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Related Concept Videos

Microcracking in Concrete01:20

Microcracking in Concrete

116
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...
116
Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

181
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...
181
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

146
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.
146
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

116
The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
116
Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

125
Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
One such test is the revolving disc test, where three plates...
125

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Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight
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Improved U-net network asphalt pavement crack detection method.

Qiong Zhang1,2, Shanshan Chen1, Yue Wu1

  • 1Changchun University of Science and Technology, Changchun, Jilin, China.

Plos One
|May 31, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved U-Net network for enhanced pavement crack detection. The new method significantly boosts accuracy in identifying road cracks, especially narrow ones, improving road safety.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Road Engineering

Background:

  • Road crack detection is crucial for road safety.
  • Basic U-Net models struggle with precise pavement crack segmentation, particularly for narrow cracks and complex backgrounds.

Purpose of the Study:

  • To propose an improved U-Net network for more accurate pavement crack segmentation.
  • To enhance the identification of narrow cracks and improve overall segmentation precision.

Main Methods:

  • Incorporated VGG16 and Upsampling Convolution (Up_Conv) modules.
  • Utilized Block depth separable convolution and Channel Attention (Ca) mechanism.
  • Integrated Depthwise GSConv Convolution (DG_Conv) and UnetUp modules in the decoding path.

Main Results:

  • Achieved a 13.6% increase in F1-score and a 9.9% increase in mean Intersection over Union (mIoU) compared to traditional U-Net.
  • Demonstrated superior accuracy and generalization ability over U-Net, Segnet, and Linknet.
  • Successfully detected cracks in complex backgrounds on CFD and Deepcrack datasets.

Conclusions:

  • The improved U-Net model offers a novel and effective approach for asphalt pavement crack detection.
  • The model shows high potential for practical applications in road maintenance and ground deployment.