State-of-Practice on As-Is Modelling of Industrial Facilities

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-the-art 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 it.

[1]  George Vosselman,et al.  Segmentation of point clouds using smoothness constraints , 2006 .

[2]  Konrad Schindler,et al.  Joint classification and contour extraction of large 3D point clouds , 2017 .

[3]  Jan Dirk Wegner,et al.  Contour Detection in Unstructured 3D Point Clouds , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Ioannis Brilakis,et al.  Prioritising Object Types of Industrial Facilities to Reduce As-Is Modelling Time , 2018 .

[5]  A. Patil,et al.  An adaptive approach for the reconstruction and modeling of as-built 3D pipelines from point clouds , 2017 .

[6]  Frank Schultmann,et al.  Building Information Modeling (BIM) for existing buildings — Literature review and future needs , 2014 .

[7]  Satoshi Kanai,et al.  As-built modeling of piping system from terrestrial laser-scanned point clouds using normal-based region growing , 2013, J. Comput. Des. Eng..

[8]  James M. Douglas,et al.  Conceptual Design of Chemical Processes , 1988 .

[9]  Carl T. Haas,et al.  Automatic Detection of Cylindrical Objects in Built Facilities , 2014, J. Comput. Civ. Eng..

[10]  José Luis López Cuadrado,et al.  GEODIM: A Semantic Model-Based System for 3D Recognition of Industrial Scenes , 2017 .

[11]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[12]  George Vosselman,et al.  The 3D reconstruction of straight and curved pipes using digital line photogrammetry , 1998 .

[13]  Stefano Tornincasa,et al.  The future and the evolution of CAD , 2010 .

[14]  Michele Fumarola,et al.  Generating virtual environments of real world facilities: Discussing four different approaches , 2011 .

[15]  C. Kim,et al.  Knowledge-Based Approach for 3D Reconstruction of As-Built Industrial Plant Models from Laser-Scan Data , 2013 .

[16]  Christian Boucheny,et al.  Multi-Sensor As-Built Models of Complex Industrial Architectures , 2015, Remote. Sens..