extractingdifferentspatio-semanticstructures...

EXTRACTING DIFFERENT SPATIO-SEMANTIC STRUCTURESFROM IFC USING A TRIPLE GRAPH GRAMMAR

HELGA TAUSCHER1 and RUDI STOUFFS21,2National University of Singapore1,2{helga.tauscher|stouffs}@nus.edu.sg

Abstract. We report on the findings from adopting a triple graphgrammar approach to convert an IFC buildingmodel into another format,e.g. CityGML. Several rule sets were developed to reflect on both thetype of input geometry and the geometric information sought. We touchupon the application of this structure to different target models.

Keywords. BIM; IFC; CityGML; triple graph grammar;spatio-semantic structure.

1. Introduction and backgroundAn IFC building model may contain an elaborate and detailed description of abuilding that can serve many different purposes, e.g., building energy simulation.However, such simulation or other purposes may require the IFC building modelto be converted into another format. This conversion is a common problem thatreceives much attention. Lilis et al. 2018 describe checking of IFC files forinformation relevant to thermal simulation as a first step towards conversion intoEnergyPlus format. Regidor et al. 2018 present a very basic implementation(e.g., no internal walls) to convert IFC into TRNSYS using Matlab. Choi etal. (2016) include IFC-to-IDF functionality as part of their “BIM-based EPA(energy-performance assessment)” tool but don’t describe how they extract thegeometry, instead focusing on the material library component. Rose and Bazjanac(2013) describe the conversion of an IFC model without defined thermal spaceboundaries into geometry suitable for import into EnergyPlus. However, all theseefforts are very particular to one kind of conversion and fail to address the problemat a more general level. Although Rose and Bazjanac (2013) present an algorithmbased on graph theory, they acknowledge that the algorithm is specific to theone-dimensional heat transmission paradigm as adopted by EnergyPlus.

We report on the adoption of a triple graph grammar (TGG) as a formalframework for conversion of IFC models into CityGML Level of Detail (LoD)3/4 building models, capturing both geometric and semantic information asavailable in the IFC models and representable in CityGML models (Figure 1shows an example of a triple graph). The objective of this conversion is tocreate semantically enriched 3D city models that include both exterior and interiorstructures such as corridors, rooms, internal doors, and stairs. The advantagesof a triple graph grammar approach are considered manifold: firstly, it allows

Intelligent & Informed, Proceedings of the 24th International Conference of the Association forComputer-Aided Architectural Design Research in Asia (CAADRIA) 2019, Volume 1, 605-614. © 2019and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA),Hong Kong.

606 H. TAUSCHER AND R. STOUFFS

for the conversion rules to be graphically defined; secondly, the generation ofthe conversion routines corresponding to these rules can be automated; thirdly,a complete mapping can be achieved in an incremental way by adding rule byrule; fourthly, graphs provide a formal model to analyze conversions; and finally,the rule-based approach allows to compile input and use-case specific conversionprofiles. We refer to Stouffs (2018) for details about the TGG approach.

Figure 1. Triple graph for the spatio-semantic structure of a building, consisting of an IFCgraph (left), a CityGML graph (right), and a correlation graph (dashed lines).

To reduce the scope of the paper, we consider only a strict mapping here, whereno information is added or irreversibly removed. That is, there is no geometricenhancement or generalization during the mapping. If such operations are needed,e.g., triangulation or Boolean operations on CSG, we assume it has either beendealt with in a separate, clearly distinguished preprocessing step or it will becarried out in postprocessing operations on single connected pairs of source andtarget elements or larger parts of the generated triple graph.

For the conversion the spatio-semantic structure, we developed 8 differentrule sets, pertaining to different conditions on the source side and differentrequirements on the target side. In this paper, we focus on the details of theserule sets as well as the implications of each rule set for the source and target side,that is, IFC requirements and CityGML standard conformance, respectively. Weapplied the rule sets to a range of IFC models and analyzed the results with regardto coverage of the IFC input and conformance of the CityGML output. We brieflytouch upon the use of the structure of the rule sets to convert from IFC models intoother formats than CityGML, such as EnergyPlus’ IDF format.

2. Dimensions of the spatio-semantic mappingTo develop triple graph transformation rules for models with geometric content,it is crucial to handle the spatial semantics such that the resulting conversion canbridge potential discrepancies between the source and target schema. In order toidentify and isolate these spatio-semantic aspects of the transformation, we adapt

EXTRACTING DIFFERENT SPATIO-SEMANTIC STRUCTURES FROMIFC USING A TRIPLE GRAPH GRAMMAR

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a process that starts with exemplary source and target instance graphs. These arethen broken into smaller reusable pieces by identifying similarities and differencesbetween various samples. In Section 3 we will then correlate these building blocksand combine them as source and target templates into transformation rules.

