Abstract
This paper critically reviews the role of performance-based generative design in fast prototyping of buildings, describes the methodology of an automated generative layout design to produce complete building solutions, and presents a case study of multi-story buildings in urban context.The proposed approach evolves the building design solutions by interacting with the city 3D geometry and evaluates the energy consumption for air-conditioning. The building designs take into consideration urban geometric constraints and objectives, such as alignment with surrounding buildings, urban lot area, and relative and absolute position of the generated elements. During the evaluation process, the urban context is considered for casting shadows and reflecting solar radiation.The case study consists of six alternative 15-story buildings located in the city of São Paulo (Brazil), having commercial areas on the ground floor and two apartments per story on the remaining floors. The results show that, despite having similar apartments in every story, the urban context has a relevant impact on the buildings' energy performance. The difference between the apartments' best and worst energy performing stories ranges from 9% to 12% (ignoring the outlier story located in the first level), depending on the building solution. The results also show that the most energy efficient apartments’ story is not located in the top or bottom floors, but rather at an intermediate level.
Highlights
The sustainability of the built-environment is a complex subject in which interrelated systems have strong impact on the social, economic, and environment dimensions of the cities [1]
The Evolutionary Program for the Space Allocation Program (EPSAP) algorithm ran a single time, which took a runtime of 1 h and 32 min, using 20 threads in parallel computing in a ten-core 3.31 GHz CPU machine with a 32 GB RAM, to produce six alternative building designs
The new random individuals are inserted at each Evolution Strategy (ES) generation are noticeable by the peaks in the top graph (cost function f, Eq (A.8)), each followed by the corresponding Stochastic Hill Climbing (SHC) search stage, where the worst individual fitness, the population average fitness, and the display group average fitness indicators are represented
Summary
The sustainability of the built-environment is a complex subject in which interrelated systems have strong impact on the social, economic, and environment dimensions of the cities [1]. As the complexity of building design demands knowledge in different fields and large number of alternatives to evaluate, performance-based design methods present themselves as promising tools to assist the building practitioners in the decision-making. These consist of finding novel solutions using building performance as guiding factor [10], where performance is assessed by simulation of a digital model set under predefined conditions [11]. Besides allowing integration of synthesis and analytic phases of design, these automated and fast-prototyping design methods help to overcome designers’ “limitations of knowledge or fixation” and automate tedium design tasks, “leaving more time for creative activities, and help reduce errors” [14]
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