Resilient and Sustainable Infrastructure Systems Design and Analysis

弹性和可持续基础设施系统设计与分析

• 批准号: 2295251
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2295251
• 关键词: Resilient; Sustainable; Infrastructure; Systems; Design

The impact of climate change has become more frequent, visible and extreme in recent years with increasingly adverse and unpredictable future effects predicted by the scientific community. As a result, there are increased challenges and complexities associated with the engineering design of infrastructure systems related to economic, environmental and social considerations. These complexities naturally arise from the changing operational parameters required by these systems throughout their lifetime which impact performance across several important metrics. Given the associated risk and lack of future trend data, there is a need to revise the design approach for complex infrastructure systems. Furthermore, the built environment has already been identified as the industrial sector which puts the most pressure on our global sustainability resulting from the associated energy, material and resource consumption of the construction and operation of these structures. Designing for flexibility from the early stages of the design process can help significantly increase the sustainability, resilience, and potential for adaptability of these systems with important positive externalities related to the United Nations Sustainable Development Goals (UN SDGs). Traditional modelling and design approaches, however, tend to require more data than is available for accuracy and present major drawbacks related to computational intensity and complexity of the modelling process. Consequently, they tend to target only one objective and are not feasible to use in the early stages of the design decision making process. Given the importance of multidisciplinary interactions in ultimately determining lifetime performance, there is a demand for a more broadly applicable tool. To ensure that investments lead to the deployment of resilient and sustainable systems, a successful flexibility analysis must also provide credibility for decision makers through quantitative measures while accounting for qualitative factors. It is therefore essential to develop a new data driven methodology which can be applicable across infrastructure systems for a decision rule approach to real option analysis and flexibility design. The long-term sustainability and effectiveness of such a system in increasing our ability for climate change adaptation globally is closely linked to integration with the UN SDGs rather than simple traditional economic value optimization methods. Machine learning (ML) thus offers a great potential solution to these issues as it can allow fast prediction and parameter variation for more efficient exploration of alternative designs and ultimately optimized lifetime performance across a wide range of scenarios. Using ML can help to mitigate risks and uncertainty in the design process at lower computational costs than traditional approaches, yielding enhanced and adaptable infrastructure systems which are more resilient and economically viable in the face of climate change.Project ObjectivesThe central question this project asks is: How can ML be applied to the design and analysis of infrastructure systems by leveraging flexibility to enable enhanced resilience, sustainability and economic performance? Project specific research questions may be:1)How does the role of stakeholders at different levels of the decision-making change over time and with uncertainty? When is it most optimal to include different flexibility considerations in the design process?2)How can engineers identify alternative designs or configurations which are better suited for flexibility with simplicity and early on? What sort of input would be necessary to achieve such a task under uncertainty?3)How can this data driven methodology be applied to enable enhanced energy access and policy in developing countries through optimized distributed generation capacity and placement in the absence of local structures?

近年来，气候变化的影响变得更加频繁、明显和极端，科学界预测未来的影响越来越不利和不可预测。因此，与经济、环境和社会考虑相关的基础设施系统工程设计面临着越来越多的挑战和复杂性。这些复杂性自然源于这些系统在其整个生命周期中所需的不断变化的操作参数，这些参数会影响几个重要指标的性能。鉴于相关风险和缺乏未来趋势数据，有必要修订复杂基础设施系统的设计方法。此外，建筑环境已经被确定为对我们的全球可持续性造成最大压力的工业部门，这是由于这些结构的建造和运营所消耗的相关能源，材料和资源。从设计过程的早期阶段就进行灵活性设计，有助于显著提高这些系统的可持续性、弹性和适应性潜力，并具有与联合国可持续发展目标（UN SDG）相关的重要正外部性。然而，传统的建模和设计方法往往需要更多的数据，而不是可用的准确性，并提出了与建模过程的计算强度和复杂性有关的主要缺点。因此，它们往往只针对一个目标，在设计决策过程的早期阶段使用是不可行的。考虑到多学科相互作用在最终确定寿命性能方面的重要性，需要一种更广泛适用的工具。为了确保投资导致部署弹性和可持续的系统，成功的灵活性分析还必须通过定量措施为决策者提供可信度，同时考虑定性因素。因此，有必要开发一种新的数据驱动的方法，可以适用于整个基础设施系统的决策规则方法的真实的选项分析和灵活性设计。这种系统在提高全球适应气候变化能力方面的长期可持续性和有效性与联合国可持续发展目标的整合密切相关，而不是简单的传统经济价值优化方法。因此，机器学习（ML）为这些问题提供了一个非常有潜力的解决方案，因为它可以允许快速预测和参数变化，以便更有效地探索替代设计，并最终在各种场景中优化寿命性能。使用机器学习可以帮助减轻设计过程中的风险和不确定性，计算成本低于传统方法，从而产生增强的和适应性强的基础设施系统，在面对气候变化时更具弹性和经济可行性。项目目标本项目提出的核心问题是：ML如何应用于基础设施系统的设计和分析，利用灵活性来增强弹性，可持续性和经济效益？项目具体的研究问题可能是：1）利益相关者在决策的不同层次上的作用如何随着时间和不确定性而变化？什么时候在设计过程中考虑不同的灵活性是最佳的？2)工程师如何在早期识别更适合灵活性和简单性的替代设计或配置？在不确定的情况下完成这样一项任务需要什么样的投入？3)如何应用这种数据驱动的方法，在没有地方结构的情况下，通过优化分布式发电能力和布局，加强发展中国家的能源供应和政策？