Identification of Bridges in Bars Database Related by WSD Method and an Intelligent Decision Support System to Convert the WSD-Based Rating to LFD-Basing Rating

摘要

An intelligent decision support system (IDSS) has been developed to help bridge engineers convert a WSDbased bridge rating to the LFD-based rating using two different soft computing approaches: case-based reasoning (CBR) and artificial neural networks. The former is used to predict the lateral bracing requirements and the latter is used to determine the LFD-based section properties. The LFD-based rating of steel bridges requires a detailed description of the steel girders geometric properties that may not be available. A counterpropagation neural (CPN) 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 CPN network using both standard wideflange shapes and representative plate girder data, the proposed model can generate the detailed section properties needed for LFD-based rating of steel bridges accurately. The training set for the CPN network was based on the AISC W-shape database plus 100 plate girder designs. It can be readily extended to include additional plate girder designs. The CBR module for determining the steel bridge girder lateral bracing requirements is developed using lateral bracing design data from ODOT standard bridge design drawings dating as far back as 1939 and as recently as 1997. The IDSS has been developed using the object-oriented features of Microsoft Visual Basic and the spreadsheet program, Microsoft Excel (the version released in 2000). The computational models and the IDSS created in this research can be used by bridge engineers as an intelligent decision support system to convert the WSD-based bridge rating to LFDbased bridge rating substantially faster than the conventional approach.