Early-stage design support combining machine learning and building information modelling

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Manav Mahan Singh
  • Chirag Deb
  • Philipp Geyer

Externe Organisationen

  • KU Leuven
  • Indian Institute of Technology Bombay (IITB)
  • ETH Zürich
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer104147
FachzeitschriftAutomation in construction
Jahrgang136
Frühes Online-Datum1 Feb. 2022
PublikationsstatusVeröffentlicht - Apr. 2022

Abstract

Global energy concerns necessitate designing energy-efficient buildings. Many important decisions affecting energy performance are made at early stages with little information. Dynamic simulations support informed decision-making; however, uncertainty, high computational time, and expensive modelling efforts impair their use at early stages. This article develops an approach using building information modelling and machine learning that provides quick energy performance information. This approach has been implemented into a web tool, p-energyanalysis.de. It allows design space exploration, assesses the energy performance of design options, compares multiple options, performs sensitivity analysis, and tracks changes. Twenty-one participants (researchers and architects) used it as a support tool for designing an energy-efficient building. Their feedbacks are discussed as part of the tool development. The study found that the tool supports early-stage design decisions by quickly providing relevant information. The limitations, such as the bias in the results towards training data population and implementation issues, are also discussed.

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Early-stage design support combining machine learning and building information modelling. / Singh, Manav Mahan; Deb, Chirag; Geyer, Philipp.
in: Automation in construction, Jahrgang 136, 104147, 04.2022.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Singh MM, Deb C, Geyer P. Early-stage design support combining machine learning and building information modelling. Automation in construction. 2022 Apr;136:104147. Epub 2022 Feb 1. doi: 10.1016/j.autcon.2022.104147
Singh, Manav Mahan ; Deb, Chirag ; Geyer, Philipp. / Early-stage design support combining machine learning and building information modelling. in: Automation in construction. 2022 ; Jahrgang 136.
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abstract = "Global energy concerns necessitate designing energy-efficient buildings. Many important decisions affecting energy performance are made at early stages with little information. Dynamic simulations support informed decision-making; however, uncertainty, high computational time, and expensive modelling efforts impair their use at early stages. This article develops an approach using building information modelling and machine learning that provides quick energy performance information. This approach has been implemented into a web tool, p-energyanalysis.de. It allows design space exploration, assesses the energy performance of design options, compares multiple options, performs sensitivity analysis, and tracks changes. Twenty-one participants (researchers and architects) used it as a support tool for designing an energy-efficient building. Their feedbacks are discussed as part of the tool development. The study found that the tool supports early-stage design decisions by quickly providing relevant information. The limitations, such as the bias in the results towards training data population and implementation issues, are also discussed.",
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