Project Details

Abstract

This research focuses on developing an advanced Maintenance Optimization System to enhance the operational efficiency of bridges within available budget. The research is vital due to the poor performance of bridges, as reflected in infrastructure reports by the American Society of Civil Engineers and aims to address the delays and inadequacies in current bridge maintenance strategies through a data-driven approach. The proposed system will leverage machine learning (ML) to predict the conditions and maintenance costs of bridge components, forming the basis for a novel optimization model that schedules maintenance tasks effectively. This dual approach ensures that limited resources are utilized in the most impactful way, extending the lifespan of bridge infrastructure while adhering to budget limitations. Outcomes include the creation of ML models capable of forecasting bridge conditions accurately and an optimization model that strategically schedules maintenance. These tools are expected to transform maintenance planning from a reactive to a proactive process, enhancing safety and extending the operational life of bridges.

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