Fatigue Reliability revolutionizes how we predict and prevent the invisible threats posed by fatigue failure - the silent, progressive weakening of materials under repeated stress which threatens everything from aircraft and bridges to wind turbines and medical devices. The book transforms fatigue reliability into a dynamic, adaptive science that evolves with real-world data

Presenting a unified framework, this volume integrates classical fatigue theories with probabilistic methods, structural health monitoring, and AI/ML techniques. At its core is a closed-loop approach connecting physics-based modeling, real-time data collection, and intelligent decision-making algorithms. This system-level methodology addresses complex interactions among materials, loading conditions, and environmental uncertainties while tackling nonlinear and data-limited scenarios that challenge conventional approaches. What distinguishes this work is its transformation of fatigue reliability from static prediction to continuous adaptation. By bridging rigorous theoretical foundations with practical engineering tools, it enables reliability assessments that update dynamically as new information emerges from operating systems.

Designed for researchers, engineers, and graduate students, it advances fatigue reliability from a static prediction problem to a dynamic, adaptive discipline.

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.