Epistemic uncertainty assessment using Incremental Dynamic Analysis and Neural Networks

Abstract

Incremental dynamic analysis (IDA) is a powerful method for the seismic performance assessment of structures. IDA is also very efficient for handling uncertainty due to the mechanical properties of the structure. In the latter case, IDA should be performed within a Monte Carlo framework requiring the execution of a vast number of nonlinear response history analyses. The increased computing effort renders the calculation of performance statistics time-consuming and hence the method is not always practical. We propose a scheme based on artificial neural networks (NN) in order to reduce the computational effort. Within a Monte Carlo approach, trained NN can rapidly generate a large sample of IDA curves and therefore allow us to easily calculate useful response statistics and fragility curves. The implementation of the proposed approach is quick, straightforward and quite accurate.