Author Type

Graduate Student

Date of Award

1994

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Pandya, Abhijit S.

Abstract

A neural network based model for prediction of bridge condition rating is proposed. The back-propagation algorithm is used to train the network to recognize the pattern of deterioration of bridges and use this knowledge in predicting the future condition rating of a bridge. The various factors which influence the deterioration rate are considered as input to the system. The model then predicts the condition rating of the three major sub-components of a bridge viz. the deck, sub-structure and the super-structure. Fuzzy logic is used to evaluate the overall condition rating of the bridge using the condition rating of the components. To demonstrate the superiority of the neural network model over the traditional models, the history of the deterioration rates for the components were also considered in the prediction of their future condition. The proposed system is versatile and can be easily extended to include other parameters and updated from time to time without much effort.

Extent

137 p.

Comments

College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 1994.|College of Engineering and Computer Science

Identifier

FADT15096

Format

pdf

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