Towards Robust Long-Term Infrastructure Asset Management under Deep Uncertainty

在高度不确定性下实现稳健的长期基础设施资产管理

• 批准号: RGPIN-2022-04591
• 负责人: Yuan, Xianxun(Arnold)
• 金额: $ 2.62万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749450
• 关键词: Towards; Robust; Long; Term; Infrastructure

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.

气候变化的威胁是真实的，但目前的基础设施适应计划令人不安。尽管加拿大的几个司法管辖区已经引入了基础设施资产管理（IAM）法规，但目前的IAM实践受到许多因素的阻碍，包括缺乏一致的长期规划框架和在深度不确定性下的有效优化工具。与此同时，人工智能的进步带来了巨大的机遇和潜力，可能会改变基础设施资产管理的未来实践。 长期目标是为可持续和弹性基础设施系统开发综合资产管理和气候适应规划方法，未来五年的拟议研究计划希望解决与开发这种综合规划方法相关的三个基本问题。研究的三个议题包括：1）气候变化威胁下老化桥梁和道路网络的风险稳健性评估; 2）决策者对气候适应长期投资的风险认知和时间偏好; 3）深度不确定性下稳健性导向的最优资产管理和气候适应规划。虽然第二个问题是一个重要的经验问题，会影响到适当的投资水平和社区参与的综合资产管理，其他两个问题是方法的发展。 对于稳健性评估，将使用申请人的研究团队最近开发的基于风险的新颖稳健性指数。进一步的增强包括桥梁和道路网络的经验性损坏-损失关系;使用深度学习的有效替代建模方法;以及用于识别关键组件的基于鲁棒性的技术。 将采用面对面调查方法，探讨资产管理决策者对气候适应长期投资的风险认知和时间偏好。调查将对公司资产经理和市议员通常参加的加拿大主要资产管理会议进行。基于累积前瞻理论的风险态度模型和不同的贴现模型将被用来拟合实证数据。最佳拟合模型将用于最后一步。 对于以稳健性为导向的优化，将开发深度强化学习方法，为气候适应活动和常规维护和康复治疗创建动态、灵活、长期的投资计划。 为了实现这些目标，研究计划被分解为六个相互关联的项目。该计划将为专业资产管理公司提供更好的工具，用于资产管理和气候适应的综合长期规划。该提案还包括一个现实的HQP培训计划，该计划在公平，多样性和包容性的原则下交织在一起。