Journal article

Evaluation of unknown foundations of bridges subjected to scour physically driven artificial neural network approach

N Yousefpour, Z Medina-Cetina, JL Briaud

Transportation Research Record Journal of the Transportation Research Board | SAGE Publications | Published : 2014

Abstract

Missing substructure information has impeded the safety assessment of bridges with unknown foundations, especially for scour-prone bridges. An approach based on artificial neural networks (ANNs) was developed to identify the inherent patterns in the substructure design of bridges with commonly available evidence (e.g., geometric characteristics of superstructures, loading conditions, soil properties, year built, and location) and then to generalize them further to bridges with unknown foundations. The proposed ANN models were trained with information collected for an inventory of bridges with available foundation records located in the Bryan District of the Texas Department of Transportation..

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University of Melbourne Researchers

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Funding Acknowledgements

This project was sponsored by the Texas DOT; John Delphia was the technical contact. The authors also thank Keyvon Jahedkar and Anthony Garcia from the Texas DOT Bryan District for their assistance in collecting the bridge information. The valuable help of Mark McClelland on load calculations and Brittany Hanly on populating and processing the database is greatly appreciated as well.