Using artificial neural networks in the design of orthotropic bridge decks

For orthotropic bridge decks a lot of progress has been made in the development of codes to aid in the design process, in addition to software tools for numerical analysis and design. However, professional software tools will not aid the designer in choosing a preliminary economic layout at the conceptual design stage. Designers would go through iterative, lengthy and expensive procedures to reach the best configuration. The present research provides a methodology to investigate the contingency of using artificial neural networks for conceptual design of orthotropic steel-deck bridge. A neural network model was trained with different combinations of dimensions, and eight types of safety checks were performed on all of them. The resulting network can predict whether the deck is safe or not. It is found that this approach for the selection of orthotropic deck dimensions is a better and cost-effective option compared with international codes or expert opinion.