A decision support tool for building design: An integrated generative design, optimisation and life cycle performance approach

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.

A systematic review on artificial intelligence applications in architecture

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Implementation and Optimization of Reverse Suspension Structure Design Model Using Deep Learning

“…Schwartz et al (2021) believed that algorithms could provide structural data of different materials for reference and comparison. They used structural performance optimization and related knowledge of building materials to turn structural performance analysis tools into an integrated tool for structural morphology generation and optimization [ 19 ].…”

Framework of an algorithm-aided BIM approach for modular residential building information models

“…17 Further, in terms of decision support for building design, research has been conducted on generative design, optimization, and life cycle performance. 18 Research on adaptable modular building systems and multicriteria optimization strategies for mass customized housing presents an adaptable modular building system and a multicriteria optimization workflow. 19 To determine how an evaluation of sustainability and affordability needs to be carried out, we therefore adapt these concepts of modular housing and generative approaches for the early design stage.…”