Chapter 1 Introduction and overview
1.1 Introduction
1.2 Historical background of fatigue
1.3 Unique contributions of this book
Chapter 2 Safe-life approach for fatigue analysis and life prediction
2.1 Fundamentals of mechanics of materials
2.2 Classical uniaxial fatigue life prediction of isotropic materials
2.3 Multiaxial fatigue life prediction of isotropic materials
2.4 Multiaxial fatigue life prediction for anisotropic composite materials
2.5 Application to structural components with finite element methods
Chapter 3 Classical crack growth approach for fatigue analysis
3.1 Fundamentals of fracture mechanics and fatigue crack growth
3.2 Near-threshold fatigue crack growth under mode I and mixed-mode loadings
3.3 Fatigue crack growth under environmental effects – hydrogen embrittlement
3.4 Fatigue delamination growth of anisotropic composite materials
3.5 Fatigue life prediction using the equivalent initial flaw size concept
3.6 Crack growth-based life prediction considering corrosion effect
3.7 Crack growth-based life prediction considering surface treatment
3.8 Integration of fatigue crack growth analysis with finite element analysis
Chapter 4 Subcycle time-based fatigue crack growth formulation
4.1 Intrinsic difficulties of cycle-based formulation
4.2 Experimental evidence of subcycle time-based fatigue crack growth
4.3 Time-based fatigue crack growth kinetics modeling
4.4 Fatigue life prediction using subcycle crack growth analysis and EIFS concept
4.5 Time-based formulation for composite material delamination
4.6 Concurrent structure-material fatigue analysis using Liu’s formulation
Chapter 5 Fatigue reliability and uncertainty quantification
5.1 Probabilistic fatigue analysis basics
5.2 Uncertainty quantification of fatigue loadings
5.3 Uncertainty quantification of material properties for fatigue safe-life analysis
5.4 Uncertainty quantification of material properties for fatigue crack growth analysis
5.5 Analytical methods for uncertainty propagation and fatigue reliability estimation
5.6 Inverse FORM for probabilistic fatigue life prediction
5.7 Efficient Monte Carlo simulation for probabilistic life prediction
5.8 Surrogate modeling for fatigue reliability analysis
Chapter 6 Fatigue reliability updating
6.1 Basic concepts for reliability updating using Bayesian updating
6.2 Bayesian updating for fatigue prognosis and reliability updating
6.3 Constrained fatigue reliability updating using the maximum relative entropy
6.4 Handling model uncertainties in fatigue prognosis
6.5 Updating using Bayesian networks for structural-level fatigue prognosis
Chapter 7 Prognostics and Health Management against fatigue
7.1 Fundamentals of Prognostics and Health Management
7.2 Fatigue diagnostics for metallic structures
7.3 Fatigue prognostics for metallic structures
7.4 Fatigue damage diagnostics for composite materials
7.5 Fatigue damage prognostics for composite materials
7.6 Fatigue prognosis with a usage monitoring system
7.7 Maintenance planning for fatigue risk management
Chapter 8 Fatigue analysis with artificial intelligence and machine learning
8.1 Introduction to neural networks
8.2 Review of AI/ML development for fatigue analysis
8.3 Learning S-N curves using probabilistic physical-guided NN
8.4 Probabilistic fatigue life prediction using mixture density networks
8.5 Machine learning with processing and microstructure information
8.6 Information fusion for prognostics using machine learning
8.7 Material design against fatigue using neural optimization
Biography
Yongming Liu, Ph.D., is Professor of Mechanical and Aerospace Engineering at Arizona State University, USA. He is also a Founding Director of the Center for Complex System Safety, a state-supported multi-university initiative advancing safety science across materials, structures, and complex engineering systems. He received his Ph.D. in Civil Engineering from Vanderbilt University, focusing on stochastic multiaxial fatigue and fracture modeling. Dr. Liu's research lies at the intersection of fatigue and fracture mechanics, uncertainty quantification, and data-driven modeling, emphasizing unified frameworks that integrate physics-based approaches with probabilistic methods and artificial intelligence techniques. His work spans aerospace systems, civil infrastructure, energy systems, and air transportation safety, focusing on reliability, safety, and lifecycle performance under uncertainty. Dr. Liu is an ASME Fellow and Fellow of the Prognostics and Health Management Society. He has authored more than 200 journal publications and several book chapters, and his work continues to advance the integration of physics-based modeling and data-driven approaches for next-generation fatigue reliability and risk-informed engineering design.
