Employing outlier and novelty detection for checking the integrity of BIM to IFC entity associations

Conferentiebijdrage

Koo, B., Shin, B. & Krijnen, T.F. (2017). Employing outlier and novelty detection for checking the integrity of BIM to IFC entity associations. Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC 2017), 28 June - 1 July, Taipe, Taiwan

Abstract

 

Although Industry Foundation Classes
(IFC) provide standards for exchanging Building
Information Modeling (BIM) data, authoring tools
still require manual mapping between BIM entities
and IFC classes. This leads to errors and omissions,
which results in corrupted data exchanges that are
unreliable and compromise the interoperability of
BIM models. This research explored the use of two
machine learning techniques for identifying
anomalies, namely outlier and novelty detection to
determine the integrity of IFC classes to BIM entity
mappings. Both approaches were tested on three BIM
models, to test their accuracy in identifying
misclassifications. Results showed that outlier
detection, which uses Mahalanobis distances, had
difficulties when several types of dissimilar elements
existed in a single IFC class and conversely was not
applicable for IFC classes with insufficient number of
elements. Novelty detection, using one-class SVM,
was trained a priori on elements with dissimilar
geometry. By creating multiple inlier boundaries,
novelty detection resolved the limitations encountered
in the former approach, and consequently performed
better in identifying outliers correctly.