Harnessing data-driven and explainable ensemble machine learning for infrastructure deterioration prediction through real-world benchmarking of models

This study presents a comprehensive comparison of six machine learning algorithms for predicting structural deterioration rates using real-world data from Indian bridges, roads, and pipelines, and introduces a robust, interpretable, and uncertainty-aware framework for infrastructure asset management.