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Microcracking in Concrete01:20

Microcracking in Concrete

99
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...
99
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

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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.
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.
123
Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

169
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...
169
Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

102
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...
102
Creep in Concrete01:22

Creep in Concrete

140
Creep refers to the time-dependent increase in strain under a sustained load, excluding other time-dependent deformations associated with shrinkage, swelling, and thermal expansion in concrete. The primary mechanism behind creep involves the loss of physically adsorbed water from the calcium silicate hydrate within the hydrated cement paste. This process is further exacerbated by concrete's non-linear stress-strain relationship, microcrack development in the interfacial transition zone, and...
140

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Related Experiment Video

Updated: May 29, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Image recognition technology for bituminous concrete reservoir panel cracks based on deep learning.

Kai Hu1,2, Yang Ling2, Jie Liu3

  • 1School of Civil Engineering and Architecture, Xi'an Technological University, Xi 'an, Shaanxi, China.

Plos One
|February 4, 2025
PubMed
Summary

This study presents an advanced deep learning anomaly model for detecting asphalt concrete cracks. The novel approach significantly improves accuracy and robustness, even under challenging environmental conditions.

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Crack detection in asphalt concrete is hindered by environmental factors affecting image quality and accuracy.
  • Existing methods struggle with variations in lighting, reflections, and weather.

Purpose of the Study:

  • To introduce a novel deep learning-based anomaly model for accurate and robust crack detection in asphalt concrete.
  • To enhance feature extraction, weighting, and information transmission for improved detection performance.

Main Methods:

  • Collected and processed a large dataset of panel images using denoising, standardization, and data augmentation.
  • Developed an improved Xception network incorporating an adaptive activation function, dynamic attention mechanism, and multi-level residual connections.
  • Utilized LabelImg software for precise labeling of crack areas.

Main Results:

  • The enhanced deep learning model achieved 97.6% accuracy and a Matthews correlation coefficient of 0.98.
  • Demonstrated stable performance and high accuracy under varying lighting conditions.
  • Significantly improved feature extraction, weighting, and information transmission capabilities.

Conclusions:

  • The novel deep learning anomaly model offers a significant advancement in asphalt concrete crack detection.
  • The proposed method enhances detection accuracy, robustness, and efficiency, overcoming environmental challenges.
  • This approach provides a valuable tool for infrastructure monitoring and maintenance.