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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: Jan 9, 2026

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

Metal Crack Length Prediction and Sensor Fault Self-Diagnosis Method Based on Deep Forest.

Qiang Gao¹, Yang Meng¹, Hua Li¹

¹The Department of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.

Sensors (Basel, Switzerland)

|December 11, 2025

Summary

This study uses finite element analysis and a Deep Forest model to accurately predict metal structure crack lengths from strain data. It also introduces a self-diagnostic method for strain sensors, improving crack monitoring intelligence.

Keywords:

crack length prediction deep forest multiple loads self-diagnosis strain compensation

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Last Updated: Jan 9, 2026

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

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

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Published on: January 6, 2023

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

Published on: January 16, 2019

Area of Science:

Mechanical Engineering
Materials Science
Computational Mechanics

Background:

Fatigue loading causes cracks in metal structures, impacting structural integrity and lifespan.
Accurate prediction of crack length is essential for ensuring structural safety and performance.
Crack length significantly influences local strain distribution within a structure.

Purpose of the Study:

To develop an accurate method for predicting crack length in metal structures using strain data.
To implement a Deep Forest model for optimizing data training and prediction accuracy.
To propose a self-diagnostic method for strain sensors to enhance monitoring reliability.

Main Methods:

Finite Element Analysis (FEA) was used to obtain strain data from compressive and tensile (CT) specimens under various loading conditions.
A Deep Forest (DF) model was employed for optimizing the training of strain data for crack length prediction.
Compensation was applied to dynamic strain data, and multi-dimensional input signals in the XY plane were utilized for prediction.

Main Results:

The study successfully predicted crack length using multi-dimensional input signals in the XY plane.
A novel self-diagnostic coefficient for strain sensors was proposed, based on the Pearson correlation coefficient.
The combined DF model and self-diagnostic coefficient demonstrated enhanced intelligence in crack state monitoring.

Conclusions:

The proposed method accurately predicts crack length in metal structures by leveraging FEA and DF modeling.
The developed self-diagnostic strain sensor capability improves the reliability of crack monitoring systems.
These advancements contribute to a higher level of intelligence in structural health monitoring for fatigue crack propagation.