The Hydrogen Readiness Index (HRI): A Hybrid Physics–Machine Learning Framework for Assessing Pipeline Hydrogen Compatibility

The transition to hydrogen within existing gas pipeline networks presents complex integrity, materials, and operational challenges that are not fully addressed by conventional design or asset integrity methodologies. This paper introduces the Hydrogen Readiness Index (HRI), a quantitative, multi-dimensional framework for assessing the readiness of steel pipelines for hydrogen service. The HRI integrates metallurgy, design and operating envelope, integrity condition, coatings and cathodic protection, environmental exposure, data quality, and operational controls within a unified weighted scoring architecture. A hybrid approach is adopted, combining deterministic physics-based rules and penalty functions with machine learning models to enhance sensitivity to nonlinear interactions and evolving operational behaviour, while preserving transparency and engineering defensibility. To accommodate variability in operator data availability, the framework is designed to operate across three defined data maturity levels, ranging from summary-based assessments to high-resolution, context-aware operational analyses. The HRI produces a normalised readiness score (0–100) and associated pillar-level diagnostics to support asset screening, prioritisation, and mitigation planning. This publication establishes the originating disclosure of the HRI framework and provides a foundation for consistent, auditable hydrogen readiness assessment applicable to transmission and distribution pipelines, with extensibility to pressure reduction stations and above-ground gas installations.