Figure 2. Spatio-semantic characteristics of rule sets, different input types in rows (triangulatedor not), different output types in columns (solid elements, boundary surfaces, floor plans).

Focusing on the IFC-to-CityGML conversion, we derived three essentialcharacteristics of the spatio-semantic mapping, each one of which might appear inone of two options. The first one relates to the source geometry and distinguishestriangulated surfaces and original IFC geometry. The second one relates to thetarget type of building elements, whether constructive building elements or theirboundary representation. The third one relates to the target level of detail, eitherLOD3 or LOD0 (floorplans). Thus, the space of mappings consists of threedimensions with two options each, resulting in a total of 8 variants. Each variantcan be described by a combination of three values d0, d1, d2, one for eachdimension, with value 0 or 1 depending on the characteristic of the respectivedimension. In the following we number the variants according to these values as

2∑i=0

di2i + 1. (1)

E.g. if we use original IFC geometry (d0 = 1), create constructive buildingelements (d1 = 0) and project to a floorplan for LOD0 (d2 = 1), we refer to it asvariant 6 (= 1012 + 1), see Figure 2. The remainder of this section describes thedimensions and respective template graphs on source (d0) and target side (d1, d2).

2.1. GEOMETRY EXTRACTION OPTIONS

The first dimension relates to the source geometry and distinguishes• preprocessed triangulated geometry (d0 = 0) from• original IFC geometry representations (d0 = 1).


Figure 3 shows the spatio-semantic IFC source structure for products (d1 = 0), e.g.space, buildingElement in left diagram, and the extraction of either triangulated(middle, d0 = 0) or original IFC geometry (right, d0 = 1).

Figure 3. Spatio-semantic IFC source structures for building elements.

The diagram on the left of Figure 3 also contains the space boundary node(d1 = 1); Figure 4 shows the geometry extraction for this case using eithertriangulated (left, d0 = 0) or original IFC geometry (right, d0 = 1).

Figure 4. Spatio-semantic IFC source structures for boundary surfaces.

2.2. TARGET DEFINITION OPTIONS

The second dimension relates to the targeted type of main spatio-semantic units,distinguishing

• building elements (d1 = 0) from• boundary surfaces (d1 = 1).

Figure 5 illustrates the two types of spatio-semantic CityGML target structures,constructive elements (left, d1 = 0) versus semantic surfaces (right, d1 = 1).Note that in Figure 5 (right), the semantic surface types InteriorWallSurface,FloorSurface, and WallSurface are only used as examples, all types of semanticsurfaces will be modeled in the same way.

Note how interior semantic surfaces are modeled as boundaries of interiorspaces whereas exterior surfaces are modeled as boundaries of storeys.Alternatively, we can also model the exterior surfaces as boundaries of the wholebuilding or all semantic surfaces as boundaries of the constructive elements.


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Figure 5. Spatio-semantic CityGML target structures, constructive elements versus semanticsurfaces.

2.3. PROJECTION

The third dimension relates to the target level of detail, distinguishing• solids 3D or surfaces 2D (d2 = 0) from• floorplan surfaces 2D or curves 1D (d2 = 1).

Figure 6 illustrates the spatio-semantic CityGML target structures, either forLOD3with unprojected 3D/2D geometry (left, d2 = 0) or for LOD0with projected2D/1D floorplan geometry (right, d2 = 1). The two floorplan variants 5 and 8 aredescribed in Konde et al. (2018).

Figure 6. Spatio-semantic CityGML target structures, LOD3 versus LOD0.

3. Rule setsWe are now going to construct triple graph transformation rules from the graphstructures outlined in Section 2. For each rule, we select one source and onetarget graph structure as templates and add connection graph edges between relatedsource (IFC) and target (CityGML) nodes. Ideally we obtain a modular rule setstructure such that we have a rule or a set of rules for each option in each dimensionand these subsets can be freely combined throughout the dimensions as per choiceof the dimensions’ options. However we realized that this is not possible. Instead,we devise three layers, where the rules in each layer are grouped according to somedimension’s choices and for each of the 8 rule sets we select from these groups.

