Prediction of seismic demand model for pulse-like ground motions using artificial neural networks

Abstract

A probabilistic seismic demand model that relates ground motion intensity measures (IMs) to the structural demand measures is a useful tool for reliability analysis of structures. It is common to utilize the scalar seismic parameters or a vector of a few seismic parameters to reveal ground motion uncertainty. However, for the qualification of an IM for representing the ground motion uncertainty, a larger vector of greater seismic component is required. This study aims to use more parameters as vector IMs in the demand model to achieve better estimation of the ground motion uncertainty. In this study, three-layer feed forward neural network was used to predict the seismic demand model of the mid-rise reinforced concrete buildings for pulse-like ground motions. The results indicate that due to the complexity of the relationship between seismic response of structures and seismic intensity parameters, using artificial neural networks method is more suitable than numerical methods to show uncertainties.

A Multi Record Based Artificial Near Fault Ground Motion Generation Method

“…Near fault ground motions generally occur near earthquake rupture faults. Near fault ground motions and far fault ground motions are significantly different [ 1 , 2 ], because near fault ground motions are believed to have the following characteristics : high PGV/PGA and PGD/PGA ratios, long-period velocity pulse and permanent ground displacements [ [3] , [4] , [5] ]. These characteristics are affected by fracture mechanism and fracture line direction.…”