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Updated: May 10, 2026

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

Automated low-cost framework for crack measurements in RC structures using deep learning approach.

Mahmoud Hassouna¹, Mohamed Marzouk², Eissa Fathalla²

¹Structural Engineering Department, Faculty of Engineering, Cairo University, Giza, 12613, Egypt. mahmoud.el-shreif.n@eng-st.cu.edu.eg.

Scientific Reports

|May 8, 2026

Summary

This study introduces an automated framework using deep learning (DL) for detecting and measuring cracks in reinforced concrete (RC) structures. The system offers accurate crack width measurements, improving structural health monitoring.

Keywords:

Crack detection Deep learning Image calibration Reinforced concrete cracks

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Area of Science:

Civil Engineering
Computer Science
Artificial Intelligence

Background:

Manual inspection of concrete cracks is time-consuming, subjective, and prone to inconsistencies.
Automated methods are needed to improve the efficiency and reliability of structural health monitoring.

Purpose of the Study:

To develop an automated framework for detecting and measuring surface crack widths in reinforced concrete (RC) members.
To overcome the limitations of manual inspection using a deep learning approach.

Main Methods:

A modified YOLO-V11 deep learning (DL) architecture was employed for crack detection and segmentation.
A crack width measurement algorithm utilizing patching and stitching was developed.
Customized image calibration and scaling were implemented for real-size dimension transfer.

Main Results:

The DL model demonstrated enhanced generalization for crack detection under realistic conditions.
The framework achieved a coefficient of variation of 16.82% and a mean relative error of 12.65% in crack width measurements.
Validation was performed on 230 crack points from experimental and existing RC structures.

Conclusions:

The proposed automated framework reliably detects and measures crack widths in RC structures.
This approach enhances the accuracy and efficiency of structural health assessment.
The integration of DL and image processing offers a robust solution for concrete crack analysis.