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Microcracking in Concrete01:20

Microcracking in Concrete

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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 30, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
05:30

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Research on tire crack detection using image deep learning method.

Shih-Lin Lin1

  • 1Graduate Institute of Vehicle Engineering, National Changhua University of Education, No.1, Jin-De Road, Changhua City, 50007, Taiwan. lin040@cc.ncue.edu.tw.

Scientific Reports
|May 17, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved ShuffleNet deep learning model for detecting tire defects like oxidation and debris. The method achieves a 94.7% detection rate, enhancing vehicle safety and reducing costs for manufacturers.

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Area of Science:

  • Automotive Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Tire quality is crucial for vehicle performance, efficiency, and safety.
  • Drivers often overlook tire oxidation and other defects beyond tread depth and air pressure.
  • Automated tire defect detection is needed to ensure safety and reduce costs.

Purpose of the Study:

  • To design a deep learning-based method for detecting tire defects.
  • To improve the traditional ShuffleNet model for enhanced tire image analysis.
  • To evaluate the effectiveness of the proposed method against existing models.

Main Methods:

  • An improved ShuffleNet model was developed for tire image defect detection.
  • The proposed method was compared against GoogLeNet, traditional ShuffleNet, VGGNet, and ResNet.
  • Tire defect detection was validated using a dedicated tire image database.

Main Results:

  • The improved ShuffleNet achieved a 94.7% detection rate for tire debris defects.
  • The method demonstrated robust and effective detection of various tire defects.
  • The proposed approach significantly reduces tire defect detection time.

Conclusions:

  • The improved ShuffleNet model is a robust and effective solution for tire defect detection.
  • This technology can help drivers and tire manufacturers save labor costs.
  • Enhanced tire defect detection contributes to improved vehicle safety and tire longevity.