An Inspection-Driven Decision-Support Framework for Deterioration Prediction and Maintenance Optimization of Highway Bridges Without Historical Inspection Records

Abstract

Maintenance planning for highway bridges without historical inspection records remains challenging because conventional deterioration models typically require long-term data for calibration. This study proposes an inspection-driven decision-support framework that integrates bridge-specific engineering calibration, Markov deterioration modelling, an independent condition-rating-based Remaining Service Life (RSL) assessment, and Markov Decision Process (MDP) optimization. The framework was demonstrated on a 26-year-old six-span composite highway bridge in Türkiye. A comprehensive inspection yielded a weighted Bridge Condition Index of 2.98, which was used to calibrate the bridge-specific Markov deterioration model. The model predicted attainment of the State-4 intervention threshold after approximately 15.71 years under a do-nothing scenario, while the independent condition-rating assessment estimated an RSL of approximately 17 years for the governing pier columns. The optimized finite-horizon MDP policy reduced the expected discounted life-cycle cost by 89.75% relative to the do-nothing strategy, while sensitivity analyses confirmed the stability of the principal maintenance policy under the examined modelling and economic perturbations. The proposed framework therefore provides a practical, transparent, and progressively updateable methodology for deterioration prediction and maintenance planning for bridges with limited historical inspection information.