Indian Journal of Science and Technology

Abstract

Building damage level due to earthquake is widely related to the features of the record which consist of many parameters. Although it is difficult to realize the ground motion parameters that have high influence on building performance, the vital parameters that may cause building damage may be considered as PGA, PGV, PGD, PGA/PGV, PGA/PGD, PGV/PGD, frequency content, effective time duration, fault line distance of the earthquake. In this study, these parameters were selected in order to determine more effective parameter on the building performance. For this aim, a model of Artificial Neural Network (ANN) algorithm was used as an efficient tool consisting of the obtained results of nonlinear time history analysis of samples. The 200 records, produced by strike-slip fault mechanism, were selected for the soil type C (Z3) according to the Turkish Earthquake Code [1]. A six story R/C frame building, with three various spans were analyzed via IDARC-2D software. The Park and Ang damage index was used in order to evaluate the vulnerability of buildings. The results showed that the ANN can be able to determine the effective parameters of ground motions with sufficient correlation. Also the most and least significant parameters of earthquake are discussed based on the results of the analysis.

Keywords: Artificial Neural Network, Building Damage, Ground Motion Parameters, Nonlinear Time History Analysis, Reinforced Concrete Building