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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: Jul 5, 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 Automated Instance Segmentation Method for Crack Detection Integrated with CrackMover Data Augmentation.

Mian Zhao¹, Xiangyang Xu¹, Xiaohua Bao²

¹School of Rail Transportation, Soochow University, Suzhou 215006, China.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This summary is machine-generated.

This study introduces CrackMover, a novel data augmentation strategy, and a new instance segmentation method to improve automatic road crack detection. These advancements significantly enhance the accuracy and efficiency of identifying pavement distress for better road safety.

Keywords:

CrackMover crack detection deep learning instance segmentation

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Area of Science:

Civil Engineering
Computer Science
Artificial Intelligence

Background:

Road safety and maintenance rely heavily on accurate crack detection.
Traditional crack detection methods are inefficient and labor-intensive.
Existing deep learning methods often lack robust localization and segmentation capabilities for pavement cracks.

Purpose of the Study:

To develop advanced deep learning techniques for automatic crack detection with improved localization and segmentation.
To introduce a novel data augmentation strategy, CrackMover, to enhance crack detection performance.
To present a new instance segmentation method for more effective pavement distress identification.

Main Methods:

Proposed CrackMover, a specialized data-augmentation strategy for crack detection.
Developed a new instance segmentation method featuring a redesigned backbone network.
Incorporated a cascade structure within the region-based convolutional network (R-CNN) component.

Main Results:

CrackMover demonstrated effectiveness across various crack detection methods.
The proposed instance segmentation method achieved an average precision of 33.3%.
This represents an 8.6% improvement over Mask R-CNN with a ResNet50 backbone.

Conclusions:

The developed CrackMover strategy and instance segmentation method significantly improve automatic crack detection.
These advancements offer a more effective solution for identifying pavement crack distress.
The findings contribute to enhanced road safety and maintenance through superior crack detection technology.