Probabilistic Models for Infrastructure Risk Assessment and Management in the Changing Climate

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Infrastructure is central to the daily life of every Canadian - from buildings, bridges, and water supply to transportation networks and energy systems, all are vital to the economic growth, health and safety, and quality of life of our communities. Concerns over the aging of existing infrastructure, largely built in the mid-20th century, have prompted research into structural reliability and risk assessment methods for guiding infrastructure renewal strategies. Otherwise, in the absence of timely mitigation of deterioration, the risk of failures is bound to increase, as evidenced by the collapse of a multistorey building in Miami, Florida (June 2021), the collapse of the Morandi bridge in Genoa, Italy (August 2018), and the failure of a concrete slab in a traffic tunnel in Montreal (August 2011). The increasing pace of climate change aggravates the intensity and frequency of extreme weather events, threatening the existing infrastructure, impacting its effectiveness, reducing lifespan, and adding to the cost of repair and renewal. For example, a crippling ice storm in Texas (February 2021), a record-setting heatwave that pushed temperatures to dangerous levels of nearly 50 C in Lytton, BC (June 2021), and unprecedented rainfall and floods in Western Europe (15 July 2021) and those caused by hurricane Ida in the United States (31 August 2021) killed many people and caused huge property damage. Thus, the increased severity of weather extremes exacerbates the risk of failure of aging infrastructure. To respond to this cumulative threat, the next phase of research is proposed to improve safety and resilience through design and life cycle management of Canadian infrastructure assets founded on a robust assessment of risks posed by the changing climate. The scientific core of this proposal is based on the probability theory and stochastic processes that can model uncertain and non-stationary loads resulting from climate change and various forms of aging-related degradation of structures. These models will predict future reliability and risk and evaluate the effects of maintenance and rehabilitation on safety and resilience through optimized investments over the life cycle of a system. As the "non-stationary" nature of the changing climate is incompatible with the assumption of stationarity used by existing structural design codes, the proposed research will generate the knowledge base to revise many provisions related to extreme load distributions, reliability metrics, design factors, and load combination rules, and lead the development of the next generation of codes. In summary, the next five-year phase of this research program will continue to build on the success of the previous research work and discover new methods and tools for risk analysis and management of infrastructure. The training of seven graduate students in the advanced areas of probabilistic risk and reliability will be a significant outcome of the proposed program.