Fatigue Reliability revolutionizes how we predict and prevent the invisible threats posed by fatigue failure—the silent, progressive weakening of materials under repeated stress that 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, this book advances fatigue reliability from a static prediction problem to a dynamic, adaptive discipline.