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Fragility assessment of RC bridges using numerical analysis and artificial neural networks

  • Razzaghi, Mehran S. (Department of Civil Engineering, Qazvin Branch, Islamic Azad University) ;
  • Safarkhanlou, Mehrdad (Department of Civil Engineering, Qazvin Branch, Islamic Azad University) ;
  • Mosleh, Araliya (CONSTRUCT-LESE, Department of Civil Engineering, Faculty of Engineering, University of Porto) ;
  • Hosseini, Parisa (Department of Civil Engineering, Qazvin Branch, Islamic Azad University)
  • Received : 2017.11.22
  • Accepted : 2018.07.16
  • Published : 2018.10.25

Abstract

This study provides fragility-based assessment of seismic performance of reinforced concrete bridges. Seismic fragility curves were created using nonlinear analysis (NA) and artificial neural networks (ANNs). Nonlinear response history analyses were performed, in order to calculate the seismic performances of the bridges. To this end, 306 bridge-earthquake cases were considered. A multi-layered perceptron (MLP) neural network was implemented to predict the seismic performances of the selected bridges. The MLP neural networks considered herein consist of an input layer with four input vectors; two hidden layers and an output vector. In order to train ANNs, 70% of the numerical results were selected, and the remained 30% were employed for testing the reliability and validation of ANNs. Several structures of MLP neural networks were examined in order to obtain suitable neural networks. After achieving the most proper structure of neural network, it was used for generating new data. A total number of 600 new bridge-earthquake cases were generated based on neural simulation. Finally, probabilistic seismic safety analyses were conducted. Herein, fragility curves were developed using numerical results, neural predictions and the combination of numerical and neural data. Results of this study revealed that ANNs are suitable tools for predicting seismic performances of RC bridges. It was also shown that yield stresses of the reinforcements is one of the important sources of uncertainty in fragility analysis of RC bridges.

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