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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

Information

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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References

Alexandrov, NM, Lewis, RM, Gumbert, CR, Green, LL and Newman, PA (2001) Approximation and model management in aerodynamic optimization with variable-fidelity models. Journal of Aircraft 38, 1093–1101.CrossRefGoogle Scholar
ASHRAE (2019) ANSI/ASHRAE/IES 90.1-2019: Energy Standard for Buildings Except Low-Rise Residential Buildings. Atlanta, GA.Google Scholar
ASHRAE (2020) 90.1 Prototype Building Models-Medium Office. Building Energy Codes Program. Available at: https://www.energycodes.gov/901-prototype-building-models-medium-office.Google Scholar
Autodesk, Inc (2021 a) About Revit and IFC. About Revit and IFC | Revit 2021 | Autodesk Knowledge Network. Retrieved 2022, from https://tinyurl.com/56brnpujGoogle Scholar
Autodesk, Inc (2021 b) Customize the IFC setup. Customize the IFC Setup | Revit 2021 | Autodesk Knowledge Network. Retrieved 2022, from https://tinyurl.com/2ysuue79Google Scholar
Ayoub, M (2020) A review on machine learning algorithms to predict daylighting inside buildings. Solar Energy 202, 249–275.CrossRefGoogle Scholar
Bachman, LR (2004) Integrated Buildings: The Systems Basis of Architecture, Vol. 9. Hoboken, NJ: John Wiley & Sons.Google Scholar
Ballard, G (2008) The lean project delivery system: an update. Lean Construction Journal, 1–19.Google Scholar
Balling, RJ and Sobieszczanski-Sobieski, J (1996) Optimization of coupled systems – a critical overview of approaches. AIAA Journal 34, 6–17.CrossRefGoogle Scholar
Bock, BS (2019) IFC for relational databases - ifcSQL. buildingSMART Forums. Available at: https://forums.buildingsmart.org/t/ifc-for-relational-databases-ifcsql/1524Google Scholar
Brown, NC and Mueller, CT (2016) The effect of performance feedback and optimization on the conceptual design process. Proceedings of IASS Annual Symposia, Vol. 2016, No. 10. International Association for Shell and Spatial Structures (IASS), pp. 1–10.Google Scholar
buildingSMART (2018) Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries — Part 1: Data schema (ISO 16739-1:2018). Retrieved from https://www.iso.org/standard/70303.htmlGoogle Scholar
Chhabra, JP and Warn, GP (2018) Investigating the use of reinforcement learning for multi-fidelity model selection in the context of design decision making. In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection.CrossRefGoogle Scholar
Cho, CY, Won, J and Ham, S (2018) IFC model restructuring framework for efficient bulk-loading to object-relational IFC model server. KSCE Journal of Civil Engineering 22, 4930–4939.CrossRefGoogle Scholar
Clevenger, CM and Haymaker, JR (2009) Framework and metrics for assessing the guidance of design processes. In DS 58-1: Proceedings of ICED 09, the 17th International Conference on Engineering Design, Vol. 1, Design Processes, Palo Alto, CA, USA, 24–27 August 2009, pp. 411–422.Google Scholar
Fernández-Godino, MG, Park, C, Kim, NH and Haftka, RT (2016) Review of multi-fidelity models. arXiv preprint arXiv:1609.07196.Google Scholar
Gerbino, S, Cieri, L, Rainieri, C and Fabbrocino, G (2021) On BIM interoperability via the IFC standard: an assessment from the structural engineering and design viewpoint. Applied Sciences 11, 11430.CrossRefGoogle Scholar
Haymaker, J, Bernal, M, Marshall, MT, Okhoya, V, Szilasi, A, Rezaee, R, Chen, C, Salveson, A, Brechtel, J, Deckinga, L, Hasan, H, Ewing, P and Welle, B (2018) Design space construction: a framework to support collaborative, parametric decision making. Journal of Information Technology in Construction (ITcon) 23, 157–178.Google Scholar
Lee, G, Jeong, J, Won, J, Cho, C, You, SJ, Ham, S and Kang, H (2014) Query performance of the IFC model server using an object-relational database approach and a traditional relational database approach. Journal of Computing in Civil Engineering 28, 210–222.CrossRefGoogle Scholar
Li, H, Liu, H, Liu, Y and Wang, Y (2016) An object-relational IFC storage model based on oracle database. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 41.Google Scholar
Martins, JR and Lambe, AB (2013) Multidisciplinary design optimization: a survey of architectures. AIAA Journal 51, 2049–2075.CrossRefGoogle Scholar
