Time-Dependent Reliability Theory and Its Applications

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ISBN/EAN: 9780323860161
Time-Dependent Reliability Theory and Its Applications introduces the theory of time-dependent reliability and presents methods to determine the reliability of structures over the lifespan of their services. The book contains state-of-the-art solutions to first passage probability derived from the theory of stochastic processes with different types of probability distribution functions, including Gaussian and non-Gaussian distributions and stationary and non-stationary processes. In addition, it provides various methods to determine the probability of failure over time, considering different failure modes and a methodology to predict the service life of structures. Sections also cover the applications of time-dependent reliability to prediction of service life and development of risk cost-optimized maintenance strategy for existing structures. This new book is for those who wants to know how to predict the service life of a structure (buildings, bridges, aircraft structures, etc.) and how to develop a risk-cost, optimized maintenance strategy for these structures. - Presents the basic knowledge required to predict service life and develop a maintenance strategy for infrastructure - Explains how to predict the remaining safe life of the infrastructure during its lifespan of operation - Describes how to carry out maintenance for an infrastructure to ensure its safe and serviceable operation during the designed service life

Chun-Qing Li is a Professor of Civil Engineering at RMIT University. His core research expertise is time-dependent reliability theory and its application to service life prediction of deteriorating materials and structures. His research areas include risk and reliability analysis of engineering works, steel corrosion and its effects on mechanical property and structural behaviour, corrosion of buried metal pipes, mixed mode fracture failure and fracture toughness, methodology of whole life design and assessment of civil infrastructure, stochastic modelling of loading and structural resistance.

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