A decision-making system for large span bridge inspection intervals based on sliding window DFNN

Abstract

To avoid expert inspection, this study develops a decision-making system for large-span bridge inspection intervals based on dynamic fuzzy neural networks (DFNN), which can find knowledge from existing inspection data. A sliding window is introduced to enable the system to learn incrementally so that the system can update along with the bridge degradation. Tsing Ma Bridge is adopted as a prototype bridge while its rating system established based on the fuzzy-analytic hierarchical process (Fuzzy-AHP) method is employed to generate training and testing samples. The capability of the system in finding the relationship between the rating indexes and the rating scores as well as renewing itself with the bridge degradation is then verified. And the influence of the length of the window is investigated. The research shows that the method can make accurate decisions for bridge inspection intervals after being trained by existing data.