Automating Infrastructure Maintenance Contracting: A Hybrid Markov–Machine Learning Framework for Predictive Asset Management and Risk Financing

Abstract

Uncertainty in infrastructure deterioration and maintenance cost forecasting continues to weaken the effectiveness of conventional maintenance contracts, contributing to budget overruns, delayed interventions, and inefficient risk allocation. This study develops a machine learning (ML)–enhanced adaptive performance-based maintenance contract (APBMC) framework that integrates hybrid Markov–ML deterioration forecasting, dynamic payment adjustment logic, and parametric risk-sharing provisions within a unified decision support structure for bridge maintenance. The framework is calibrated using a 10-year dataset from 68 US bridges (2014–2023), incorporating inspection records, structural health monitoring (SHM) summaries, traffic loading, maintenance histories, and climate-exposure variables. A hybrid Markov–ML architecture is used to predict condition-state transitions and quantify uncertainty, and the resulting forecasts are translated into contract trigger points, adaptive maintenance responses, payment adjustments, and risk financing activation rules. The framework is evaluated through historical back-testing, scenario-based simulation, and sensitivity analysis of trigger thresholds, calibration settings, and risk-sharing configurations. Results indicate 91% overall predictive accuracy, with calibration error below 3% in the contract-relevant trigger region. Relative to the lump-sum baseline, the proposed APBMC framework reduces mean life-cycle maintenance cost by 22%, improves response time to critical repairs by 31%, and lowers major rehabilitation frequency by up to 18%. The integrated risk financing layer performs effectively under both condition-based triggers, with annual activation probabilities of 0.14–0.36, and flood-based triggers, with probabilities of 0.05–0.09, reducing the likelihood of unfunded major rehabilitation from 18% to below 5%. Sensitivity analysis further identifies stable operating regions for condition thresholds and model-mixing settings that improve cost certainty while maintaining resilience. The proposed APBMC framework demonstrates the value of coupling predictive analytics, adaptive contracting, and risk financing to support more resilient, transparent, and performance-oriented infrastructure maintenance governance.