Integrating climate change predictions into infrastructure degradation modelling using advanced markovian frameworks to enhanced resilience.

This study introduces a prognostic model that quantifies infrastructure degradation in highway systems, incorporating the impacts of climate change using an advanced Markovian framework. By assimilating extensive historical maintenance records and detailed climatic data, the model employs a multi-tiered exponential erosion risk framework to enhance predictive accuracy. Our findings indicate a 15-20% acceleration in degradation rates under projected climate scenarios, emphasizing the necessity for climate-adaptive infrastructure management strategies. Utilizing maximal likelihood estimation, the model corrects sample distortion biases, resulting in a 30% improvement in the accuracy of degradation forecasts compared to conventional models. This accuracy enables maintenance cost savings of up to 25% by optimizing repair timings, thus avoiding premature interventions and reducing costs associated with reactive maintenance strategies. The validated model provides a robust tool for strategic planning and adaptive maintenance of highway systems, promoting resilient infrastructure management in the face of evolving climatic conditions. This research ensures that infrastructure professionals can anticipate and mitigate the impacts of climate change, optimizing maintenance budgets and extending the service life of highway assets.