CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

id acadia13_079
authors Jason Gerber, David; Eve Lin, Shih-Hsin; Amber Ma, Xinyue
year 2013
title Designing-In Performance: A Case Study of Applying Evolutionary Energy-Performance Feedback for Design (EEPFD)
source ACADIA 13: Adaptive Architecture [Proceedings of the 33rd Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-1-926724-22-5] Cambridge 24-26 October, 2013), pp. 79-86
summary This paper explores the application of a novel Multi-disciplinary Design Optimization (MDO) framework to the early stage design process, through a case study where the designer serves as the primary user and driver. MDO methods have drawn attention from the building design industry as a potential means of overcoming obstacles between design and building performance feedback to support design decision-making. However, precedents exploring MDOs in application to the building design have previously been limited to driving use by engineers or research teams,thereby leaving the incorporation of MDO into a design process by designers largely unexplored. In order to investigate whether MDO can enable the ability to design in a performance environment during the conceptual design stage, a MDO design framework entitled Evolutionary Energy-Performance Feedback for Design (EEPFD) was developed. This paper explores the designer as the primary user by conducting a case study where the application of EEPFD to a single family residential housing unit is incorporated. Through this case study EEPFD demonstrates an ability to assist the designer in identifying higher performing design options while meeting the designer’s aesthetic preferences. In addition the benefits, limitations, concerns and lessons learned in the application of EEPFD are also discussed.
keywords conceptual energy-performance feedback; design decision support; parametric design; multi-disciplinary design optimization; genetic algorithm
series ACADIA
type Normal Paper
email dgerber@usc.edu
full text file.pdf (1,182,243 bytes)
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