RESEARCH ARTICLE


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



Maryam Naji1, Ali Akbar Firoozi2, *
1 Department of Civil Engineering, Higher Education Complex of Saravan, Saravan, Iran
2 Department of Civil Engineering, Faculty of Engineering & Technology, University of Botswana, Gaborone, Botswana


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 185
Abstract HTML Views: 83
PDF Downloads: 131
Total Views/Downloads: 399
Unique Statistics:

Full-Text HTML Views: 117
Abstract HTML Views: 68
PDF Downloads: 80
Total Views/Downloads: 265



Creative Commons License
© 2022 Naji and Firoozi.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Civil Engineering, Faculty of Engineering & Technology, University of Botswana, Gaborone, Botswana; E-mail: a.firoozi@gmail.com


Abstract

Background:

Bridges are one of the most critical parts of a transportation network that may be damaged during earthquakes and it is necessary to have a prediction model for bridge responses under seismic loads that can be extended to other situations. Soil stiffness significantly affects load distribution when soil, piles, abutment, and superstructure all act as a combined system to resist lateral loading on a bridge.

Methods:

A two-dimensional (2D) model of integral abutment bridge (IAB) with soil springs around piles and behind the abutments for 18.3m, 35.4m, and 64.5m spans respectively, was developed with finite element (FE). The input variables were bridge span, backfill height, soil stiffness behind abutment, and soil stiffness around piles. Also, Artificial Neural Network (ANN) was examined for pile lateral force, pile displacement, pile head moment, girder axial force, and abutment moment.

Results:

Using FE the prediction of critical response for medium span (i.e., 123.6m) and large span (i.e., 249m) by ANN was performed. Findings show that backfill stiffness has an important effect on lateral displacement. The best performance was related to high stiffness backfill with intermediate clay around the pile.

Conclusion:

Stiffness of clay around the pile has an important effect on lateral displacement, pile lateral force, pile bending moment, girder axial force, and girder bending moment at the abutment.

Keywords: Prediction, Earthquake, Span, Backfill height, Soil stiffness, Pile, Lateral force.