged structures.

Conclusions:

  • The proposed hybrid deep learning model offers a highly accurate and efficient solution for structural damage detection.
  • This AI-driven approach surpasses traditional methods in reliability and speed.
  • The model is well-suited for real-time applications in disaster management and infrastructure upkeep.