Machine Learning in the Analysis of Solid Deformation, Fatigue and Fracture

by Guozheng Kang PhD, Qianhua Kan PhD, Xu Zhang PhD, Ya-Nan Hu PhD, Xiangyu Li

Overview

Machine Learning in the Analysis of Solid Deformation, Fatigue and Fracture fills a clear gap in literature by applying machine learning to deformation, fatigue, and fracture analysis in solid mechanics. The book’s focus on complex mechanisms and coupling phenomena, discussed with practical examples, makes it a valuable resource for advanced researchers. Practical examples and case studies enable readers to understand both the underlying engineering problems and the application of machine learning methods to enhance fatigue life prediction analysis for solid materials and structures.

Product Details

ISBN-13:	9780443446153
Publisher:	Elsevier Science
Publication date:	01/01/2026
Pages:	350
Product dimensions:	6.00(w) x 9.00(h) x 0.00(d)

About the Author

Guozheng Kang is Chair and Professor of Mechanics at Southwest Jiaotong University, China. He is also vice president of Southwest Jiaotong University. His research activities focus on cyclic plasticity and visco-plasticity, fatigue failure and life prediction, low-cycle fatigue, multiaxial fatigue, fretting fatigue, rolling contact fatigue and ratcheting-fatigue interaction for metallic and polymeric materials, as well as the thermo-mechanical fatigue of shape memory alloys. He has been the author/co-author of more than 400 research publications in refereed international journals and conference proceedings

Qianhua Kan, Ph.D. in Solid Mechanics, Chair Professor at Southwest Jiaotong University, and Doctoral Supervisor. He was selected as a National High-Level Talents Recruitment Program candidate (2023). He has long been engaged in research on wheel-rail rolling contact fatigue and multi-field coupled fatigue of smart materials. He has published over 200 SCI-indexed papers, with more than 4, 000 citations by others, and has been included in Stanford University’s list of the top 2% of global scientists. He has authored three monographs in both Chinese and English, as well as six textbooks. He has received the Second Prize of the Natural Science Award from the Ministry of Education and the Second Prize of the National Teaching Achievement Award

Dr Xu Zhang is a Professor at Southwest Jiaotong University, and Doctoral Supervisor. He was selected for the National High-Level Young Talent Program (in 2022) and is a Humboldt Fellow. He is also a recipient of the International Journal of Plasticity Young Researcher Award. His research primarily focuses on multiscale mechanics of advanced metallic materials. He has published over 90 SCI papers

Dr Ya-Nan Hu is an Associate Professor at Southwest Jiaotong University. Dr Hu’s research focuses on the fatigue and fracture behavior of welding and additive manufacturing materials, as well as in situ characterization of material fatigue damage behavior using advanced synchrotron radiation. As the first author or corresponding author, she has published over 20 papers

Xiangyu Li, Ph.D. (dual doctoral degrees), Professor at Southwest Jiaotong University, and Doctoral Supervisor. He has been recognized as a “New Century Excellent Talent” by the Ministry of Education, a Humboldt Scholar in Germany, and a “Distinguished Expert” of Sichuan Province. Professor Li has published over 100 papers in leading journals, including Proceedings of the National Academy of Sciences of the USA，Advanced Materials, Advanced Functional Materials, Applied Energy, Journal of the Mechanics and Physics of Solids, Computer Methods in Applied Mechanics and Engineering, and International Journal of Mechanical Sciences, with more than 1, 800 SCI citations. His honors include the Second Prize of the Natural Science Award from the Ministry of Education and the Special Award of the Zhan Tianyou Railway Science and Technology Award. He serves on the editorial boards of Mechanics Research Communications and Mechanics of Advanced Materials and Structures.

Table of Contents

1. Introduction
2. Introduction to the algorithm and procedure of machine learning methods
3. Machine learning based multiscale plasticity analysis
4. Machine learning based fracture analysis of solid materials
5. Machine learning based fatigue life prediction of solid materials
6. Machine learning based solid structure analyses

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From the Publisher

A systematic review of machine learning methods in the analysis of solid materials deformation, fatigue and fracture