Bridge Maintenance Planning Framework Using Machine Learning, Multi-Criteria Decision Analysis and Evolutionary Optimization Models (vol 141, 104460, 2022)

Although various tools and procedures have been developed to help transportation engineers to 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 aims of this study are to maximize the performance condition level of a bridge network and decrease maintenance costs by planning maintenance treatments appropriately. The framework includes bridge performance impact assessment, machine learning models, multi-criteria decision analysis model and genetic algorithm optimization model. The study analyzed 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.