Counterpropagation Neural Network Model for Steel Girder Bridge Structures

Abstract

Bridge rating is based on the method of design: working stress design (WSD) or load factor design (LFD). A large number of older bridges were rated based on the WSD code. These WSD-based bridge ratings now have to be converted to the LFD-based rating. The LFD-based rating of steel bridges requires a detailed description of the steel girder’s geometric properties, which may not be available. In this article, a counterpropagation neural network model is presented for estimating the detailed section properties of steel bridge girders needed in the LFD-based rating based on the three cross-sectional properties used in the WSD-based rating of bridges: cross-section area, moment of inertia, and section modulus. It is demonstrated that, with proper training of the network using both standard wide-flange shape and representative plate girder data, the proposed model can generate the detailed section properties needed for LFD-based rating of steel bridges quite accurately. The result of this research can be used in an intelligent decision support system (IDSS) to help bridge engineers convert a WSD-based bridge rating to the LFD-based rating.