System reliability methods using advanced sampling techniques

Reliability concepts become widely spread as a basis for structural evaluation in design as well as assessment. Simulation based concepts are very attractive to be used because of their inherent simplicity. One of the major disadvantages however is the large number of simulations required to obtain sufficiently accurate results. Seen the complexity of large structures, with the possible need of non-linear finite element methods to calculate the outcome of the limit state function, the processing time might become unrealistic. To meet this disadvantage, reliability analysis based on simulation methods (Monte Carlo or Directional Sampling) in combination with an Adaptive Response Surface are developed. To increase the efficiency of the methodology, the low order polynomials that are used for the Response Surface, are extended using Neural Networks and Splines . Both are a more universal estimator and only require simple mathematics to calculate the outcome. The technique is presented, discussed and illustrated with preliminary results.