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Microcracking in Concrete01:20

Microcracking in Concrete

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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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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Updated: Jun 18, 2025

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Autonomous Crack Detection for Mountainous Roads Using UAV Inspection System.

Xinbao Chen1,2, Chenxi Wang2, Chang Liu2

  • 1Sanya Research Institute, Hunan University of Science and Technology, Sanya 572025, China.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
Summary

This study introduces an Unmanned Aerial Vehicle (UAV) system for automatic road crack detection in mountainous areas. The system uses advanced navigation and an improved YOLOv8 algorithm for efficient and accurate inspections.

Keywords:
MRC-YOLOv8SWMUAV inspection systemmountainous roadpavement crack detection

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Area of Science:

  • Civil Engineering
  • Robotics
  • Computer Vision

Background:

  • Road cracks degrade infrastructure serviceability and safety, particularly in challenging mountainous terrains.
  • Traditional inspection methods are inefficient, labor-intensive, and unsuitable for complex road geometries.
  • Existing multi-function detection vehicles are costly and impractical for mountainous environments.

Purpose of the Study:

  • To develop a customized Unmanned Aerial Vehicle (UAV) inspection system for autonomous road crack detection in mountainous regions.
  • To enhance autonomous navigation capabilities in GPS-denied or GPS-drift scenarios.
  • To improve the accuracy and efficiency of crack detection using an optimized deep learning model.

Main Methods:

  • Implementing a slide window method (SWM) for robust path planning and autonomous navigation in challenging terrains.
  • Developing an improved MRC-YOLOv8 algorithm for accurate, real-time road crack detection from UAV imagery.
  • Conducting comprehensive experiments to validate system performance, accuracy, and efficiency.

Main Results:

  • The SWM-integrated UAV system demonstrated effective real-time route planning in GPS-denied mountainous terrains with low localization drift (2.75%) and scanning error (0.33 m).
  • The MRC-YOLOv8 algorithm achieved a high F1-Score of 87.4% and a mean Average Precision (mAP) of 92.3% for crack detection.
  • The MRC-YOLOv8 model showed a significant parameter volume reduction (0.19×10^6), indicating a more lightweight and efficient design compared to the original YOLOv8.

Conclusions:

  • The proposed UAV inspection system offers a viable, efficient, and accurate solution for routine road crack monitoring in mountainous areas.
  • The integrated SWM and MRC-YOLOv8 algorithm significantly enhance autonomous navigation and detection capabilities in complex terrains.
  • This technology provides valuable guidance for improving road infrastructure management and safety in challenging environments.