A TRIPLE GRAPH GRAMMAR APPROACH TO MAPPING IFC MODELS INTO CITYGML BUILDING MODELS

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1 A TRIPLE GRAPH GRAMMAR APPROACH TO MAPPING IFC MODELS INTO CITYGML BUILDING MODELS RUDI STOUFFS 1 National University of Singapore 1 stouffs@nus.edu.sg 1. Introduction Abstract. A triple graph grammar approach is adopted as a formal framework for semantic and geometric conversion of IFC models into CityGML Level of Detail 3/4 building models. The triple graph grammar approach supports a semantic mapping from IFC to CityGML, the generation of conversion routines from this mapping, and an incremental approach to achieving a complete and near-lossless mapping. The objective of this approach is the development of a methodology and algorithms to automate the conversion of Building Information Models into CityGML building models, capturing both geometric and semantic information as available in the BIM models, in order to create semantically enriched 3D city models that include both exterior and interior structures. Keywords. BIM; CityGML; conversion; semantic; automated. The Singapore government has embarked on a project to establish a three-dimensional city model and collaborative data platform for Singapore. The aim of this Virtual Singapore project is to achieve a 3D digital platform that will enable users from different sectors to develop sophisticated tools and applications for test-bedding concepts and services, planning and decision-making, and research on technologies to solve emerging and complex challenges for Singapore ( Virtual Singapore, 2017). Using both airborne and mobile mapping surveys, the current incarnation of the Virtual Singapore model is as an LoD2 (Level of Detail) CityGML model (Figure 1). Current endeavors aim to create a semantically enriched LoD3/4 CityGML model. In this paper, we elaborate on a research project to develop a methodology and algorithms to automate the conversion of Building Information Models (BIM) into CityGML building models, capturing both geometric and semantic information as available in the BIM models, and including exterior as well as interior structures such as corridors, rooms, internal doors, and stairs. While automation has been demonstrated before, mainly for exterior building shells in LoD1 and LoD2, this research focuses on achieving a complete and near-lossless mapping for both exterior and interior structures in LoD3/4. A formal framework for semantic and geometric conversion adopting triple graph grammars is being developed in order to improve the degree of automation T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping, Proceedings of the 23 rd International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2018, Volume 2, and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) in Hong Kong.

2 42 R. STOUFFS and completeness of the mapping process. This formal framework includes the specification of an Application Domain Extension (ADE) to CityGML in order to allow for the representation of semantic information beyond what CityGML currently provides for. In addition, we are working with existing BIM models obtained from government agencies as well as major BIM software vendors. The development of the methodology and algorithms does not target the conversion of native BIM models as used by specific BIM software applications but, instead, BIM models in the Industry Foundation Classes (IFC) data format for openbim. Nevertheless, the existing BIM models are native BIM models and we are investigating export configurations that result in high-quality IFC4 models for the problem at hand. Considering that IFC is merely a data format and not a model specification, the project objective is to address a wide variety of native BIM models as resulting from major BIM software applications and reflecting on how buildings and their indoor structures are modeled by different BIM users and practices.the knowledge gained from this will be much more broadly applicable than the conversion into CityGML as a particular target format. In this paper, we contemplate the notion of a complete and near-lossless conversion and present triple graph grammars as a formal framework for semantic and geometric conversion of IFC models into CityGML LoD 3/4 building models. Figure 1. Building model in LoD1 to LoD4. (Source: Benner et al., 2010). 2. The automation challenge Several attempts have been made to simplify BIM models and integrate them with Geospatial Information Systems (GIS). For example, Isikdag (2006) demonstrated the transfer of selective information from BIM to (ESRI) Shapefiles and Geodatabases. The Open Geospatial Consortium (OGC) completed tests on the integration of CityGML and IFC models in OWS-4 testbed (Lapierre and Cote, 2008). These tests focused on the development of a Web Feature Server (WFS) for IFC data, adopting the OGC Web Services Common Specification as a proven and widely adopted infrastructure for designing the web services for BIM, and on the translation of IFC to CityGML, but, limited to IFC Spaces (rooms). While a number of efforts have been discontinued (e.g., de Laat and van Berlo, 2011), there exists both a commercial ( Safe Software, 2015) and an open source (Donkers, 2013) solution for conversion from IFC to CityGML. Safe Software FME supports reading IFC and writing CityGML, however the conversion is not a native function and the required data model transformations need to be built in FME. Pre-made conversion procedures do exist ( Safe Software, 2015); however, these converters do not transform the IFC geometries and, thus, do not create

