Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{189627,
        author = {Raj J Mehta},
        title = {Stochastic Risk Modeling for Infrastructure Asset Failure Under Uncertainty},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7145-7158},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189627},
        abstract = {Infrastructure owners increasingly rely on risk-based asset management to allocate limited budgets across large, heterogeneous portfolios. However, operational tools often compress uncertainty into deterministic risk matrices or point estimates, limiting auditability and obscuring tail risk. This paper formalizes a stochastic decision architecture for infrastructure asset failure risk that preserves uncertainty structure from deterioration, failure-time, and consequence models through to governance-ready decision outputs. The architecture couples stochastic deterioration representations (e.g., Markov/state-space or gamma-process) with hazard-based failure models and probabilistic consequence severity, propagating aleatory and epistemic uncertainty via Monte Carlo simulation or analytic approximations. Outputs include time-dependent failure probability, expected present value of loss, and tail-risk metrics (e.g., CVaR), alongside ranking-stability controls that quantify decision confidence and identify decision-unstable assets for targeted inspection or data improvement. A transparent demonstration portfolio (N=50 assets; synthetic but realistic ranges) establishes how uncertainty-preserving rankings can diverge from deterministic risk-matrix scores, breaking ties and revealing tail-driven priorities. A climate stress-test scenario, treated as a regulatory-style stress test rather than a physical climate model, illustrates portfolio loss amplification (approximately 25% increase in expected present value of failure loss in the demonstration) and supports scenario-ready capital planning. Overall, the proposed decision architecture enables defensible prioritization by producing auditable rankings, explicit separation of mean versus tail risk, and stability-aware governance artifacts.},
        keywords = {infrastructure asset management; auditable decision architecture; stochastic deterioration; hazard/survival analysis; uncertainty quantification; ranking stability; CVaR; stress testing.},
        month = {December},
        }