Developing Predictive Models of Superstructure Ratings for Steel and Prestressed Concrete Bridges

A large number of deficient bridges may endanger the public and affect the economy at a broader scale. Bridge superstructure rating is a critical element that affects the overall sufficiency rating of a bridge. Accurately predicting the superstructure performance of a bridge may help agencies better prioritize their resources for maintenance and repairs. The main objective of the paper is to utilize data mining techniques to develop reliable models to predict the superstructure rating of bridges. This research utilizes the national bridge inventory (NBI) database as the main source of information. A focused subset was created based on the defined scope of the research: year built (≥ 1955), kind of material-design (prestressed concrete and steel), type of design (stringer/multi-beam or girder), and deck type (concrete cast-in-place). This paper takes three approaches for model development including linear regression, decision tree, and neural network. The best model was identified for each superstructure material through comparisons among different models. In addition, a discussion of individual variables and their contributions to predict superstructure rating was performed. The identified models provide insight into when a bridge superstructure needs maintenance and reconstruction.

• Record URL:
  https://doi.org/10.1061/9780784479827.087
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784479827
• Supplemental Notes:
  • © 2016 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Contreras-Nieto, Cristian
  • Lewis, Phil
  • Shan, Yongwei
• Conference:
  • Construction Research Congress 2016
  • Location: San Juan Puerto Rico, United States
  • Date: 2016-5-31 to 2016-6-2
• Publication Date: 2016

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 859-868
• Monograph Title: Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan

Subject/Index Terms

• TRT Terms: Bridge superstructures; Cast in place concrete; Concrete bridges; Data mining; Decision trees; Linear regression analysis; Maintenance; Neural networks; Steel bridges
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01605820
• Record Type: Publication
• ISBN: 9780784479827
• Files: TRIS, ASCE
• Created Date: May 24 2016 3:03PM