Prioritizing object types for modelling existing industrial facilities
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Publication Date
2018-12Journal Title
Automation in Construction
ISSN
0926-5805
Publisher
Elsevier BV
Volume
96
Pages
211-223
Type
Article
Metadata
Show full item recordCitation
Agapaki, E., Miatt, G., & Brilakis, I. (2018). Prioritizing object types for modelling existing industrial facilities. Automation in Construction, 96 211-223. https://doi.org/10.1016/j.autcon.2018.09.011
Abstract
The cost of modelling existing industrial facilities currently counteracts the benefits these models provide. 90 % of the modelling cost is spent on converting point cloud data to 3D models due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. However, automatically classifying millions of IOs is a very hard classification problem due to the very large number of classes and the strong similarities between them. This paper tackles this challenge by (1) discovering the most frequent IOs and (2) measuring the manhours required for modelling them in a state of the art software, EdgeWise. This allows to measure (a) the Total Labor Hours (TLH) spent per object type and (b) the performance of EdgeWise. We discovered that pipes, electrical conduit and circular hollow sections require 80 % of the TLH needed to build the plant model. We showed that EdgeWise achieves cylinder detection with 75% recall and 62% precision. This paper is the first to discover the most laborious to model IOs and the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving it.
Sponsorship
European Commission (334241)
Engineering and Physical Sciences Research Council (EP/N021614/1)
Identifiers
External DOI: https://doi.org/10.1016/j.autcon.2018.09.011
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285510
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: http://creativecommons.org/licenses/by-nc-nd/4.0/
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