SEISMIC FRAGILITY SURFACE ANALYSIS OF RC FRAME STRUCTURES BASED ON BP NEURAL NETWORKS: ACCOUNTING FOR THE EFFECTS OF GROUND MOTION INTENSITY AND DURATION

Abstract

Comparing with short-duration ground motions, long-duration ground motions may intensify the damage and increase the failure probability of structures. Therefore, it is necessary to thoroughly investigate the influence of ground motion duration characteristics on the seismic fragility analysis results. A seismic fragility surface analysis approach based on back propagation (BP) artificial neural networks was proposed. It can account for the effect of both ground motion intensity and duration. Seismic fragility analysis was conducted to get the fragility surfaces under different damage levels. Three reinforced concrete fame structures with different heights were taken as the study cases. Long- and short-duration record sets were selected as the inputs. BP neural network models were employed to build the relationship between the ground motion intensity measures and structural responses, and the seismic fragility surfaces of the investigated structures were obtained. The validity of the proposed approach was discussed. The analysis results show that the accuracy of the established BP neural network model is high. It indicates that the fragility analysis results by this approach is reliable. Comparing with the conventional procedures, the neural network is capable of building more effective correlation models between the ground motion duration and structural damage to obtain fragility analysis results that account for ground motion duration. This approach can be further expanded to include more ground motion characteristics into the program for seismic fragility analysis. It has a definite application prospect.