Probabilistic Hazard Analysis Framework with Stochastic Optimal Control for Deteriorating Civil Infrastructure Systems

2026-04-24

AI Summary

This study addresses the degradation of safety and resilience in civil infrastructure systems under the coupled effects of multiple hazards and environmental deterioration. It proposes a lifecycle risk optimization framework that integrates hazard uncertainty, non-stationary deterioration, damage accumulation, and state-dependent vulnerability. By leveraging Markov decision processes with time-varying deterioration transition matrices and stochastic optimal control theory, the work introduces a tensor Kronecker decomposition–based approach to model transition dynamics. This method preserves the accuracy of global dynamic programming solutions while reducing computational complexity from exponential to linear scale at the system level. The resulting framework enables efficient, data-driven, adaptive maintenance strategies for multi-hazard scenarios—such as earthquakes—and achieves cost-effective risk mitigation over the infrastructure lifecycle.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The safety and resilience of civil infrastructure systems are increasingly threatened by compounded risks from various hazard events and structural deterioration due to environmental stressors. This study presents a comprehensive risk-informed, life-cycle optimization framework that extends the Performance-Based Earthquake Engineering (PBEE) and probabilistic seismic loss estimation paradigms by combining hazard uncertainties, nonstationary deterioration, structural damage accumulation, and state-dependent fragility assessments, with optimal, adaptive maintenance strategies in time. The life-cycle cost optimization is formulated in this work as a Markov Decision Process (MDP) problem, utilizing derived, transition matrices reflecting time-variant deterioration effects and hazard risks. To mitigate the curse of dimensionality in system-level optimization, a novel tensor-based method exploiting Kronecker-factored transition dynamics is introduced, reducing complexity from exponential to linear in the number of components while still preserving exact, global dynamic programming solutions. Overall, the framework is general and versatile, able to accommodate various hazard types. A seismic hazard application is, however, demonstrated and explained in detail in this work. The developed methodology eventually provides decision-makers with a practical, data-driven tool toward cost effective risk mitigation of civil infrastructure systems.

Problem

• probabilistic hazard analysis
• structural deterioration
• life-cycle optimization
• civil infrastructure systems
• risk mitigation

Innovation

• Stochastic Optimal Control
• Markov Decision Process
• Tensor-based Optimization
• Time-variant Deterioration
• Probabilistic Hazard Analysis