3 A TRIPLE GRAPH GRAMMAR APPROACH TO MAPPING IFC MODELS INTO CITYGML BUILDING MODELS 43 completely valid LoD3 CityGML geometries. At the same time, the semantics mapping is limited and not fully correct. Donkers (2013) does demonstrate the automatic generation of valid and semantically rich CityGML LoD3 building models, however the conversion is not complete as it is output-driven, rather than input-driven. Rather than attempting to store all IFC information into a CityGML model, his approach is to construct a proper CityGML model using only the information that is needed for a successful conversion. Donkers (2013) elaborates an IFC to CityGML LoD3 transformation in three stages. In the first stage, relevant building elements are extracted using semantic filtering. In the second stage, a volumetric representation of the complete building is generated through geometric transformation using Boolean and morphological operations like dilation and erosion. In the third stage, a refinement process is applied to guarantee the compliance with ISO 19107:2003. The process is focused on LoD3 with an outlook to LoD4. LoD1 and LoD2 are not considered. As can be concluded from these examples, the difficulty is not so much in the automation, but in the complete and near-lossless mapping between IFC and CityGML models (including interior structures), especially in the light of variations in BIM models as developed with different BIM software applications and by different users and practices, and in the light of future requirements on a semantically enriched 3D city (CityGML) model, for example to export information to other formats and applications. The default geometric representation of physical building elements in IFC is volumetric. This can be a parametric representation (e.g., extrusion of a parametric profile), a Constructive Solid Geometry (CSG) or a boundary representation (B-rep). However, volumetric building elements are not supported by CityGML; instead, building elements in CityGML are modeled only as boundary surfaces for buildings or rooms. This means that the volumetric representation of building elements in IFC has to be converted into boundary surfaces of the CityGML building or room (Figure 2). Additionally, IFC uses a local Cartesian coordination system; a global geographic location can be optionally specified for the site object. On the other hand, CityGML uses global coordinates given a reference coordinate system. The corresponding conversion between different geo-referencing methods can be done by well-known coordinate transformations. Figure 2. Differences in the model of a building story between IFC (left) and CityGML (right). (Source: Nagel et al., 2009).

4 44 R. STOUFFS Another difference is semantic. For example, in IFC, the physical building elements are represented as objects with relations. Such relations can be used, e.g., for material information, properties, or connections between building elements. However, building elements in CityGML are only modeled as boundary surfaces, and not as objects and, thus, do not allow for further properties or relations to other boundary surfaces. For example, while CityGML LoD3 models specify both outer walls and windows, inherently, they do not include any relationship between a window and the wall it is part of. Many other semantic differences exist as well. For this, it is important to define a formal semantic mapping between the two schemas. Several studies (Benner et al., 2005; Isikdag and Zlatanova, 2009; Nagel et al., 2009) have emphasized that a formal framework for strict semantic and geometric conversion is required for a complete integration of CityGML and IFC. While it is possible to semi-automate part of the semantic mapping between IFC and CityGML itself, for example, using linguistic and text-mining techniques (Cheng et al., 2015), only a manual method of semantic mapping can guarantee accuracy. BIM models are far more detailed than traditional GIS models, however, the CityGML schema can easily be extended using the concept of Application Domain Extension (ADE). Various ADEs have been considered by researchers to extend the CityGML schema and allow for additional information available in the IFC schema to be included in CityGML. For example, de Laat and van Berlo (2011) consider a GeoBIM-ADE to allow more semantic data from IFC to be added into CityGML, with a focus on extending details of existing CityGML objects (e.g., Room, Window, Door, Opening, Buildingfurniture, BuildingInstallation). Deng et al. (2015) developed a Semantic City Model (SMC) ADE to enrich CityGML models by providing more semantic information such as the linkage relationship between walls and building stories. Hijazi et al. (2011) employ the UtilityNetworkADE to map interior utility information contained in an IFC model. Finally, all this work is further complicated by the fact that IFC is merely a data format and not a model specification, and that there is no universally accepted building model for IFC. Others have attempted to define a reasonably standard building model (e.g., El-Mekawy et al., 2012). However, considering the wide variety of IFC models representing actual buildings in Singapore, as available to the Housing & Development Board (HDB), the Building & Construction Authority (BCA), and other potential users, as resulting from different BIM software applications, and as modeled by different BIM users and practices, and the objective to achieve a complete and near-lossless mapping, such an approach is not a practical one. Instead, we closely collaborate with HDB, BCA, and major BIM software vendors, and collect IFC models from different sources for testing and demonstration, in order to ensure that the methodology, algorithms, and conversion routines work with BIM models developed with any of the major BIM software applications, including Revit (Autodesk), AECOsim/Microstation (Bentley), ArchiCAD (Graphisoft) and Tekla (Trimble). At the same time, we attempt to extract minimal requirements for e-submission of BIM models that will facilitate the conversion of BIM models to CityGML and can assist in other conversions as well.

