Development of a decision support system for sustainable and resilience evaluation of urban underground space physical infrastructureud

摘要

The research described in this thesis proposes a new,novel evaluation ‘framework’ and accompanying operational ‘model’, the ‘SUURE’ (Sustainable Underground Use Resilience Evaluation) ‘framework’, which adopts a socio-ecological systems (SES) approach to evaluation, combining sustainability science and resilience theory, at the same time incorporating a range of interdisciplinary tools and methods to achieve this. It purposes at its core to aid in sustainability evaluation of urban underground space, by evaluating the process to the outcome of sustainable solutions i.e. the capacity to adapt to change in different steady states. In consequence, a fundamentally different approach to planning that utilises future socio-ecological scenarios (NSP, PR, MF and FW) is incorporated in the ‘framework’ as a means of evaluating through its operational ‘model’, the sensitivity of investment decisions made today in the name of sustainability i.e. will the proposed engineering sustainability solution continue to deliver its intended function into the future, whatever that may be. The SUURE operational ‘model’ was employed as a proof of concept to the case study area of Birmingham Eastside, evaluating the potential use of multi-utility tunnels (MUT) in Eastside as a means of engendering sustainable and resilient use of urban underground space, through sustainable utility placement, both now and into the future. The flush-fitting MUT was found to be having the highest overall baseline (present-day) performance resilience index ratio at mean of 0.739, the shallow MUT second at 0.656, and the deep MUT last at 0.212. With regard to the evaluation of continued MUT functional performance into the future (whatever that may be), all three MUT options, if implemented today in the name of sustainability, would continue to deliver and retain their core functional performance, the deep MUT showing the most significant increase across all evaluation clusters (social, economic, physical, bio-physical and location aspects).