Towards Robust Long-Term Infrastructure Asset Management under Deep Uncertainty

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The threats of climate change are real, but the current infrastructure adaptation plans are unsettling. Although regulations on infrastructure asset management (IAM) have been introduced to several jurisdictions in Canada, current IAM practice has been impeded by a number of factors including the lack of a consistent long-term planning framework and effective optimization tools under deep uncertainty. Meanwhile, advancements in artificial intelligence are presenting huge opportunities and potentials that may change the future practice of infrastructure asset management. With a long-term objective aiming to develop an integrated asset management and climate adaptation planning methodology for sustainable and resilient infrastructure systems, the proposed research program for the next five years wants to address three fundamental issues related to the development of such an integrated planning methodology. The three issues to be addressed include: 1) risk-based robustness assessment of ageing bridges and road networks under climate change threats; 2) decision makers' risk perception and time preference of long-term investment on climate adaptations; and 3) Robustness-oriented optimal asset management and climate adaptation planning under deep uncertainty. While the second issue is an important empirical question that would affect the proper level of investment and community engagement in IAM, the other two issues are of method development. For the robustness assessment, a novel risk-based robustness index that was recently developed by the applicant's research team will be used. Further enhancements include empirical damage-loss relationships for bridges and road networks; efficient surrogate modelling method using deep learning; and robustness-based technique for identifying critical components. An in-person survey method will be used to probe asset management decision makers' risk perception and time preference of long-term investment on climate adaptations. Surveys will be conducted on major asset management conferences in Canada that corporate asset managers and municipal councillors usually attend. Cumulative prospective theory-based risk attitude model and different discounting models will be used to fit the empirical data. The best fitted models will be used for the last step. For the robustness-oriented optimization, deep reinforcement learning methods will be developed to create a dynamic, flexible, long-term investment plan for both climate adaptation activities and conventional maintenance and rehabilitation treatments. To achieve these objectives, the research program is broken down to six interrelated projects. The proposed program will provide professional asset managers with a better tool for integrated long-term planning of asset management and climate adaptation. The proposal also includes a realistic HQP training program that is interwoven under the principle of equity, diversity and inclusion.