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Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
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Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods.

Zhong Qu1,2, Fang-Rong Ju1, Yang Guo2

  • 1School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China.

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Summary
This summary is machine-generated.

This study introduces CrackHHP, an ultra-efficient algorithm for detecting concrete cracks. It significantly improves accuracy and efficiency in identifying even tiny fractures, crucial for tunnel asset management.

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Computer Vision

Background:

  • Effective monitoring of concrete surface conditions is vital for cost-efficient tunnel asset management.
  • Accurate and efficient crack detection is challenging due to surface imperfections like blebs, stains, and variable illumination.
  • Unclear and tiny cracks often go undetected with current methods.

Purpose of the Study:

  • To develop an ultra-efficient crack detection algorithm (CrackHHP) for concrete surfaces.
  • To improve the detection of tiny and unclear cracks.
  • To enhance the overall efficiency and accuracy of concrete crack monitoring.

Main Methods:

  • Proposed an improved pre-extraction process using overlapping grids and a redefined pixel value to identify candidate dark pixels while preserving crack integrity.
  • Introduced a second percolation processing step based on the percolation model to connect fractured segments and detect minute cracks.
  • Integrated the improved pre-extraction and second percolation processes into the CrackHHP algorithm.

Main Results:

  • The CrackHHP algorithm demonstrates a significant improvement in both the efficiency and accuracy of crack detection.
  • The method effectively preserves crack features during the initial candidate identification phase.
  • The second percolation process successfully connects tiny fractures, enabling the detection of previously unidentifiable small cracks.

Conclusions:

  • CrackHHP offers a highly efficient and accurate solution for concrete crack detection.
  • The proposed algorithm addresses key challenges in monitoring tunnel asset conditions.
  • This advancement is crucial for effective infrastructure management and maintenance.