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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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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

A Deep Learning Approach for Surface Crack Classification and Segmentation in Unmanned Aerial Vehicle Assisted

Shamendra Egodawela¹, Amirali Khodadadian Gostar¹, H A D Samith Buddika²

¹School of Engineering, RMIT University, 124 La Trobe St, Melbourne, VIC 3000, Australia.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This summary is machine-generated.

This study introduces a rapid surface crack detection system using two unmanned aerial vehicles (UAVs) and a novel convolutional neural network (CNN) called CrackClassCNN. The system achieved 95.02% accuracy, significantly improving infrastructure inspection efficiency.

Keywords:

concrete cracks convolutional neural network (CNN)deep learning unmanned aerial vehicles (UAVs)

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Area of Science:

Civil Engineering
Computer Vision
Robotics

Background:

Surface crack detection is crucial for infrastructure health monitoring.
Traditional inspection methods are time-consuming and labor-intensive.
Accessing confined spaces in infrastructure poses significant challenges.

Purpose of the Study:

To develop a rapid and reliable system for surface crack detection in infrastructure.
To evaluate the effectiveness of a novel convolutional neural network (CNN) architecture, CrackClassCNN, for crack classification.
To assess the performance of the Segment Anything Model (SAM) for crack segmentation.

Main Methods:

Deployment of two unmanned aerial vehicles (UAVs) for simultaneous image acquisition.
Utilizing a binary classification CNN (CrackClassCNN) for crack identification in images.
Employing the Segment Anything Model (SAM) for segmenting detected crack areas.
Benchmarking CrackClassCNN against state-of-the-art CNN architectures and SAM against manual annotations.

Main Results:

The novel CrackClassCNN achieved a classification accuracy of 95.02%.
The Segment Anything Model (SAM) demonstrated strong performance in crack segmentation with a mean IoU of 0.778 and an F1 score of 0.735.
The integrated UAV and CNN system proved highly effective for efficient infrastructure inspection.

Conclusions:

The developed UAV platform and CrackClassCNN offer a transformative solution for rapid and reliable surface crack detection.
The system is particularly suitable for inspecting infrastructure in confined spaces.
The combination of advanced UAV technology and AI-driven image analysis significantly enhances infrastructure health surveys.