Clash relevance prediction in BIM-Based design coordination using Bayesian statistics

Yuqing Hu, Daniel Castro-Lacouture

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Multiple disciplines in construction projects have greatly improved the efficiency of their coordination efforts by using building information modeling (BIM) for clash detection. However, because the outcome of clash detection includes many irrelevant clashes that have no substantial influence on a project, the precision of the method has been questioned. To address this problem, this paper uses Bayesian statistics to distinguish relevant from irrelevant clashes for improving the clash detection of BIM. The paper compares naive Bayesian, the Bayesian network, and Bayesian probit regression, and validates the effectiveness of each method. Additionally, the paper discusses how prediction can be improved by combining the three methods by majority rule. Bayesian statistics provide a method of mining knowledge from historical data and leads to clash management processes that are more independent of the project experience of BIM coordinators.

Original languageEnglish (US)
Title of host publicationConstruction Research Congress 2018
Subtitle of host publicationConstruction Project Management - Selected Papers from the Construction Research Congress 2018
EditorsRebecca Harris, Chao Wang, Christofer Harper, Charles Berryman, Yongcheol Lee
PublisherAmerican Society of Civil Engineers (ASCE)
Pages649-658
Number of pages10
ISBN (Electronic)9780784481271
DOIs
StatePublished - 2018
EventConstruction Research Congress 2018: Construction Project Managemen, CRC 2018 - New Orleans, United States
Duration: Apr 2 2018Apr 4 2018

Publication series

NameConstruction Research Congress 2018: Construction Project Management - Selected Papers from the Construction Research Congress 2018
Volume2018-April

Other

OtherConstruction Research Congress 2018: Construction Project Managemen, CRC 2018
CountryUnited States
CityNew Orleans
Period4/2/184/4/18

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction

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