Agapaki Brilakis 2018a - Revision history

Scipediacontent at 15:31, 21 January 2021

2021-01-21T15:31:49Z

Scipediacontent: Scipediacontent moved page Draft Content 192157485 to Agapaki Brilakis 2018a

2020-10-14T14:08:44Z

Scipediacontent moved page Draft Content 192157485 to Agapaki Brilakis 2018a

Scipediacontent: Created page with " == Abstract == 90% of the time needed for the conversion from point clouds to 3D models of industrial facilities is spent on geometric modelling due to the sheer number of I..."

2020-10-14T14:08:41Z

Created page with " == Abstract == 90% of the time needed for the conversion from point clouds to 3D models of industrial facilities is spent on geometric modelling due to the sheer number of I..."

New page

== Abstract ==

90% of the time needed for the conversion from point clouds to 3D models of industrial facilities is spent on geometric modelling due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. Our previous work has successfully identified the most frequent industrial objects which are in descending order: electrical conduit, straight pipes, circular hollow sections, elbows, channels, solid bars, I-beams, angles, flanges and valves. We modelled those on a state-of-theart software, EdgeWise and then evaluated the performance of this software for pipeline and structural modelling. The modelling of pipelines is summarized in three basic steps: (a) automated extraction of cylinders, (b) their semantic classification and (c) manual extraction and editing of pipes. The results showed that cylinders are modelled with 75 % recall and 62 % precision on average. We discovered that pipes, electrical conduit and circular hollow sections require 80 % of the Total Modelling Hours (TMH) of the 10 most frequent IOs to build the plant model. TMH was then compared to modelling hours in Revit and showed that 67 % of pipe modelling time is saved by EdgeWise. This paper is 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 i

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_192157485-beopen944-4049-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1007/978-3-319-91635-4_6 http://dx.doi.org/10.1007/978-3-319-91635-4_6]

* [https://www.repository.cam.ac.uk/bitstream/1810/279173/1/EG-ICE2018_012_final_v4.pdf https://www.repository.cam.ac.uk/bitstream/1810/279173/1/EG-ICE2018_012_final_v4.pdf]

DOIS: 10.17863/CAM.26553 10.1007/978-3-319-91635-4_6

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 % of the time needed for the conversion from point clouds to 3D models of industrial facilities is spent on geometric modelling due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. Our previous work has successfully identified the most frequent industrial objects which are in descending order: electrical conduit, straight pipes, circular hollow sections, elbows, channels, solid bars, I-beams, angles, flanges and valves. We modelled those on a state-of-theart software, EdgeWise and then evaluated the performance of this software for pipeline and structural modelling. The modelling of pipelines is summarized in three basic steps: (a) automated extraction of cylinders, (b) their semantic classification and (c) manual extraction and editing of pipes. The results showed that cylinders are modelled with 75 % recall and 62 % precision on average. We discovered that pipes, electrical conduit and circular hollow sections require 80 % of the Total Modelling Hours (TMH) of the 10 most frequent IOs to build the plant model. TMH was then compared to modelling hours in Revit and showed that 67 % of pipe modelling time is saved by EdgeWise. This paper is 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 i
@@ Line 17: / Line 12: @@
 * [https://www.repository.cam.ac.uk/bitstream/1810/279173/1/EG-ICE2018_012_final_v4.pdf https://www.repository.cam.ac.uk/bitstream/1810/279173/1/EG-ICE2018_012_final_v4.pdf]
-DOIS: 10.17863/CAM.26553 10.1007/978-3-319-91635-4_6
+DOIS: 10.17863/cam.26553 10.1007/978-3-319-91635-4_6

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