Seismic Damage-Level Prediction Model of Cable-Stayed Bridge Using Nonlinear Time History Analysis for Intelligent Structural Health Monitoring under Earthquake Events

Abstract

Abstract The current condition of a bridge should be given the highest priority to ensure its safety and its serviceability to the bridge users. Therefore, this paper explained the development of an intelligent decision-support tool in Bridge Health Monitoring system using a Neural Network model as a prediction of seismic damage-level for cable-stayed bridge. A total of eight earthquake loads scaled to various Peak Ground Acceleration (PGA) values. The input and output data which were fed into Feed Forward Artificial Neural Network (ANN) for damage level prediction model were developed based on acceleration responses from the Non-Linear Time History (NLTH) analysis of the cable-stayed bridge. A total of 16620 data were used as the input data. The damage-level categorization is based on FEMA 356. Data used for the ANN training are 70% for training, 15% for validation, and 15% for testing. The damage level prediction can greatly help bridge authority in order to maintain their bridge structure integrity by identifying and predicting the probability of damage occurring under earthquake loads.