Forecasting the Fracture: A Cost-Driven Machine Learning Framework for Optimal Bridge Maintenance Prioritization

Abstract

Aging civil infrastructure presents a critical economic and public safety challenge, with maintenance backlogs costing hundreds of billions of dollars. This study moves beyond simple condition prediction to develop and validate a comprehensive, cost-driven framework for optimizing bridge maintenance schedules. Leveraging a five-year (2020-2024) longitudinal cohort constructed from the U.S. National Bridge Inventory (NBI), we train a tuned gradient-PUBLIC boosted model (XGBoost) to predict the one-year-ahead probability of a bridge transitioning into a "Poor" or "Failing" condition. This probabilistic forecast is then integrated into a decision-theoretic framework that explicitly weighs the expected cost of failure against the fixed cost of proactive maintenance. By simulating this framework on a held-out test set, we identify an optimal, risk-based decision threshold that maximizes net cost savings. The economic simulation reveals a positive but modest net savings, underscoring the critical dependence of such strategies on model precision. To ensure transparency and build stakeholder trust, Explainable AI (SHAP) is used to dissect the model's logic, confirming that deck condition, traffic volume, and bridge age are the primary drivers of its predictions, aligning with established engineering principles. This work provides a rigorous, scalable, and fully articulated methodology for turning predictive insights into economically optimal, data-driven policy for critical infrastructure management, while also quantifying the profound impact of model precision on real-world economic viability.