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

TL;DR

This paper introduces a comprehensive, probabilistic, life-cycle optimization framework for civil infrastructure risk management, integrating hazard uncertainties, deterioration, and adaptive maintenance strategies using advanced computational methods.

Contribution

It extends existing seismic risk paradigms by combining hazard, deterioration, and fragility assessments within a Markov Decision Process, employing a novel tensor-based method for system-level optimization.

Findings

Reduced computational complexity from exponential to linear in system components.

Demonstrated application to seismic hazard risk mitigation.

Provided a practical, data-driven decision-making tool for infrastructure management.

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.