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Microcracking in Concrete

Microcracking in Concrete

Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...

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Related Experiment Video

Updated: Nov 15, 2025

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

An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu's Algorithm.

Xiaolong Chen¹, Jian Li², Shuowen Huang²

¹School of Water Conservancy Science & Engineering, Zhengzhou University, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)

|March 6, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for concrete crack detection by fusing 3D point clouds and 2D images. The enhanced technique significantly improves accuracy over traditional 2D image or 3D point cloud methods.

Keywords:

3D laser point cloud Otsu’s algorithm concrete crack detection the fusion of point clouds and images

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Last Updated: Nov 15, 2025

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Area of Science:

Civil Engineering
Computer Vision
Materials Science

Background:

Concrete cracks are a major structural concern.
2D image-based crack detection is limited by artifacts.
3D point cloud methods face measurement accuracy challenges.

Purpose of the Study:

To develop an automatic concrete crack detection method fusing 3D point clouds and 2D images.
To overcome limitations of existing single-modality detection techniques.

Main Methods:

High-precision registration of projected depth images and 2D images.
Pixel-level fusion of depth and gray information.
Improved Otsu algorithm for rough crack detection, followed by connected domain labeling and morphological methods for fine extraction.

Main Results:

Achieved an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%.
Demonstrated superior performance compared to single-image (F1 score 67.6%) and single point cloud (F1 score 76.0%) methods.
Validated across multiple scales and various concrete crack types.

Conclusions:

The proposed fusion method offers high detection accuracy and universality for concrete cracks.
This technique shows significant potential for automatic concrete crack detection applications.