Expanding the use of smart braced frames to govern the seismic response of structures by providing ductility and elasticity has been hampered and delayed by cost indexes. The braces in frames comprise two segments of expensive shape memory alloys (SMAs) and high-strength steel with high stiffness. These smart materials can reduce seismic damage by providing stiffness, yielding, and phase shifting. In this study, the length of the SMA segments in three- and six-story frames (applied either at all floors or as part of a dual system) was increased to determine the optimal length at a constant period. Performance levels and fragility curves were obtained to evaluate the seismic behavior of the optimized frame. The response modification factor determined based on the static pushover, incremental nonlinear dynamic analysis, and linear dynamic analysis suggests the ductility and over-strength of the optimized frame. The probability of being in or exceeding each damage state was determined with a Monte Carlo analysis and was acceptable and in accordance with previous deterministic analysis results.

This study focuses on seismic fragility assessment of horizontal curved bridge, which has been derived by neural network prediction. The objective is the optimization of structural responses of metaheuristic solutions. A regression model for the responses of the horizontal curved bridge with variable coefficients is built in the neural networks simulation environment based on the existing NTHA data. In order to achieve accurate results in a neural network, 1677 seismic analysis was performed in OpenSees. To achieve better performance of neural network and reduce the dimensionality of input data, dimensionality reduction techniques such as factor analysis approach were applied. Different types of neural network training algorithm were used and the best algorithm was adopted. The developed ANN approach is then used to verify the fragility curves of NTHA. The obtained results indicated that neural network approach could be used for predicting the seismic behavior of bridge elements and fragility, with enough feature extraction of ground motion records and response of structure according to the statistical works. Fragility curves extracted from the two approaches generally show proper compliance.