Shannon Information Entropy for Seismic Reliability Assessment of Structures Using Self-Organizing Neural Networks

Abstract

Localization and quantification of structural damages and find a failure probability is the key important in reliability assessment of structures. In this study, a Self-Organizing Neural Network (SONN) with Shannon Information Entropy simulation is used to reduce the computational effort required for reliability analysis and damage detection. To this end, one demonstrative structure is modeled and then several damage scenarios are defined. These scenarios are considered as training datasets for establishing a Self-Organizing Neural Network model. In this regard, the relation between structural responses (input) and structural stiffness (output) is established using Self-Organizing Neural Network models. The established SONN is more economical and achieves reasonable accuracy in detection of structural damages under ground motion. Furthermore, in order to assess the reliability of structure, five random variables are considered. Namely, columns’ area of first, second and third floor, elasticity modulus and gravity loads. The SONN is trained by Shannon Information Entropy simulation technique. Finally, the trained neural network specifies the failure probability of purposed structure. Although MCS can predict the failure probability for a given structure, the SONN model helps simulation techniques to receive an acceptable accuracy and reduce computational effort.