Evaluation of Unknown Foundations of Bridges Subjected to Scour: Physically Driven Artificial Neural Network Approach

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. Results showed that the proposed ANN models were able to make successful predictions about the foundation type and the embedment depth for deep foundations. In addition, the degree of uncertainty in the models’ predictions was evaluated by performing the random subsampling method. Graphs of the probability of exceedance were generated that allowed for factoring the predicted pile depth on the basis of a reasonable probability of failure caused by scour. As a consequence, departments of transportation can adopt a similar methodology to reclassify the bridges with unknown foundations included in the National Bridge Inventory.

• Record URL:
  • https://doi.org/10.3141/2433-04
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309295215
• Authors:
  • Yousefpour, Negin
  • Medina-Cetina, Zenon
  • Briaud, Jean-Louis
• Publication Date: 2014

Language

• English

Media Info

• Media Type: Print
• Features: Figures; Maps; Photos; References; Tables;
• Pagination: pp 27–38
• Monograph Title: Geology and Properties of Earth Materials 2014
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2433
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Bridge design; Bridge foundations; Bridge substructures; Bridge superstructures; Loads; Neural networks; Scour
• Identifier Terms: National Bridge Inventory
• Geographic Terms: Texas
• Subject Areas: Bridges and other structures; Geotechnology; Highways; I24: Design of Bridges and Retaining Walls;

Filing Info

• Accession Number: 01515043
• Record Type: Publication
• ISBN: 9780309295215
• Report/Paper Numbers: 14-3434
• Files: TRIS, TRB, ATRI
• Created Date: Feb 21 2014 3:16PM