Artificial Neural Network Model of Bridge Deterioration

Abstract

Accurate prediction of bridge condition is essential for the planning of maintenance, repair, and rehabilitation. An examination of the assumptions (for example, maintenance independency) of the existing Markovian model reveals possible limitations in its ability to adequately model the procession of deterioration for these purposes. This study uses statistical analysis to identify significant factors influencing the deterioration and develops an application model for estimating the future condition of bridges. Based on data derived from historical maintenance and inspection of concrete decks in Wisconsin, this study identifies 11 significant factors and develops an artificial neural network (ANN) model to predict associated deterioration. An analysis of the application of ANN finds that it performs well when modeling deck deterioration in terms of pattern classification. The developed model has the capacity to accurately predict the condition of bridge decks and therefore provide pertinent information for maintenance planning and decision making at both the project level and the network level.