Boonstra, S.

Emmerich, M.T.M.

Hofmeyer, H.

van der Blom, K.

English

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Enabling large design search spaces in building optimisation

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 Enabling large design search spaces in building optimisationCitation for published version (APA):Boonstra, S., van der Blom, K., Hofmeyer, H., & Emmerich, M. T. M. (2017). Enabling large design searchspaces in building optimisation. Poster session presented at 20th Engineering Mechanics Symposium , Arnhem,Netherlands.Document status and date:Published: 01/10/2017Document Version:Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. Peopleinterested in the research are advised to contact the author for the final version of the publication, or visit theDOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and pagenumbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:www.tue.nl/taverneTake down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nlproviding details and we will investigate your claim.Download date: 04. Oct. 2023Enabling Large Design Search Spaces in Building OptimisationS. Boonstra1,3, K. van der Blom2, H. Hofmeyer1,3, M.T.M. Emmerich21Eindhoven University of Technology2Leiden University3Graduate School Engineering MechanicsPhD1 2 3 4IntroductionResource depletion and pollution demand us humans tochange. This is widely acknowledged but not widely sup-ported in daily practice. The built environment is responsi-ble for 40% of resource depletion, however it has only re-cently begun to implement climate minded changes. Thepresented research aims to define optimisation methodolo-gies that will help engineers to find optimal building de-signs to ultimately contribute to a healthy planet.Design Search SpaceIn building design optimisation, the complete search space isstill too large and too complex for modern optimisation tech-niques. Engineers therefore limit search spaces to manage-able sizes, leading to a possible loss of optimal designs. Thepresented research is an endeavour to enlarge the searchspaces that engineers traditionally search. This, by findingnew design search spaces in an iterative and hybrid processbased on previously found optima, design rules, and/or engi-neering input.14214414614815015215415620 40 60 80 100 120heating energy [kWh]compliance [Nm]DominatedSolutionsPareto frontapproximationFigure 1: Results of a super-structured search using the SC.Design search spaces are considered super-structuredwhen they have fixed variables that are often constrained,and super-structure free when the type or existence of vari-ables and their constraints can change. Two building spatialdesign representations that are limited to a collection of or-thogonal spaces are introduced. The supercube (SC) is acollection of cells that are individually controlled to collec-tively represent one or more spaces in a building and it isthus super-structured. The movable and sizeable represen-tation (MS) is a collection of spaces that are freely locatedand sized and it is thus super-structure free.Optimisation algorithms have been implemented and definedfor both a super-structured and a super-structure free de-sign search space. Figure 1 shows results from the super-structured search with the SMS-EMOA algorithm when op-timising for strain energy (compliance) and heating energy.Figure 2 shows the transformations made to a buildingdesign during the super-structure free simulations of co-evolutionary design (CD) processes, in which it is also triedto optimise for compliance and heating energy.010000200003000040000500000 50 100 150 200 250 300 350 400 450 500heating energy [kWh]compliance [Nm]14015016050 150 250Figure 2: Results of a super-structure free search using theMS representation.Combination of Design Search SpacesThe results in the previous section show the strong points ofeach approach, SMS-EMOA can effectively consider manysolutions albeit though at the cost of time. CD on the otherhand can move to different solutions fast, however it is not ef-fective. As mentioned, both optimisation approaches will behybridised with the intent to employ the best of both. An op-timisation result from one optimisation will as such be usedto define the design search space of the other, this will con-tinue in a consecutive manner until a stopping criterion hasbeen reached. Recently this method has been successfullyimplemented, a small comparison is given in figure 3.14214615015415816210 35 60heating energy [KWh]compliance [Nm]Super-structuredoptimisationCombinedmethodCD CD CDCD CDSMS-EMOASMS-EMOASMS-EMOASMS-EMOASMS-EMOASMS-EMOAextended searchFigure 3: The graph shows the improvement of the combinedmethod over a pure super-structured optimisation. The visu-alised solutions are the knee points in the pareto front ap-proximation after each algorithm.

