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Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network.

Hyun Kyu Shin1, Yong Han Ahn1, Sang Hyo Lee2

  • 1Architectural Engineering, Hanyang University, ERICA, Ansan 15588, Korea.

Materials (Basel, Switzerland)
|December 9, 2020
PubMed
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A new convolutional neural network (CNN) model, CMDnet, accurately identifies concrete surface damages like cracks and rebar exposure. This automated system achieves 98.9% accuracy, improving infrastructure inspection efficiency.

Area of Science:

  • Civil Engineering
  • Artificial Intelligence
  • Computer Vision

Background:

  • Urban infrastructure faces increasing deterioration, exposing structural defects.
  • Vision-based damage recognition is crucial for effective building diagnosis.
  • Conventional image processing methods struggle with manual feature extraction in real-world scenarios.

Purpose of the Study:

  • To develop an automated damage recognition technique for concrete structures.
  • To overcome limitations of traditional image processing in defect detection.
  • To introduce a novel convolutional neural network model for multi-damage recognition.

Main Methods:

  • A convolutional neural network-based image recognition technique was employed.
  • A concrete multi-damage recognition neural network (CMDnet) was developed.
Keywords:
attention networkconcrete defectsconvolutional neural networkdamage recognitiondeep learning

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  • An image dataset of 1981 concrete surface damages (cracks, rebar exposure, delamination) and intact samples was utilized.

    • Main Results:

    • The proposed CMDnet model accurately classified various concrete damage types.
    • The trained CMDnet achieved a high damage-detection accuracy of 98.9%.
    • Experimental validation confirmed the model's effectiveness in recognizing surface damages from digital images.

    Conclusions:

    • The developed CMDnet model demonstrates superior performance in concrete surface damage detection and recognition.
    • The model can be integrated into automatic damage detection networks for civil engineering applications.
    • This approach accelerates efficient damage identification in deteriorating structures.