Seismic reliability assessment of structures using artificial neural network

Localization and quantification of structural damage and estimating the failure probability are key outputs in the reliability assessment of structures. In this study, an Artificial Neural Network (ANN) is used to reduce the computational effort required for reliability analysis and damage detection. Toward this end, one demonstrative structure is modeled and then several damage scenarios are defined. These scenarios are considered as training data sets for establishing an ANN model. In this regard, the relationship between structural response (input) and structural stiffness (output) is established using ANN models. The established ANN is more economical and achieves reasonable accuracy in detection of structural damage under a set of ground motions. Furthermore, in order to assess the reliability of a structure, five random variables are considered. These are columns' area of the first, second, and third floor, elasticity modulus, and gravity loads. The ANN is trained by suing the Monte Carlo Simulation (MCS) technique. Finally, the trained neural network specifies the failure probability of the proposed structure. Although MCS can predict the failure probability for a given structure, the ANN model helps simulation techniques to receive an acceptable accuracy and reduce computational effort.