Abstract
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.
Highlights
A fragility curve describes the relationship between a seismic intensity measure and the corresponding probability of exceedance as determined by a specified limit state
The ability to predict neural network is dependent on input parameters such as IA, IC, cumulative absolute velocity (CAV), and specific energy density (SED) which are representative of earthquake ground motions
The trained Artificial neural network (ANN) is shown to be effective in predicting responses related to limit states using a set of influence variables, including inputs and outputs factors
Summary
A fragility curve describes the relationship between a seismic intensity measure and the corresponding probability of exceedance as determined by a specified limit state. To evaluate the seismic fragility curves for isolated bridges, Siqueira et al utilized ADINA software for bearing FE models and compared it to the experimental results [6]. DIANA finite element (FE) program was used to establish the reinforced concrete frame models and simulation of progressive collapse. To investigate resistance against progressive collapse, Brunesi and Nascimbene presented a procedure for reinforced concrete buildings subjected to blast loading using a fiber-based model [9]. As demonstrated by Moller et al, there is an applicable method for the improvement of the numerical efficiency of the fuzzy stochastic structural collapse simulation under consideration of uncertainty [10]. Because analytical fragility curves based on nonlinear time history analysis have some inherent limitations, metaheuristic methods based on soft computing have so far been little studied, including neural network prediction. Some metaheuristic methods such as neural networks and fuzzy logic have been proposed and compared with the results of structural analysis methods which are reliable
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