| Description |
ix, 44 leaves : illustrations (some colored) ; 29 cm |
| Summary |
"Existing techniques for reliability analysis are computationally expensive. This thesis presents a new technique that requires significantly less computational effort. This technique relies on the First Order Reliability Method (FORM), most probable point based (MPP)-based importance sampling, and support vector machines (SVM). These methods are used to calculate the probability of failure using a small number of samples. First, the MPP is located, and then samples are generated around this point. These samples are used to approximate the limit-state function in the SVM. The MPP of the approximated function is then shifted to the MPP of the given limit-state function. Finally, the approximated function is evaluated by calculating the probability of failure. The small sample size required by this method reduces the computational cost for linear as well as nonlinear problems. The results have proved remarkably accurate in comparison with those obtained from Monte Carlo simulation with a large sample size"--Abstract, leaf iii. |
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