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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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MicrocrackAttentionNext: Advancing Microcrack Detection in Wave Field Analysis Using Deep Neural Networks Through

Fatahlla Moreh¹, Yusuf Hasan², Bilal Zahid Hussain³

¹Department of Geo-Science, Christian Albrechts University, 24118 Kiel, Germany.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

This study introduces an improved deep learning method for detecting microcracks in materials. The novel asymmetric network effectively addresses data limitations and class imbalance, achieving high accuracy in microcrack detection.

Keywords:

CNN acoustic emission attention feature space visualization microcrack detection segmentation spatio–temporal data

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Area of Science:

Materials Science
Computer Science
Artificial Intelligence

Background:

Automated microcrack detection using deep neural networks (DNNs) is crucial but hindered by limited, high-dimensional spatio-temporal data.
Significant class imbalance in crack datasets, with crack pixels as low as 5%, challenges DNNs, leading to poor detection of microcracks.

Purpose of the Study:

To develop an effective DNN model for microcrack detection that overcomes data limitations and class imbalance.
To investigate the influence of different activation and loss functions on microcrack detection performance.

Main Methods:

An asymmetric encoder-decoder network with an adaptive feature reuse block was proposed for microcrack detection.
The manifold discovery and analysis (MDA) algorithm was employed for feature space visualization to analyze activation and loss functions.

Main Results:

The proposed network architecture and training methodology demonstrated effectiveness in microcrack detection.
An overall accuracy of 87.74% was achieved, indicating significant improvement in detecting microcracks.

Conclusions:

The developed asymmetric encoder-decoder network with adaptive feature reuse is a promising approach for microcrack detection.
The study highlights the importance of addressing class imbalance and optimizing network architecture for accurate microcrack identification.