The paper presented methodologies for designing targeted tests for reliability validation to increase the confidence in the estimated failure probability of a component/system for a given number of tests. Four approaches for physical tests and one approach for numerical tests were developed and demonstrated. An IS approach for validating a design obtained using an approximate model was developed. This approach computes the probability of failure of the same design using a very detailed model as a reference and also using IS. The method was demonstrated on a frame structure whose local details were analyzed using an approximate model. Mease and Nair's approach and an optimization approach, which optimize the sampling probability distributions of those parameters that can be controlled in tests, were presented and illustrated. These approaches use IS techniques. Both approaches were compared and proven to be more accurate than standard techniques that test random samples of components or systems under real operating conditions. The robustness of these approaches to errors in the analytical models was investigated. The approaches yield unbiased estimates of the probability of failure even if there are errors in the analytical probability distributions of the uncontrollable variables. However, errors in the distributions of the controllable parameters bias the estimate of the failure probability from the test. In both cases where the true probability distributions and approximate probability distributions of the controllable random variables were used to design the tests, the IS approaches improved the accuracy of the estimate of the probability of failure compared to the standard approach. However, finding the optimal sampling probability distribution can be expensive when there are more than 5 controllable test parameters. A Bayesian Targeted testing approach that validates the reliability of systems by performing optimal physical tests was developed. This approach models errors in the analytical model of a system by random parameters. The approach minimizes the variance of the probability of failure estimated from tests by testing the components/systems at more severe conditions than the operating conditions. This approach is suitable for validation problems in which we can only test few systems (e.g. one to five). The Bayesian approach improved the confidence in the probability of failure of a simple structure, in an example problem. A variance reduction approach for validating series system reliability by optimizing the number of tests to be performed on each component was developed. The approach relies on analytical estimates of the failure probabilities of the components to optimize the number of components that should be tested to maximize the confidence in the system probability of failure. If the components are independent and their failure probabilities are small (e.g. 0.05) the optimum number of tests for a component is approximately proportional to the square root of its failure probability.


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