3.1. SPATIAL HIERARCHY

The first layer contains rules that occur in every rule set. These rules create thespatial structure of one or more buildings in a project, further subdivided into partsand/or storeys. The rules of the following layers can subsequently rely on thespatial structure being already in place.

From Figure 5 it can be seen that rooms are also needed in every case: Both


target structures for constructive elements and semantic surfaces contain roomnodes with connecting edges from the building (interiorRoom) as well as fromthe storey (room). By combining the target template with a corresponding sourcetemplate (compare Figure 3), we get a transformation rule as shown in Figure 7,left side.

Figure 7. Rules to create the room nodes (left) and constructive building element nodes (right)in CityGML and attach them to the existing triple graph instance.

3.2. SPATIO-SEMANTIC UNITS: BUILDING ELEMENT STRUCTURE VERSUSBOUNDARY SURFACE STRUCTURE

The second layer contains the rules that create the main spatio-semantic units inCityGML as per choice of dimension d1.

The rule to create the constructive element nodes in the CityGML instancegraph (Figure 7 right), looks similar to the rule for room nodes (Figure 7 left).This can be confirmed by looking at the source and target structures definedin Section 2. Note however the subtle differences in how spaces and buildingelements respectively are modeled in relation to storeys and buildings in IFCversus CityGML. These rules occur in the rule sets for variants 1, 2, 5 and 6.

Asmentioned in Section 2.2we can aim for the semantic surfaces as boundariesof the constructive elements or as boundaries of the rooms. From a geometricpoint of view, this is a matter of a) inverting the surface normals and b) includingor excluding surfaces of the constructive element that are not exposed to the room,that is surfaces between multiple constructive elements. These two aspects arehandled slightly differently in CityGML compared to IFC.

In any case, the concrete type of the semantic surface in CityGML is inferredboth from the type of building element and the type of space (interior or exterior)that define the respective boundary surface. The semantic surfaces given in Figure8 are only examples, other rules generate FloorSurface or CeilingSurface insteadof InteriorWallSurface, and GroundSurface, RoofSurface, OuterFloorSurface,OuterCeilingSurface instead of WallSurface.

Note, how we reach the boundary surface from the space if we want to attachit to the room (left, for interior surfaces), but we have to go through the buildingelements to reach the boundary surface from the storey if we want to attach it to thestorey or building (right, for exterior surfaces). This is because on the IFC side, theexterior space is not included in the spatial building structure. These rules occurin the rule sets for variants 3, 4, 7 and 8.


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Depending on the d1 choice the connected pair of an IFC and a CityGMLnode we end up with in this layer will be of one of two types. For d1 = 0(rules in Figure 7), we have a pair of IfcProduct (supertype of both IfcSpaceand IfcBuildingElement) and AbstractSpace (supertype of both bldg:Room andbldg:ConstructiveElement). For d1 = 1 (rules in Figure 8), we have a pairof IfcRelSpaceBoundary and con:AbstractConstructionSurface (supertype of allsemantic surfaces).

Figure 8. Rules to create interior (left) and exterior (right) semantic surface nodes in CityGMLand attach them to the existing triple graph instance.

3.3. GEOMETRY EXTRACTION FROM IFC AND ASSIGNMENT TO CITYGML

The rules in the third layer add the actual geometry to the spatio-semantic unitscreated with rules from layer 2.

Figure 9. Examplary rules to access IFC geometry nodes and create GML geometry nodes inthe CityGML model for variant 1 (left) and variant 4 (right).

These rules combine an IFC source template (Figures 3middle, left or Figure 4)according to the choice of d0 with a CityGML target template (Figure 6) accordingto the choice of d2. In addition, we have two different types of exit node pairs fromthe layer 2 to attach the geometry to, so that we will have a basic set of 8 rules inthis layer, one for each rule set. Figure 9 shows two of them.

After creating the GML geometry nodes, these have to be populated with lowerlevel geometry elements down to the actual coordinates. Thus we either need


another layer of rules below this layer that converts the IFC geometry resourcesinto the respective GML elements or a post-processing step acting on the createdpairs of source and target nodes. We do not elaborate on the details of this actualgeometry conversion in the current paper, thus we have omitted additional nodesnecessary to position the generated geometry from the rules shown above.

4. Rule application results and implications on the source and target sideHere we present the results from applying the various rule sets to three sampleprojects and also discuss some implications on both IFC and CityGML.

For the spatio-semantic structure we have a total of 27 rules, were each of the8 rule sets contains some of them in varying combinations. We have applied theserule sets to a selection of three models:

• a two-storey residential building (A),• the Revit advanced tutorial model, using the IFC4 export (B),• and a ten-storey office building (C).

