Abstract
The positioning of vehicular live loads on a bridge superstructure to achieve the maximum force effects in the bridge pier is an important design issue. For highway bridges, the worst load positioning for the superstructure design usually does not produce the worst force effects for the pier design. As a result, the correct positioning of the loads for the pier is left to engineering judgment. This paper investigates an implementation of neural networks to predict the worst load positioning for the bridge pier. The networks predict the load positioning for both single and multiple column piers given input parameters that describe the pier and bridge configuration. The procedure presented herein is intended to offer an alternative to the trial and error load positioning method used by many practicing engineers.
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