Using algorithms to designate pre-fabricated wall materials: A case study with two implementation methods
Designating wall materials manually is neither efficient nor competent to find the ideal solution. Therefore, the current study aims to develop a new designation approach based on multi-objective optimization which can automatically explore a larger space of solutions and obtain the optimal material...
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| Published in | Case Studies in Construction Materials Vol. 10; p. e00220 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.06.2019
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2214-5095 2214-5095 |
| DOI | 10.1016/j.cscm.2019.e00220 |
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| Abstract | Designating wall materials manually is neither efficient nor competent to find the ideal solution. Therefore, the current study aims to develop a new designation approach based on multi-objective optimization which can automatically explore a larger space of solutions and obtain the optimal material combination. The core concept is to use wall properties as objectives to search for satisfying combinations, rather than the thresholds to validate manually proposed solutions. A case study is carried out to find material combinations with favourable U value, specific heat capacity, and the total material price. It is realised by two different research methods: using the k-nearest neighbours (k-NN) algorithm in the Python environment, and using the Strength Pareto Evolutionary Algorithm 2 (SPEA2) in the Grasshopper. Finally, the usability test was conducted among 15 architects. Test results confirmed that this new approach could save much time and find solutions with better performance. The present study has significance in reducing architects’ repetitive work while speeding up the decision-making process. |
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| AbstractList | Designating wall materials manually is neither efficient nor competent to find the ideal solution. Therefore, the current study aims to develop a new designation approach based on multi-objective optimization which can automatically explore a larger space of solutions and obtain the optimal material combination. The core concept is to use wall properties as objectives to search for satisfying combinations, rather than the thresholds to validate manually proposed solutions. A case study is carried out to find material combinations with favourable U value, specific heat capacity, and the total material price. It is realised by two different research methods: using the k-nearest neighbours (k-NN) algorithm in the Python environment, and using the Strength Pareto Evolutionary Algorithm 2 (SPEA2) in the Grasshopper. Finally, the usability test was conducted among 15 architects. Test results confirmed that this new approach could save much time and find solutions with better performance. The present study has significance in reducing architects’ repetitive work while speeding up the decision-making process. Designating wall materials manually is neither efficient nor competent to find the ideal solution. Therefore, the current study aims to develop a new designation approach based on multi-objective optimization which can automatically explore a larger space of solutions and obtain the optimal material combination. The core concept is to use wall properties as objectives to search for satisfying combinations, rather than the thresholds to validate manually proposed solutions. A case study is carried out to find material combinations with favourable U value, specific heat capacity, and the total material price. It is realised by two different research methods: using the k-nearest neighbours (k-NN) algorithm in the Python environment, and using the Strength Pareto Evolutionary Algorithm 2 (SPEA2) in the Grasshopper. Finally, the usability test was conducted among 15 architects. Test results confirmed that this new approach could save much time and find solutions with better performance. The present study has significance in reducing architects’ repetitive work while speeding up the decision-making process. Keywords: Optimization, Design automation, Decision making, Design science, Fabrication |
| ArticleNumber | e00220 |
| Author | Zhao, Shenghuan |
| Author_xml | – sequence: 1 givenname: Shenghuan orcidid: 0000-0003-3109-6087 surname: Zhao fullname: Zhao, Shenghuan email: shenghuan.zhao@polimi.it organization: Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133, Milano, Italy |
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| Cites_doi | 10.1016/j.buildenv.2006.11.024 10.1016/j.enbuild.2013.02.003 10.3390/app7090910 10.1177/1369433217739706 10.1016/j.apenergy.2015.11.076 10.1016/j.enbuild.2016.05.054 10.1016/j.engappai.2017.06.020 10.1109/TEVC.2017.2672689 10.1109/TIT.1967.1053964 10.1016/j.buildenv.2008.08.009 10.1016/j.applthermaleng.2003.10.015 10.1016/j.enbuild.2011.05.015 |
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| Keywords | Fabrication Design science Design automation Decision making Optimization |
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| Title | Using algorithms to designate pre-fabricated wall materials: A case study with two implementation methods |
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