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Related Concept Videos

Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

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

Design Example: Joints in Concrete Pavements

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...
Microcracking in Concrete01:20

Microcracking in Concrete

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 Videos

A Dual-Path CNN and Transformer Network for Continuous Pavement Crack Detection.

Jinhe Zhang1,2, Shangyu Sun1,2,3, Weidong Song1,2

  • 1School of Geomatics, Liaoning Technical University, Fuxin 123000, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

A new dual-path network accurately detects road cracks, improving pavement maintenance. This method enhances continuous crack segmentation, leading to more reliable road condition assessments and reduced evaluation errors.

Keywords:
Vision Transformerconvolutional neural networkcrack segmentationdeep learning

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Pavement cracks are common road defects requiring timely detection for effective maintenance.
  • Existing crack detection methods often produce fragmented results, hindering accurate length assessment and reliability.
  • Elongated and irregular crack shapes pose significant challenges for current segmentation techniques.

Purpose of the Study:

  • To develop an advanced dual-path crack segmentation network for improved pavement distress detection.
  • To enhance the accuracy and completeness of continuous crack segmentation, especially for challenging crack geometries.
  • To provide a more reliable method for pavement condition evaluation and maintenance planning.

Main Methods:

  • Proposed a dual-path network integrating Convolutional Neural Networks (CNN) and Transformers.
  • CNN branch utilizes dynamic multi-branch convolution for enhanced directional perception of cracks.
  • Transformer branch employs a lightweight DCNv4 module to capture long-range dependencies efficiently.
  • A multi-path fusion module integrates features from both branches for improved semantic representation.
  • Implemented a combined BCE and Dice loss function to address class imbalance between cracks and background.

Main Results:

  • The proposed model significantly outperformed existing methods on four benchmark datasets (CFD, DeepCrack537, Gaps384, Crack500) in terms of F-score and mIoU.
  • Ablation studies confirmed the effectiveness of the dual-path architecture and its constituent modules.
  • Field validation demonstrated high accuracy, with a Pavement Condition Index (PCI) deviation of only 0.81 compared to manual assessments.

Conclusions:

  • The dual-path crack segmentation network offers a robust solution for detecting continuous pavement cracks.
  • The method significantly improves the accuracy and completeness of crack segmentation, addressing limitations of prior approaches.
  • The validated practical value demonstrates its potential for enhancing pavement maintenance assessment and road management.