ANALYSIS OF BRIDGE CONDITION RATING DATA USING NEURAL NETWORKS

Currently, bridges are evaluated using either a visual inspection process or a detailed structural analysis. When bridge evaluation is conducted by a visual inspection, a subjective rating is assigned to a bridge component. With analytical evaluation, the rating is computed based on the load applied and the resistance of the bridge component. There have been several attempts to correlate the subjective rating to the analytical rating. The conventional statistical analyses, as well as methods based on fuzzy logic, have not been very successful in providing a clear relationship between the 2 rating systems. This paper describes the application of neural network systems in developing the relation between subjective ratings and bridge parameters as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on bridge parameters. The specific application problem for railroad bridges in the commuter rail system in the Chicago metro area is presented.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/08859507
• Corporate Authors:

  Blackwell Publishing

  350 Main Street
  Malden, MA  United States  02148
• Authors:
  • Cattan, J
  • Mohammadi, J
• Publication Date: 1997

Language

• English

Media Info

• Features: References;
• Pagination: p. 419-429
• Serial:
  • Microcomputers in Civil Engineering
  • Volume: 12
  • Issue Number: 6
  • Publisher: Blackwell Publishing
  • ISSN: 0885-9507

Subject/Index Terms

• TRT Terms: Bridges; Inspection; Investigation of structure; Neural networks; Rail transit; Railroad bridges; Structural analysis; Structural deterioration and defects
• Uncontrolled Terms: Bridge components
• Geographic Terms: Chicago Metropolitan Area; United States
• Subject Areas: Bridges and other structures; Design; Highways; Public Transportation; I24: Design of Bridges and Retaining Walls;

Filing Info

• Accession Number: 00792608
• Record Type: Publication
• Files: TRIS
• Created Date: May 16 2000 12:00AM