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Infrastructure Resilience Conference 2018

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Spatial optimisation to enhance resilience against multiple objectives

Future development faces a number of resilience challenges. Over longer timeframes infrastructure needs to adapt to a climate change risks, mitigate to reduce greenhouse gas emissions, plan for changing demographics, and tackle other sustainable development objectives. Over short timeframes, emergency response resources need to balance their location relative to vulnerable locations and infrastructure versus housing costs, transportation times and susceptibility of transport routes to disruption themselves (e.g. bridge failure or flooding). Planners therefore face a challenge of multidimensional, spatial optimization in order to balance potential tradeoffs and maximize synergies between these engineering challenges. To address this, we have developed a spatial optimization framework which uses a spatially implemented genetic algorithm to generate a set of Pareto-optimal results that provide engineers and planners with the best set of trade-off spatial plans. Application in Greater London (U.K.) optimises six objectives: (i) minimize heat risks, (ii) minimize flooding risks, (iii) minimize transport travel costs to minimize associated emissions, (iv) maximize brownfield development, (v) minimize urban sprawl, and (vi) prevent development of greenspace. Results show that spatial development strategies can be identified that are optimal for specific objectives and differ significantly from the existing development strategies. The analysis reveals interesting tradeoffs: for example, increases in heat or flood risk can be avoided, but there are no strategies that do not increase at least one of these. Furthermore, tradeoffs between risk and sustainability objectives can be more severe: for example, minimizing heat risk is only possible if future development is allowed to sprawl significantly. Spatial structure of infrastructure and built environment is crucial in mediating resilience and sustainability objectives. However, not all planning objectives are suited to quantified optimization and so the results should form part of an evidence base to improve the delivery of risk and sustainability management in future urban development.

Richard Dawson
Newcastle University
United Kingdom

Stuart Barr
Newcastle University
United Kingdom

Daniel Caparros-Midwood
Amec Foster Wheeler Environment & Infrastructure
United Kingdom

Fulvio Lopane
Newcastle University
United Kingdom

 

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