Reference number : GC-2017-Q1-02728, amendment number : , agreement type : G, recipient legal name : Li, Yong (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : 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 &ldquo;second-generation&rdquo; 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., program purpose french : 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 &ldquo;second-generation&rdquo; 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., agreement title english : Condition-based risk assessment and management for infrastructure retrofit in the face of uncertainty, agreement title french : Condition-based risk assessment and management for infrastructure retrofit in the face of uncertainty, agreement number : RGPIN, agreement value : 110000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada