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An Automated Image-Based Multivariant Concrete Defect Recognition Using a Convolutional Neural Network with an

Bubryur Kim¹, Se-Woon Choi², Gang Hu³

¹Department of Robot and Smart System Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea.

Sensors (Basel, Switzerland)

|May 20, 2022

Summary

This study introduces an automated concrete defect recognition system using a neural network. The advanced model accurately identifies various structural flaws in concrete, improving infrastructure assessment.

Keywords:

concrete cracks convolutional neural network delamination multivariant defects spalling surface crack

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Area of Science:

Civil Engineering
Computer Science
Materials Science

Background:

Deterioration of buildings and infrastructure in urban areas is a significant concern.
Traditional manual inspection methods for concrete defects are subjective and limited in accuracy.
Existing structural flaws like cracks, spalling, and delamination require efficient and objective assessment techniques.

Purpose of the Study:

To develop an automated, image-based multivariant defect recognition technique for concrete structures.
To enhance the objective and efficient assessment of structural health issues in concrete.
To categorize various concrete defects, including surface cracks, delamination, and spalling.

Main Methods:

A dataset of 3650 images of concrete defects was utilized.
A convolution-based multivariant defect recognition neural network model was developed.
The model was trained to differentiate between non-defective concrete and various defect types.

Main Results:

The developed model achieved a 98.8% accuracy in defect detection.
The system successfully categorized multiple types of concrete defects.
The automated technique demonstrated high efficiency in recognizing structural flaws.

Conclusions:

The proposed automated system offers a reliable solution for concrete defect recognition.
This technology can accelerate the evaluation of existing infrastructure conditions.
The developed method promotes advancements in defect detection and recognition for structural health monitoring.