Probability, reliability, and statistical methods in engineering design
Learn the tools to assess product reliability! Haldar and Mahadevan crystallize the research and experience of the last few decades into the most up-to-date book on risk-based design concepts in engineering available. The fundamentals of reliability and statistics necessary for risk-based engineering analysis and design are clearly presented. And with the help of many practical examples integrated throughout the text, the material is made very relevant to today's practice. Key Features
* Covers all the fundamental concepts and mathematical skills needed to conduct reliability assessments.
* Presents the most widely-used reliability assessment methods.
* Concepts that are required for the implementation of risk-based design in practical problems are developed gradually.
* Both risk-based and deterministic design concepts are included to show the transition from traditional to modern design practice.
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Basic Concept of Reliability
Mathematics of Probability
Modeling of Uncertainty
9 other sections not shown
algorithm assumed beam beta distribution calculate the probability Chapter checking point computed concept confidence interval considered corresponding denoted design point Determine diagram discrete random variables discussed in Section earthquake engineering equivalent normal estimated evaluated example extreme value distribution f-distribution failure probability first-order FORM Method limit state equation linear load and resistance load factors lognormal distribution lognormal random variable lognormal variable matrix maximum mean and standard mean and variance mean value MVFOSM nonlinear nonnormal normal distribution normal random variable normal variables number of simulation obtained occurrence parameters performance function probabilistic probability of failure probability paper problem random numbers random vari regression equation relationship reliability analysis respectively response variable safety factor safety index sample points sample space second-order shown in Figure simulation cycles SORM standard deviation standard normal statistically independent Step structure Suppose Table tion uncertainty vector Young's modulus