Resilient and Sustainable Infrastructure Systems Design and Analysis

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 Objectives
The 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?