It could be relevant to ask the users which entities they are (or are not) interested in in the output.
Documented as part of Isaac Fatokun, Arun Raveendran Nair, Thamer Mecharnia, Maxime Lefrançois, Victor Charpenay, Fabien Badeig and Antoine Zimmermann, (2023) "Modular Knowledge integration for Smart Building Digital Twins", LDAC 2023
Section 5.1, item 2_pruneObjects.py.
Describe the solution you'd like
The tool could rapidly analyse the content of the IFC, and display check boxes to the user for each type of entity that exists in the IFC file (+ number of instances). The user could uncheck the boxes they are not interested in
Additional context
For example for the use case described in the paper above, We only need a few type of elements for our use case. This step keeps
triples with RDF terms whose IRI contain one of: ”site”, ”building”, ”storey”, ”space”, ”wall”,
”window”, ”ifcowl_ifcfurniture”, ”electricappliance”. It prunes triples with RDF terms
whose IRI contain one of ”airterminal”, ”beam”, ”cablecarrierfitting”, ”cablecarriersegment”, ”column”, ”covering”, ”curtainwall”, ”ductfitting”, ”ductsegment”, ”flowterminal”,
”ifcowl_ifcopeningelement”, ”lightfixture”, ”member”, ”plate”, ”railing”, ”ramp”, ”roof”,
”slab”, ”stair”, ”stairflight”
It could be relevant to ask the users which entities they are (or are not) interested in in the output.
Documented as part of Isaac Fatokun, Arun Raveendran Nair, Thamer Mecharnia, Maxime Lefrançois, Victor Charpenay, Fabien Badeig and Antoine Zimmermann, (2023) "Modular Knowledge integration for Smart Building Digital Twins", LDAC 2023
Section 5.1, item 2_pruneObjects.py.
Describe the solution you'd like
The tool could rapidly analyse the content of the IFC, and display check boxes to the user for each type of entity that exists in the IFC file (+ number of instances). The user could uncheck the boxes they are not interested in
Additional context
For example for the use case described in the paper above, We only need a few type of elements for our use case. This step keeps triples with RDF terms whose IRI contain one of: ”site”, ”building”, ”storey”, ”space”, ”wall”, ”window”, ”ifcowl_ifcfurniture”, ”electricappliance”. It prunes triples with RDF terms whose IRI contain one of ”airterminal”, ”beam”, ”cablecarrierfitting”, ”cablecarriersegment”, ”column”, ”covering”, ”curtainwall”, ”ductfitting”, ”ductsegment”, ”flowterminal”, ”ifcowl_ifcopeningelement”, ”lightfixture”, ”member”, ”plate”, ”railing”, ”ramp”, ”roof”, ”slab”, ”stair”, ”stairflight”