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• W3144848557 abstract "Building performance evaluation is generally carried out through a non-automated process, where computational models are iteratively built and simulated, and their energy demand is calculated. This study presents a computational tool that automates the generation of optimal building designs in respect of their Life Cycle Carbon Footprint (LCCF) and Life Cycle Costs (LCC). This is achieved by an integration of three computational concepts: (a) A designated space-allocation generative-design application, (b) Using building geometry as a parameter in NSGA-II optimization and (c) Life Cycle performance (embodied carbon and operational carbon, through the use of thermal simulations for LCCF and LCC calculation). Examining the generation of a two-storey terrace house building, located in London, UK, the study shows that a set of building parameters combinations that resulted with a pareto front of near-optimal buildings, in terms of LCCF and LCC, could be identified by using the tool. The study shows that 80% of the optimal building’s LCCF are related to the building operational stage (σ = 2), while 77% of the building’s LCC is related to the initial capital investment (σ = 2). Analysis further suggests that space heating is the largest contributor to the building’s emissions, while it has a relatively low impact on costs. Examining the optimal building in terms compliance requirements (the building with the best operational performance), the study demonstrated how this building performs poorly in terms of Life Cycle performance. The paper further presents an analysis of various life-cycle aspects, for example, a year-by-year performance breakdown, and an investigation into operational and embodied carbon emissions." @default.
• W3144848557 created "2021-04-13" @default.
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• W3144848557 date "2021-03-28" @default.
• W3144848557 modified "2023-10-18" @default.
• W3144848557 title "A decision support tool for building design: An integrated generative design, optimisation and life cycle performance approach" @default.
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• W3144848557 doi "https://doi.org/10.1177/1478077121999802" @default.
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