Artificial neural network models for the prediction of bridge deck condition ratings

Condition assessment is extremely vital in the decision making process of how millions of taxpayer dollars are spent on repairing or improving aging infrastructure. This research looks at developing artificial neural network (ANN) models to predict the condition ratings of concrete highway bridge decks in Michigan. Historical condition assessments chronicled in the national bridge inventory (NBI) database were used to develop the ANN models. The high complexity of the NBI database due to non-linear variable relationships, subjectivity from manual inspections, and missing data has limited utilization of the database in the development of prediction models. However, ANNs can produce correct responses even in the presence of noise or uncertainty in the training data, and can satisfactorily predict the outcome of complex problems or those with a high degree of nonlinear behavior. Two types of artificial neural networks, multi-layer perceptrons (MLPs) and ensembles of neural networks (ENNs), were developed to predict the condition ratings of concrete bridge decks. A bridge management system (BMS) that optimizes the allocation of repair and maintenance funds for a network of bridges is proposed. The BMS uses a genetic algorithm and the trained ENN models to predict bridge deck degradation. The genetic algorithm aims to minimize the repair costs over a pre-defined planning horizon while maintaining adequate bridge deck conditions. Employing the proposed BMS leads to the selection of optimal bridge repair strategies to protect valuable infrastructure assets while satisfying budgetary constraints.