5 A TRIPLE GRAPH GRAMMAR APPROACH TO MAPPING IFC MODELS INTO CITYGML BUILDING MODELS The research methodology To address these challenges, we adopt a two-pronged approach (Figure 3). Firstly, we are developing a formal framework for semantic and geometric conversion that supports models to be automatically exchanged. This framework will include an identification of the necessary transformations to convert IFC geometry data into CityGML geometry, as well as the ability to assign semantic data from the IFC building model into elements of the CityGML model. This research draws upon literature on formal approaches for semantic and geometric conversion between IFC and CityGML (e.g., Benner et al., 2005; Isikdag and Zlatanova, 2009; Nagel et al., 2009), on Application Domain Extensions (ADEs) for CityGML relating to BIM (e.g., de Laat and van Berlo, 2011; Hijazi et al., 2011; Deng et al., 2015), and the experiences and results of the OGC FutureCities Pilot in collaboration with BuildingSMART. It also considers user requirements, specifically from HDB and BCA, such as the practice to convert to the STL format to support Computational Fluid Dynamics (CFD) wind simulations. While conversions from CityGML to other formats are not directly targeted, requirements on a CityGML model relating to such conversions, such as the need to identify the relationship between a window and the wall it belongs to in the CityGML model, are considered. Additionally, major BIM software vendors are consulted on the semantic mapping to ensure broad applicability. Secondly, we translate the mapping between the two data models, including the necessary transformations as identified, into conversion algorithms and software routines that allow a complete and near-lossless automated mapping from IFC to CityGML. Figure 3. Two-pronged approach: formal framework and software development.

6 46 R. STOUFFS 3.1. COMPLETE AND NEAR-LOSSLESS The formal objective of the project is to convert BIM models into CityGML building models in order to create semantically enriched 3D city models. Adopting a complete (and near-lossless) mapping from IFC to CityGML for both exterior and interior structures in LoD3/4 ensures the most semantically rich model but is, otherwise, ill-advised. BIM models generally contain very detailed building information, information that may be of little value in a city model and will only slow any processing of the city model. For this reason, one of the secondary objectives of the project is to define the meaning of a complete and near-lossless mapping and to identify exactly which information should be included in the mapping. As an example, a complete breakdown of the material composition of a wall may not be required, but a roughness value for the finishing layer of an exterior wall may be relevant from the point of view of being able to perform a CFD simulation of part of the city model. For this reason, we are collecting use cases from collaborators and other potential users of the 3D city model to understand which information needs to be included in the complete and near-lossless mapping in order to ensure their use cases apply. Unfortunately, these use cases are not necessarily obvious as the adoption and use of a 3D city model of Singapore is a mere promise at this time and will only be made available to government agencies in July of Although there may be obvious use cases, such as drawing from the 3D city model in order to perform a CFD simulation of a building within its built environment, such use cases are few. At a basis, we are focusing on architectural BIM models, ignoring structural and MEP (Mechanical, Electrical, and Plumbing) models and information. At the same time, we are focusing on LoD3/4 (exterior and interior), though we are exploring whether some plan-based LoD0 information may need to be included for particular use cases. In particular, we are considering to introduce some kind of use case-based configurability to allow for alternative complete and near-lossless mappings. Of course, such configurability would not apply to the integration of BIM-derived CityGML models into the overarching Virtual Singapore model. Also, it is not our intention to put the onus back on the user by providing a complex and detailed configurability interface. This research does not intend to provide a solution for all and everybody, but a practical solution for our collaborators and anybody with similar requirements, and the knowledge that underlies such solution and can serve other objectives. In conclusion, complete and near-lossless is a term we are defining for this research in cooperation with our collaborators in order to ensure the research and its solution(s) are driven by user needs. Some configurability may be provided TRIPLE GRAPH GRAMMARS Triple graph grammars are a grammar formalism first introduced by Andy Schürr in 1994 as a technique for model transformation (Schürr, 1994). The triple graph rules correlate two graphs through a third graph. These rules can serve to (formally) relate two types of models, to transform a model of one type into

