Urbanisation is surging, with over half of the human population predicted to live in an urban environment in the near future. This growth will place a strain on critical infrastructure distribution networks (water, energy, food, healthcare), which already operate in a state that is complex and intertwined within society. To transition towards a sustainable society, there needs to be a change in both societal behaviours (e.g. reducing water/energy/food waste activities) and the technology currently in place (e.g. greater use of green energy, digital twins, preventative maintenance solutions and precision technology).
This project will allow you to:
- Explore the challenge of understanding and modelling waste behaviours for a selected amenity (water, energy, food, healthcare) that will further the understanding of waste behaviours in society
- Model the reliability/availability/resilience of critical infrastructures
You will also have the opportunity to use (or propose the use of) advanced technologies (e.g. AI/ML/DL, Digital Twins) as a solution to reducing waste behaviours.
- Review current processes and state-of-the-art for waste behaviour modelling, and what current approaches are used to adapt to waste reduction.
- Investigate which sectors (e.g. water, energy, food) are most impacted by waste behaviour and how this affects production/service provision.
- Identify solutions/applications that are suitable for supporting critical infrastructure technology.
- Shooshtarian, S.; Caldera, S.; Maqsood, T.; Ryley, T. Using Recycled Construction and Demolition Waste Products: A Review of Stakeholders’ Perceptions, Decisions, and Motivations. Recycling 2020, 5, 31. https://doi.org/10.3390/recycling5040031
- Barreiro, J.; Lopes, R.; Ferreira, F.; Brito, R.; Telhado, M.J.; Matos, J.S.; Matos, R.S. Assessing Urban Resilience in Complex and Dynamic Systems: The RESCCUE Project Approach in Lisbon Research Site. Sustainability 2020, 12, 8931. https://doi.org/10.3390/su12218931
Theme(s): Water, Energy and Food Network, Waste Behaviours, Artificial intelligence and machine learning, Digital Twins
Will Hurst (email@example.com)