Soft Computing Techniques in Probabilistic Seismic Analysis of Structures

Abstract

Earthquake-resistant design of structures using probabilistic analysis is an emerging field in structural engineering. The objective of this chapter is to investigate the efficiency of soft computing methods when incorporated into the solution of computationally intensive earthquake engineering problems. Two methodologies are proposed in this work where limit-state probabilities of exceedance for real world structures are determined. Neural networks based metamodels are used in order to replace a large number of time-consuming structural analyses required for the calculation of a limit-state probability. The Rprop algorithm is employed for the training of the neural networks; using data obtained from appropriately selected structural analyses.