Bridge maintenance planning framework using machine learning, multi-attribute utility theory and evolutionary optimization models

Abstract

Although various tools and procedures have been developed to help transportation engineers objectively evaluate bridge maintenance needs, selecting and scoping projects still relies on engineering judgment. The present paper intends to help engineers evaluate and maintain bridges by developing a comprehensive bridge maintenance planning framework (BMPF) within financial and performance constraints. The paper's objectives are to maximize the performance condition level of bridges and to minimize the maintenance cost by optimally planning the maintenance treatments. The framework includes bridge performance impact assessment, machine learning models, multi-attribute utility theory ranking model and genetic algorithm optimization model. The study analysed 95 bridges in a network with an 84% accuracy machine learning model prediction. Decision-makers' preferences were considered to rank all bridges using Multi-Attribute Utility Theory. 19 bridges were then chosen for maintenance based on budget and performance using a genetic algorithm model. The BMPF was observed to improve project productivity, reduce down time, and improve bridge inventory condition. Future research can explore the use of other optimization approaches and also include traffic flow and construction cost analysis for a better maintenance cost estimation. The machine learning model for performance prediction can be enhanced by utilizing different techniques.