Condition-based risk assessment and management for infrastructure retrofit in the face of uncertainty

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The societal living and economic growth rely heavily on critical infrastructural systems, with the assets value of about $1.1 trillion in Canada. However, according to the 2016 Canadian Infrastructure Report Card, 35% of those infrastructure assets are in fair, poor or very poor condition, due to aggressive environmental attacks (e.g., corrosion) and recurring low- or moderate-intensity hazard events (e.g., earthquakes). The degradation, in the form of progressive structural/material deteriorations and shock-based physical damage, reduces the performance of engineering structures. In recognition of devastating life safety and economic consequences potentially caused by their failure or loss of functionality in extreme hazard events, the condition-based risk assessment and management (e.g., through optimum retrofit) of existing infrastructure systems has become an emergent need in recent years.******The goal of the proposed research program is to develop a condition-based risk assessment and management (CRAM) framework for the “second-generation” retrofit strategy in the face of uncertainty. This framework integrates condition identification (i.e., estimating the current state probabilistically) of the hazard- or age-affected infrastructure systems, risk assessment with pertinent sources of uncertainty considered, and risk-informed optimum retrofit design for risk management. The condition identification and risk assessment will provide information on retrofit, and a unified risk-informed retrofit or repair design approach using optimization will be developed to aid in enhancing the structural performance.******This proposed research program will focus on existing infrastructure such as bridges and buildings subjected to seismic hazard. The four aims or themes are developing (A) detailed nonlinear finite element (FE) models of engineering structures with degrading properties; (B) nonlinear FE model updating techniques used for condition identification; (C) an uncertainty propagation tool for risk assessment accounting for pertinent uncertainties including model parameter uncertainty; and (D) a unified optimum retrofit approach used for risk-mitigation in the face of uncertainty. The successful outcome of this project will reach a milestone in the long-term research program on extending and applying the CRAM framework to a wide range of engineering risk problems, providing society with risk management solutions.