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Topic

Advancing risk assessment of climate change for the resiliency of the built environment: multi-physical risk analysis

Department of Civil Engineering

Date & location

• Monday, September 29, 2025

• 9:00 A.M.

• Virtual Defence

Reviewers

Supervisory Committee

• Dr. Thomas Froese, Department of Civil Engineering, University of Victoria (Supervisor)

• Dr.  Phalguni Mukopadhyaya, Department of Civil Engineering, UVic (Member)

• Dr. Adel Guitouni, School of Business, UVic (Outside Member) 

External Examiner

• Dr. Daniel Hoornweg, Department of Energy and Nu clear Engineering, Ontario Tech University 

Chair of Oral Examination

• Dr. Adam Murray, Department of Computer Science, UVic 

Abstract

The built-environment sectors in Canada are highly vulnerable to a wide range of climate-related risks through varying extent of stresses and shocks linked to extreme weather events and other climate-related changes. The impacts on assets are significant, with inflation-adjusted insured losses from environmental perils showing a rising trend, totaling $30 billion (2023 C$) over the past decade, not including socioeconomic impact of the resulting functional disruptions. These impacts underscore the key role of adaptation measures to reduce the costs of climate risks by enhancing the resilience of built assets under a variety of climate change scenarios. However, accelerating the adaptation of built assets to the mounting effects of climate change is complex and presents significant challenges for decision-makers. It requires extensive local data, possess uncertainty, and often relies on expensive, one-off contracts, if at all. This has impacted our ability to foresee future-imposed climate risks in built-environment.

This PhD thesis aims to enhance advances in risk analysis by proposing new methods in quantifying the vulnerability and reliability of building systems under stresses and shocks at asset level of resolution, as well as forecasting the potential changes in energy demand imposed by climate change on urban areas. The thesis presents four risk modeling to assess the climate physical risks on the Canadian built assets and to provide reliable risk forecasting to improve the decision-making in asset operations.

The first study presents a runtime-based degradation model using stochastic processes with random effects to assess climate change risks on HVAC systems. The proposed method captures the correlation between climate parameters and degradation rate of the units by leveraging runtime data and future climate projections. It quantified non-stationary changes in degradation rates over asset lifecycles and the functional degradation of filtration effectiveness in varying climates. The second study presents a meta-modeling using the Response Surface Methodology (RSM) to assess moisture-related degradation risk of building envelopes in different ASHRAE climate zones. From the method, the resulted analytical functions can be used to compare the moisture performance of different enclosure solutions for different climate zones. The third study presents a degradation model using dynamic Bayesian approach that integrates condition-based degraded failure and faulty failure of building components under climate stress and shock. The method extended the reliability analysis with economic-based assessment to evaluate the value-at-risk and maintenance strategies of the asset. The last study presents a Monte Carlo regression approach to explore the climate change impact on energy demand changes in Victoria, Canada. This study adopted the established response function from literature and applied it to future climate projections in the city of Victoria to estimate the log electricity demand. The results can be used, at building level, for adaptation strategies and resource allocation, such as retrofitting action plan and building energy management.

The thesis findings led to the development of the Resiliency Opportunity Assessment and Response (ROAR) tool, funded by the Greater Victoria 2030 District Program. This high-level IT solution enables users to quickly assess climate-related risks and identify opportunities to improve resilience and to know where to prioritize responses by identifying low-cost, low-carbon options, as well as opportunities that warrant further investigation through detailed audits or studies. The tool features three modules: “stress” risks, “shock” risks, and “energy” risks, and contributes to the Canadian Sustainability Disclosure Standards (CSDS), as an enabler for ESG requirement to disclose climate risks as part of securities regulations. The research show cases new insights, methods, and tools for minimizing climate risk in built-environment decision-making.