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Machine learning based construction simulation and optimization
Harbin Institute of Technology Resources Engineering, Harbin, CHINA.
Harbin Institute of Technology Resources Engineering, Harbin, CHINA.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.ORCID iD: 0000-0002-4695-5369
2018 (English)In: Proceedings of the 2018 Winter Simulation Conference / [ed] M. Rabe; A.A. Juan; N. Mustafee; A. Skoogh; S. Jain; B. Johansson, IEEE, 2018, p. 2025-2036, article id 8632290Conference paper, Published paper (Refereed)
Abstract [en]

Building construction comprises interaction and interdependence among processes. Discrete-event simulation (DES) is widely applied to model these processes interaction. To find optimal construction plans, optimization technique is usually integrated with DES. However, present simulation-optimization integrated method directly invokes simulation model within optimization algorithms, which is found significantly computationally expensive. This study proposes a machine learning based construction simulation and optimization integrated method. After trained by DES, the machine learning model accelerates simulation-optimization integration by nearly real-time providing fitness evaluation within optimization. This method was implemented into a real construction project for construction time-cost-environment optimization. Results show that proposed machine learning based method significantly reduce computing time compared with original simulation-optimization integration. Less than 1% of construction cost and time improvement were miss, while greenhouse gas emissions obtained same performance. The new method could be a more effective DES and optimization integration approach for practical engineering application.

Place, publisher, year, edition, pages
IEEE, 2018. p. 2025-2036, article id 8632290
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
URN: urn:nbn:se:ltu:diva-73236DOI: 10.1109/WSC.2018.8632290Scopus ID: 2-s2.0-85062601673ISBN: 9781538665725 (print)OAI: oai:DiVA.org:ltu-73236DiVA, id: diva2:1297010
Conference
2018 Winter Simulation Conference, WSC 2018; The Swedish Exhibition and Congress CentreGothenburg; Sweden; 9 - 12 December 2018
Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-03-18Bibliographically approved

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Lu, Weizhuo

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf