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Multidisciplinary concurrent optimization framework for multi-phase building design process

Published online by Cambridge University Press:  13 January 2023

Naveen Kumar Muthumanickam Open the ORCID record for Naveen Kumar Muthumanickam [Opens in a new window] ,
Jose Pinto Duarte Open the ORCID record for Jose Pinto Duarte [Opens in a new window]  and
Timothy W. Simpson Open the ORCID record for Timothy W. Simpson [Opens in a new window]
Naveen Kumar Muthumanickam
Affiliation:
National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA
Jose Pinto Duarte*
Affiliation:
Stuckeman School of Architecture and Landscape Architecture, The Pennsylvania State University, 150 Stuckeman Family Building, University Park, PA 16803, USA
Timothy W. Simpson
Affiliation:
Paul Morrow Professor of Engineering Design and Manufacturing, The Pennsylvania State University, 205 Leonhard Building, University Park, PA 16803, USA
*
Author for correspondence: Jose Pinto Duarte, E-mail: jxp400@psu.edu
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Abstract

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. This paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

Keywords

Building design optimizationbuilding information modeling (BIM)concurrent engineeringdaylightingenergy usegenerative designmetamodelsmulti-fidelity
Type
Research Article
Information
AI EDAM , Volume 37 , 2023 , e3
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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