Muthumanickam, NK (2021) Multidisciplinary Design Optimization Framework for Multi-Phase Building Design Process - Technology Demonstration Using Design of Office Building and Robotically 3D Printed Habitat (Doctoral dissertation, The Pennsylvania State University). PSU Electronic Thesis and Dissertation for Graduate School Repository. Available at: https://etda.libraries.psu.edu/catalog/24577nxm78Google Scholar
Muthumanickam, NK, Hasik, V, Unal, M, Miller, SW, Unwalla, T, Bilec, MM, Iulo, LD and Warn, GP (2018) Investigation of energy modeling methods of multiple fidelities: a case study. Proceedings of the 7th International Building Physics Conference. September 23–26, 2018. Syracuse, NY, USA: IBPC, p. 3. doi:10.14305/ibpc.2018.ms-1.03CrossRefGoogle Scholar
Muthumanickam, NK, Duarte, JP and Simpson, TW (2022 a) Machine learning based surrogate model for faster daylighting estimation in building design. Proceedings of the 6th Residential Building Design and Construction Conference, RBDCC 2022, May 11–12, 2022. University Park, PA. Available at: https://www.phrc.psu.edu/Conferences/Residential-Building-Design-and-Construction-Conference/6th-RBDCC.aspxGoogle Scholar
Muthumanickam, NK, Duarte, JP and Simpson, TW (2022 b) Multidisciplinary design optimization in architecture, engineering and construction: a detailed review and call for collaboration. Submitted to Journal of Structural and Multidisciplinary Optimization (under review).Google Scholar
Nour, M (2009) Performance of different (BIM/IFC) exchange formats within private collaborative workspace for collaborative work. Journal of Information Technology in Construction (ITcon) 14, 736–752.Google Scholar
Pena, WM and Parshall, SA (2012) Problem Seeking: An Architectural Programming Primer. Hoboken, NJ: John Wiley & Sons.Google Scholar
Reinhart, C (2015) Opinion: climate-based daylighting metrics in LEEDv4 – a fragile progress. Lighting Research and Technology 47, 388.CrossRefGoogle Scholar
Sen, P and Yang, JB (2012) Multiple Criteria Decision Support in Engineering Design. London: Springer Science & Business Media.Google Scholar
Shea, K, Aish, R and Gourtovaia, M (2005) Towards integrated performance-driven generative design tools. Automation in Construction 14, 253–264.CrossRefGoogle Scholar
Simpson, TW, Poplinski, JD, Koch, PN and Allen, JK (2001) Metamodels for computer-based engineering design: survey and recommendations. Engineering with Computers 17, 129–150.CrossRefGoogle Scholar
Solihin, W, Eastman, C, Lee, YC and Yang, DH (2017) A simplified relational database schema for transformation of BIM data into a query-efficient and spatially enabled database. Automation in Construction 84, 367–383.CrossRefGoogle Scholar
Tresidder, E, Zhang, Y and Forrester, AI (2012) Acceleration of building design optimisation through the use of Kriging surrogate models. Proceedings of Building Simulation and Optimization, pp. 1–8.Google Scholar
Turan, I, Chegut, A, Fink, D and Reinhart, C (2020) The value of daylight in office spaces. Building and Environment 168, 106503.CrossRefGoogle Scholar
Unal, M and Warn, GP (2017) A set-based approach to support decision-making on the restoration of infrastructure networks. Earthquake Spectra 33, 781–801.CrossRefGoogle Scholar
Unal, M, Miller, SW, Chhabra, JP, Warn, GP, Yukish, MA and Simpson, TW (2017) A sequential decision process for the system-level design of structural frames. Structural and Multidisciplinary Optimization 56, 991–1011.CrossRefGoogle Scholar
Wortmann, T (2018) Efficient, visual, and interactive architectural design optimization with model-based methods. doi:10.13140/RG.2.2.15380.55685.CrossRefGoogle Scholar
Wortmann, T, Costa, A, Nannicini, G and Schroepfer, T (2015) Advantages of surrogate models for architectural design optimization. AI EDAM 29, 471–481.Google Scholar
Wyszomirski, M and Gotlib, D (2020) A unified database solution to process BIM and GIS data. Applied Sciences 10, 8518.CrossRefGoogle Scholar
Yang, D, Sun, Y, Turrin, M, Buelow, PV and Paul, J (2015) Multi-objective and multidisciplinary design optimization of large sports building envelopes: a case study. Proceedings of IASS Annual Symposia, Vol. 2015, No. 20. International Association for Shell and Spatial Structures (IASS), pp. 1–14.Google Scholar
Zimina, D, Ballard, G and Pasquire, C (2012) Target value design: using collaboration and a lean approach to reduce construction cost. Construction Management and Economics 30, 383–398.CrossRefGoogle Scholar