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Related Concept Videos

Fatigue01:21

Fatigue

888
Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
888
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
370
Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

623
Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
623
Microcracking in Concrete01:20

Microcracking in Concrete

512
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...
512

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Updated: Feb 25, 2026

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method
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A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation.

Hongxun Wang1, Weifang Zhang2, Fuqiang Sun3

  • 1School of Reliability and Systems Engineering, Beihang University, Haidian District, Beijing 100191, China. wanghongxun@buaa.edu.cn.

Materials (Basel, Switzerland)
|August 5, 2017
PubMed
Summary

Machine learning models accurately predict fatigue crack growth rates, outperforming traditional methods. Extreme learning machine (ELM) models demonstrated the best agreement with experimental data for fatigue crack propagation.

Keywords:
extreme learning machine (ELM)fatigue crack growthfatigue life predictionmachine learning algorithmsstress ratio

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computational Science

Background:

  • Fatigue crack growth rate (da/dN) and stress intensity factor range (ΔK) relationships are often nonlinear, especially in the Paris region.
  • Stress ratio effects on fatigue crack growth vary significantly across different materials.
  • Existing fatigue crack growth models struggle to accurately capture these nonlinearities and material-specific behaviors.

Purpose of the Study:

  • To propose a novel fatigue crack growth calculation method using machine learning algorithms (MLAs).
  • To evaluate the performance of three specific MLAs: Extreme Learning Machine (ELM), Radial Basis Function Network (RBFN), and Genetic Algorithms optimized Back Propagation network (GABP).
  • To compare the accuracy and effectiveness of MLA-based predictions against the classical two-parameter (K*) approach.

Main Methods:

  • Development and application of fatigue crack growth models based on ELM, RBFN, and GABP.
  • Validation of MLA-based models using experimental fatigue crack growth data from diverse materials.
  • Comparative analysis of the predictive capabilities of the three MLAs and the K* approach.

Main Results:

  • MLA-based methods significantly outperform the K* approach in predicting fatigue crack growth rates.
  • All three MLAs demonstrated superior accuracy and effectiveness compared to the classical model.
  • Extreme Learning Machine (ELM) algorithms exhibited the best overall agreement with experimental data due to their global optimization and extrapolation capabilities.

Conclusions:

  • Machine learning offers a flexible and powerful approach for modeling complex fatigue crack growth phenomena.
  • ELM, RBFN, and GABP are viable alternatives to traditional models, with ELM showing the most promising results.
  • The proposed MLA-based method enhances the accuracy and reliability of fatigue crack growth predictions across various materials.