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Microcracking in Concrete

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 Video

Updated: Aug 2, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

A Novel Real-Time Autonomous Crack Inspection System Based on Unmanned Aerial Vehicles.

Kwai-Wa Tse¹, Rendong Pi², Yuxiang Sun²

¹Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong.

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

This summary is machine-generated.

This study introduces a new drone-based system for real-time crack inspection in infrastructure. The system uses an improved YOLOv4 model to accurately detect and classify cracks, offering a cost-effective and efficient solution.

Keywords:

UAS YOLOv4 attention module autonomous inspection crack detection crack localization deep learning unmanned aerial vehicles

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Published on: November 26, 2019

Area of Science:

Civil Engineering
Computer Vision
Robotics

Background:

Traditional infrastructure crack inspection is labor-intensive, costly, and time-consuming.
Existing methods often rely on physical deformation signals, limiting efficiency and accuracy.
There is a need for automated, real-time structural health monitoring solutions.

Purpose of the Study:

To develop a novel, real-time crack inspection system utilizing unmanned aerial vehicles (UAVs).
To accurately detect, classify, and locate various crack types in large infrastructures.
To provide a low-cost, lightweight, and robust solution for structural health assessment.

Main Methods:

Implementation of an improved YOLOv4 object detection model incorporating an attention module.
Integration of the detector with a UAV platform for aerial data acquisition.
Development of a system for crack localization in the world coordinate system.

Main Results:

The improved YOLOv4 detector achieved a mean average precision (mAP) of 90.02%.
The system demonstrated a 5.23% mAP improvement over the original YOLOv4.
The UAV-based system proved effective and robust in real-world crack inspection scenarios.

Conclusions:

The proposed UAV-based system offers a significant advancement in real-time infrastructure crack inspection.
The enhanced YOLOv4 detector provides high accuracy and efficiency for crack detection and classification.
The system's low cost, lightweight design, and navigational flexibility make it suitable for practical applications.