BIONIC ARCHITECTURE BY USING DEEP LEARNING ALGORITHMS

The main reason of Bionic Architecture is the designs of curved forms reminiscent of structures in biology and fractal mathematics. In the past decades, the methods that are much more economical to achieve reasonable accuracy are always required. In recent years, there has been a growing interest in using Artificial Neural Networks (ANNs), a computing technique that works in a way similar to that of biological nervous systems. Many researchers used ANN to study a beam using multilayer perceptron (MLP) ANN. Furthermore, another application of ANN is to evaluation of the failure probability and safety levels of structural systems. Bakhshi and Vazirizade used a radial network in order to predict the stiffness of the each member in a frame according to its response to a record. In fact, they showed ANN can provide a mapping from the maximum story drifts to columns stiffness. Gomes et al. and Bucher used ANN for obtaining the failure probability for a cantilever beam and compared ANN with other conventional methods. They found that ANN methods that can approximate the limit state function may decrease the total computational effort on the reliability assessment, but more studies, including large systems with non-linear behavior must be studied. Elhewy et al. studied about the ability of ANN model to predict the failure probability of a composite plate. They compared the performance of the ANN-based RSM (Response Surface Methods) (ANN-based FORM and ANN-based MCS) with that of the polynomial-based RSM. Their results showed that the ANN-based RSM wasmore efficient and accurate than the polynomial-based RSM. It was shown that the RSM may not be precise when the probability of failure was extremely small as well as the RSM requires a relatively long computation time as the number of random variables increases. Zhang and Foschi employed ANN for seismic reliability assessment of a bridge bent with and without seismic isolation, but in that case they used explicit limit states. However, most of them are utilized explicit and approximate limit states and more focused on the reliability assessment of components by ANN. In this regard, this study is focused on two separate parts; (1) localization and quantification of structural damages using ANN; (2) seismic reliability assessment of one structure using ANN-based MCS.

Keywords:

Artificial neural network , Monte Carlo simulation , Architecture