Performance of Bridge Envelope During Earthquake Using Finite Element and Artificial Neural Network Techniques

Background: Bridges are one of the most critical parts a transportation network that may be damaged during earthquakes and it is necessary to have prediction model for bridge responses under seismic loads can extended other situations. Soil stiffness significantly affects load distribution when soil, piles, abutment, superstructure all act as combined system resist lateral loading on bridge. Methods: A two-dimensional (2D) integral abutment (IAB) with soil springs around piles behind abutments 18.3m, 35.4m, 64.5m spans respectively, was developed finite element (FE). The input variables were span, backfill height, piles. Also, Artificial Neural Network (ANN) examined pile force, displacement, head moment, girder axial moment. Results: Using FE response medium span ( i.e ., 123.6m) large 249m) by ANN performed. Findings show has an important effect displacement. best performance related high intermediate clay pile. Conclusion: Stiffness bending moment at abutment.