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Related Concept Videos

Microcracking in Concrete01:20

Microcracking in Concrete

229
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...
229
Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

283
Concrete pavement joints are essential for maintaining the structural integrity and longevity of pavement by controlling where and how the pavement cracks. These joints can be categorized based on their functions, such as contraction or control joints, construction joints, isolation joints, and expansion joints.
Contraction joints are typically formed by sawing a groove into the concrete shortly after it has hardened. This creates a weakened vertical plane, deliberately encouraging cracking at...
283
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

272
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.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
272

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A Pavement Crack Detection Method Based on Multiscale Attention and HFS.

Chun Li1,2,3, Yu Wen1, Qingxuan Shi1,2,3

  • 1School of Cyber Security and Computer, Hebei University, Baoding 071002, China.

Computational Intelligence and Neuroscience
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This study introduces a new pavement crack detection method using multiscale attention and hesitant fuzzy sets (HFS) to improve accuracy. The approach enhances detail capture and noise reduction for better road crack identification.

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional U-shaped networks struggle with pavement crack detection due to information loss.
  • Accurate pavement crack detection is crucial for road maintenance and safety.

Purpose of the Study:

  • To develop an improved pavement crack detection method addressing limitations of existing techniques.
  • To enhance the accuracy and detail extraction in pavement crack segmentation and classification.

Main Methods:

  • Proposed a pavement crack segmentation network utilizing an encoding-decoding structure with ResNeXt50 feature extraction.
  • Incorporated a multiscale feature fusion (MFF) module for context information and a dual attention (EDA) module for detail enhancement.
  • Employed hesitant fuzzy sets (HFS) for crack similarity calculation and hesitation fuzzy measure for binary image segmentation.

Main Results:

  • Achieved segmentation performance metrics: Intersection over Union (IoU) at 55.56%, Precision at 74.26%, and Dice coefficient at 67.43%.
  • Obtained classification accuracy for transversal and longitudinal cracks at 84% ± 0.5%, and for block and alligator cracks at 78% ± 0.5%.
  • Demonstrated superior detection of crack details, complex topological structures, and small cracks compared to traditional methods.

Conclusions:

  • The proposed multiscale attention and HFS method significantly improves pavement crack detection accuracy.
  • The network effectively captures fine crack details and handles complex crack patterns, outperforming existing approaches.
  • This method offers a robust solution for automated pavement crack analysis in road inspection.