A Climate‑Risk‑Adjusted Asset Health Index Framework for Railway Infrastructure: Integrating Wireless Sensor Networks, Prognostics, and Intelligent Asset Management Platforms

Abstract

Railway operators have the double problem of aged infrastructure, which requires cost-effective renovation, and growing risk factors caused by climate change, which increase the risk of failure. Although condition monitoring and predictive, risk-adjusted maintenance are advocated, few organizations, on average, successfully convert data variety into cohesive, decision-supporting risk-prioritization of different assets. In this work, an integrated, advanced framework will be offered to present a risk-adjusted Asset Health Index (AHI) using continuous risk analysis based on WSN, data analytics for RUL, and enterprise-wide integrated risk-aware management planning. This will use literature-reviewed best practices in aggregate risk calculation for Asset Health Index and railway infrastructure health analysis, with an additional "context layer" tailored to assess health based on vulnerability to climate-related risks, including heat buckling, flood and scour, and debris from windstorms. The work will also present decision-support bibliographic libraries for risk practices, aggregation, treatment, and risk thresholds. Two simulation scenarios will also be included to show how climate risk accelerates health degradation and changes prioritization. This research provides a approach blueprint checklist to help railway organizations leap over data fragmentation and move forward in smart, risk-based optimization of infrastructure maintenance.