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\begin{document}

\title{Fatigue life prediction in presence of mean stresses using domain
knowledge-integrated ensemble of extreme learning machines}



\author[1]{Lei Gan}%
\author[2]{Hao Wu}%
\author[1]{Zheng  Zhong}%
\affil[1]{Harbin Institute of Technology Shenzhen}%
\affil[2]{Tongji University School of Aerospace Engineering and Applied Mechanics}%


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  \date{June 14, 2024}


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\begin{abstract}
An accurate and stable data-driven model is proposed in this work for
fatigue life prediction in presence of mean stresses. In the model,
multiple independent extreme learning machines are trained using
different training data and neural network configurations, and are then
combined equally in an ensemble to model the complex correlations
between fatigue life, material properties and mechanical responses.
Meanwhile, theoretical prediction, as a representation of domain
knowledge, is integrated to optimize the data-driven processes of model
training and prediction, diversifying the information source of fatigue
life modeling. Extensive experimental results covering thirteen metallic
materials and a wide range of mean stress levels are collected from the
open literature for model training and evaluation. The results
demonstrate that the proposed model can achieve high accuracy and good
stability {[}simultaneously{]}(javascript:;), even with a small training
dataset, showing great applicability for fatigue life prediction under
mean stress loading conditions.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Fatigue life prediction in presence of mean stresses20220209.docx`  available at \url{https://authorea.com/users/482214/articles/568869-fatigue-life-prediction-in-presence-of-mean-stresses-using-domain-knowledge-integrated-ensemble-of-extreme-learning-machines}

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