The results from applying the rule sets are illustrated in Figure 10 and Table 1.

Figure 10. Results for projects A and B, boundary surfaces (left) and building elements (right).

Table 1. Number of applications per rule for the 3 sample projects A, B, and C.

For the availability of IFC input data we identify issues when it comes to theboundary surface variants (d1 = 1): Space boundaries are not always properlyexported from CAD software to IFC and not easy to compute (Lilis et al. 2017).They are neither contained in RV nor in DTV (the two certification MVDs) andalthough they are contained in EV (Energy View), connection geometry is notallowed in this view. In the IFC schema, connection geometry is optional and evenif provided, only the surface geometry related to the space is mandatory, whereasthe surface geometry on the building element is optional. Outer space boundariestowards the environment are rarely included in exported IFC files. Even whenspace boundaries are included in IFC, if we use preprocessed geometry (d0 = 0),the preprocessing may not cover boundary surfaces.

In these cases where boundary surfaces are not present or not accessible, we canalternatively infer semantic surfaces from the solid similar to the reconstructionof semantics from uninterpreted 3D models described by Nagel et al. (2009).Although here, we can use the semantics of the host building element, for some


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surface types this is not easy to achieve automatically. Another problem is that inpractice, preprocessing engines such as IfcOpenShell default to triangulation andalso resolve Boolean operations in constructive solid geometry representations,which we may not be desired, e.g. in the floorplan cases (d2 = 1).

For the standard conformance of the CityGML output the choice ofspactio-semantic mapping has the following implication: Constructive buildingelements (d1 = 0) and LOD0 (d2 = 1) are only available in CityGML3 (Kutzner2018). Thus, only variants 3 and 4 can be realized with the concepts available inCityGML2. These are exactly the variants that rely on boundary surfaces. Thisreflects the discrepancy of the spatio-semantic paradigms of IFC and CityGML.

5. Rule reuse and adaptation to a different target schemaThe triple graph grammar approach can also be used to convert from IFC modelsinto other formats than CityGML, such as gbXML or EnergyPlus’ IDF format forthe sake of energy analyses. The question arises whether a rule structure devisedfor IFC-to-CityGML conversion might also be applicable to other conversions,such as IFC-to-IDF. That is, whether triple graph grammar rules with the same IFCtemplate graph, but obviously different target template graphs, may be applicableto both IFC-to-CityGML and IFC-to-IDF conversions. If yes, the advantages ofthe triple graph grammar approach become much more significant.

While we are actively exploring the response to this question, exploringEnergyPlus’ IDF as the target definition, we have not yet concluded theinvestigation. As such, we can only conjecture a response at this time. The spaceboundary concept in IFC that we use for CityGML semantic surface extractionwas originally meant for energy simulation. Thus, the source structure of the rulesets for variants 3 and 4 are appropriate and it can be conjectured that these can beused as they are with a substituted target structure. However, the different overalstructure of the IDF target type graph as compared to CityGML leads to differentrules, since the hierarchical processing is driven by the output structure.

6. ConclusionWe conclude that the rule structure presented is well adapted to map betweendifferent spatial semantics on the source (IFC) and target (CityGML) side.However, we have not yet been able to generalize these findings to other targetformats. In addition, we can conclude that to bridge the conceptual discrepancybetween IFC and CityGML in terms of spatio-semantics with a strict mappingapproach we either need to pose extended requirements on the IFC side to containsome CityGML-like constructions or we need to rely on IFC-like concepts beingpresent in an extended CityGML scheme.

Geometry processing that uses existing IFC concepts to fulfil theserequirements should combine1. a space boundary generation algorithm (Lilis et al. 2017) that considers the

external envelope while maintaining connection to constructive elements,2. discretization that can produce polygon patches instead of triangles and does not

carry out Boolean operations by default.


Such geometry processing can be achieved either through pre- / post-processingwith modification and enrichment on the source or target side or during theconversion process itself in an operation local to the to related nodes. In general,point 1 can be achieved better in a separate processsing step, because of the globalcontext in which this processing occurs, while point 2 can actually be achievedduring the conversion itself.

Acknowledgements: This material is based on research/work supportedby the National Research Foundation under Virtual Singapore Award No.NRF2015VSG-AA3DCM001-008. The authors would like to acknowledge theremaining members of the research team: Patrick Janssen, James Crawford, FilipBiljecki, Amol Konde, Joie Lim, and Kamel Adouane.

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