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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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Related Experiment Video

Updated: Jun 7, 2025

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

Published on: August 31, 2022

A highly efficient tunnel lining crack detection model based on Mini-Unet.

Baoxian Li¹, Xu Chu¹, Fusheng Lin²

¹School of Transportation and Geomatics Engineering, Shenyang Jian Zhu University, Shenyang, 110168, China.

Scientific Reports

|November 15, 2024

Summary

This summary is machine-generated.

This study introduces Mini-Unet, a lightweight deep learning model for efficient tunnel lining crack detection. It balances accuracy and speed, improving real-time infrastructure monitoring.

Keywords:

Crack detection Deep learning Hybrid loss function Lightweight model Sematic segmentation Tunnel engineering

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Area of Science:

Civil Engineering
Computer Science
Artificial Intelligence

Background:

Accurate tunnel lining crack detection is vital for infrastructure maintenance and safety.
Deep learning, especially Convolutional Neural Networks (CNNs), shows promise for crack detection but often faces challenges in balancing accuracy and computational efficiency.
Existing CNN models for tunnel crack detection can be computationally intensive, limiting real-time applications.

Purpose of the Study:

To propose a lightweight and efficient deep learning model for accurate tunnel lining crack detection.
To address the trade-off between detection accuracy and algorithmic efficiency in current CNN-based methods.
To facilitate the practical application of AI for real-time tunnel inspection.

Main Methods:

Developed Mini-Unet, a refined U-Net architecture incorporating depthwise separable convolutions (DSConv) to reduce model complexity.
Employed a hybrid loss function combining dice loss and cross-entropy loss to handle class imbalance between cracks and background.
Evaluated Mini-Unet against several mainstream models using key performance metrics.

Main Results:

Mini-Unet achieved a mean intersection over union (MIoU) of 60.76% and a mean precision of 84.18%.
The model demonstrated a Frames Per Second (FPS) of 5.635, indicating efficient processing.
Mini-Unet outperformed several mainstream models in terms of both accuracy and speed.

Conclusions:

Mini-Unet offers a viable solution for rapid and accurate tunnel lining crack detection.
The lightweight design and optimized parameters make it suitable for real-time AI-powered tunnel monitoring.
This advancement supports improved tunnel maintenance strategies and infrastructure safety.