 Enabling large design search spaces in building optimisationCitation for published version (APA):Boonstra, S., van der Blom, K., Hofmeyer, H., & Emmerich, M. T. M. (2017). Enabling large design searchspaces in building optimisation. Poster session presented at 20th Engineering Mechanics Symposium , Arnhem,Netherlands.Document status and date:Published: 01/10/2017Document Version:Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. Peopleinterested in the research are advised to contact the author for the final version of the publication, or visit theDOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and pagenumbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:www.tue.nl/taverneTake down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nlproviding details and we will investigate your claim.Download date: 05. Oct. 2023Enabling Large Design Search Spaces in Building OptimisationS. Boonstra1,3, K. van der Blom2, H. Hofmeyer1,3, M.T.M. Emmerich21Eindhoven University of Technology2Leiden University3Graduate School Engineering MechanicsPhD1 2 3 4IntroductionResource depletion and pollution demand us humans tochange. This is widely acknowledged but not widely sup-ported in daily practice. The built environment is responsi-ble for 40% of resource depletion, however it has only re-cently begun to implement climate minded changes. Thepresented research aims to define optimisation methodolo-gies that will help engineers to find optimal building de-signs to ultimately contribute to a healthy planet.Design Search SpaceIn building design optimisation, the complete search space isstill too large and too complex for modern optimisation tech-niques. Engineers therefore limit search spaces to manage-able sizes, leading to a possible loss of optimal designs. Thepresented research is an endeavour to enlarge the searchspaces that engineers traditionally search. This, by findingnew design search spaces in an iterative and hybrid processbased on previously found optima, design rules, and/or engi-neering input.14214414614815015215415620 40 60 80 100 120heating energy [kWh]compliance [Nm]DominatedSolutionsPareto frontapproximationFigure 1: Results of a super-structured search using the SC.Design search spaces are considered super-structuredwhen they have fixed variables that are often constrained,and super-structure free when the type or existence of vari-ables and their constraints can change. Two building spatialdesign representations that are limited to a collection of or-thogonal spaces are introduced. The supercube (SC) is acollection of cells that are individually controlled to collec-tively represent one or more spaces in a building and it isthus super-structured. The movable and sizeable represen-tation (MS) is a collection of spaces that are freely locatedand sized and it is thus super-structure free.Optimisation algorithms have been implemented and definedfor both a super-structured and a super-structure free de-sign search space. Figure 1 shows results from the super-structured search with the SMS-EMOA algorithm when op-timising for strain energy (compliance) and heating energy.Figure 2 shows the transformations made to a buildingdesign during the super-structure free simulations of co-evolutionary design (CD) processes, in which it is also triedto optimise for compliance and heating energy.010000200003000040000500000 50 100 150 200 250 300 350 400 450 500heating energy [kWh]compliance [Nm]14015016050 150 250Figure 2: Results of a super-structure free search using theMS representation.Combination of Design Search SpacesThe results in the previous section show the strong points ofeach approach, SMS-EMOA can effectively consider manysolutions albeit though at the cost of time. CD on the otherhand can move to different solutions fast, however it is not ef-fective. As mentioned, both optimisation approaches will behybridised with the intent to employ the best of both. An op-timisation result from one optimisation will as such be usedto define the design search space of the other, this will con-tinue in a consecutive manner until a stopping criterion hasbeen reached. Recently this method has been successfullyimplemented, a small comparison is given in figure 3.14214615015415816210 35 60heating energy [KWh]compliance [Nm]Super-structuredoptimisationCombinedmethodCD CD CDCD CDSMS-EMOASMS-EMOASMS-EMOASMS-EMOASMS-EMOASMS-EMOAextended searchFigure 3: The graph shows the improvement of the combinedmethod over a pure super-structured optimisation. The visu-alised solutions are the knee points in the pareto front ap-proximation after each algorithm.

Enabling large design search spaces in building optimisation

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