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Open AccessArticle

Filtering of Irrelevant Clashes Detected by BIM Software Using a Hybrid Method of Rule-Based Reasoning and Supervised Machine Learning

1
Department of Civil Engineering, Feng Chia University, Taichung 407, Taiwan
2
Department of Civil and Construction Engineering, National Yunlin University of Science and Technology, Yunlin 640, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5324; https://doi.org/10.3390/app9245324
Received: 9 October 2019 / Revised: 24 November 2019 / Accepted: 2 December 2019 / Published: 6 December 2019
(This article belongs to the Special Issue BIM in the Construction Industry)
Construction projects are usually designed by different professional teams, where design clashes may inevitably occur. With the clash detection tools provided by Building Information Modeling (BIM) software, these clashes can be discovered at an early stage. However, the number of clashes detected by BIM software is often huge. The literature states that the majority of those clashes are found to be irrelevant, i.e., harmless to the building and its construction. How to filter out these irrelevant clashes from the detection report is one of the issues to be resolved urgently in the construction industry. This study develops a method that automatically screens for irrelevant clashes by combining the two techniques of rule-based reasoning and supervised machine learning. First, we acquire experts’ knowledge through interviews to compile rules for the preliminary classification of clash types. Subsequently, the results of the initial classification inferred by the rules are added into the training dataset to improve the predictive performance of the classifiers implemented by supervised machine learning. The average predictive performance obtained by using the hybrid method is up to 0.96, which has been improved from the traditional machine learning process only using individual or ensemble learning classifiers by 6%–17%. View Full-Text
Keywords: clash detection; supervised machine learning; building information modeling (BIM) clash detection; supervised machine learning; building information modeling (BIM)
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Lin, W.Y.; Huang, Y.-H. Filtering of Irrelevant Clashes Detected by BIM Software Using a Hybrid Method of Rule-Based Reasoning and Supervised Machine Learning. Appl. Sci. 2019, 9, 5324.

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