Artificial neural network-based response surface methods for reliability analysis of pre-stressed concrete bridges

Two artificial neural network (ANN)-based response surface methods for reliability analysis of pre-stressed concrete bridges are presented. The first method is the traditional ANN-based response surface method, originally introduced by Papadrakakis et al. in 1996 (Papadrakakis, M., Papadopoulos, V., and Lagaro, N., 1996. Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation. Computer Methods in Applied Mechanics and Engineering, 136, 145-163), which is applied here to the reliability analysis of pre-stressed concrete bridges. The second method is an improved ANN-based response surface method developed recently by the authors for the reliability analysis of truss structures, in which the key idea is that the uniform design method (UDM) is adopted to select training data for establishing an ANN model, thereby greatly improving the quality of training datasets for establishing an ANN model and dramatically reducing the required number of training datasets. There are two main objectives of the present work. Firstly, an attempt is made to extensively examine the performance of the traditional ANN-based response surface method since no detailed study has been carried out to investigate the effectiveness of this ANN-based response surface method on the basis of the reliability analysis of complicated structures such as pre-stressed concrete bridges. Secondly, the recently developed ANN-based response surface method is extended to the reliability analysis of pre-stressed concrete bridges. A detailed numerical investigation is carried out to compare the performance of the two methods.