The Structural Reliability Of A Number Of Thematic Studies

Although the structure reliability theory and analysis methods are gradually well established, the application of reliability methods encounters many problems in engineering, such as imprecision of transforming non-normal randon variables into normal randon variables, unacceptable computional cost and low accuracy, etc. For the problems, the thesis presented a series of reliability methods combining with new mathematic theory, such as simulated annealing optimization, statistical learning theory, Markov chain simulation, convex model, etc. These presented methods listed as follows possess high precision and efficiency in the application.(1) On the basis of the equivalent normal distribution with three parameters and the simulated annealing optimization, an algorithm to calculate the failure probability is proposed for the structure with non-normal variables. The relative formulae are derived, and the implementation of the presented algorithm is demonstrated. The error of the failure probability calculation by this method is decreased due to the precise three parameters sought by the simulated annealing, which has global optimization property. Comparing with Monte-Carlo simulation, the computational effort of the presented algorithm is smaller, therefore it is more suitable for engineering application than Monte-Carlo simulation.(2) For reliability analysis of structure with implicit limit state function, an iterative algorithm is presented on the basis of support vector classification machine. In the presented method, the support vector classification machine is employed to construct surrogate of the implicit limit state function. By use of the rational iteration, the constructed support vector classification machine can converge to the actual limit state function in the important region, which contributes to the failure probability significantly, thus the precision of the reliability analysis is improved.(3) Two reliability parameter sensitivity analysis methods are presented, which are semi-analytical method and fast numerical simulation method. The two methods are both based on importance sampling Markov Chain simulation. The remarkable advantage of the semi-analytical method is its high efficiency. By increasing very limited computational effort in the presented semi-analytical method, the reliability...