7 A TRIPLE GRAPH GRAMMAR APPROACH TO MAPPING IFC MODELS INTO CITYGML BUILDING MODELS 47 another, to compute the correspondence between two existing models, or to maintain the consistency between the two types of models (Kindler and Wagner, 2007). In our research, we adopt triple graph grammars to formally relate IFC and CityGML, both semantically and geometrically, and to transform a BIM model, expressed as an IFC graph, into a CityGML model expressed as a GML graph. The relationships between the two graphs as established within the transformation process are captured in the edges of the third graph that link from nodes in the IFC graph to nodes in the CityGML graph (Figure 4). Triple graph grammars can be used for both forward and backward transformations, but here we consider only a one-directional transformation. The use of a triple graph grammar allows us to develop the formal mapping from IFC to CityGML incrementally. An added advantage of the triple graph grammar is that while the rules can be expressed graphically, these can be automatically translated into software routines to perform the actual transformation. Figure 4. Triple graph example correlating an IFC graph and a CityGML graph. Dashed lines denote edges belonging to the correlation graph. (Author: Helga Tauscher). Figure 5 shows an example of a rule identifying an interior wall surface within the IFC graph and creating a corresponding node in the CityGML graph. An interior wall surface within IFC is identified as an internal, physical space boundary that relates to a wall and a space and has a connection surface geometry. It is the latter geometry that is transformed into an interior wall surface node in the CityGML graph; both are related through a connection edge. We consider two types of rules. The previous example specifies a feature rule, identifying a feature pattern in IFC and creating a corresponding node or feature in CityGML. The other type is a relation rule, identifying a relation pattern in IFC and creating a corresponding relation in CityGML. Currently, we are applying these rule types mainly in an alternating and recursive manner, where a feature rule may invoke a relation rule upon application and where this relation rule in turn may invoke another feature rule that is required to apply in order to be able to create the relation edge. As an example, a feature rule may map an IFCBuilding node onto a bldg:building node in the GML graph. This feature rule may then

8 48 R. STOUFFS invoke a relation rule in order to create a consistsofbuildingpart relationship between the bldg:building node and a bldg:buildingpart node. However, as the latter hasn t yet been created, the appropriate feature rule would be called upon first. A more extensive example is presented in Figure 6. Though this alternating manner obviously limits the applicability of these rules to a recursive, hierarchical development, it allows for a straightforward generation of conversion routines from this mapping, and it can easily be extended to include other rule types and patterns. Figure 5. Example of a triple graph rule transforming an interior wall surface from IFC into CityGML. The left-hand-side of the rule specifies four IFC nodes and their mutual edges; the right-hand-side of the rule adds (indicated in red) a CityGML node and a connection edge between an existing IFC node and the new CityGML node. (Author: Helga Tauscher). 4. Conclusion A triple graph grammar approach is adopted as a formal framework for semantic and geometric conversion of IFC models into CityGML Level of Detail 3/4 building models. The triple graph grammar approach supports a semantic mapping from IFC to CityGML, the generation of conversion routines from this mapping, and an incremental approach to achieving a complete and near-lossless mapping of Building Information Models into CityGML building models, capturing both geometric and semantic information as available in the BIM models, in order to create semantically enriched 3D city models that include both exterior and interior structures. Acknowledgements This material is based on research/work supported by the National Research Foundation under Virtual Singapore Award No. NRF2015VSG-AA3DCM I would like to acknowledge the input from the entire research team: Patrick Janssen, James Crawford, Filip Biljecki, Helga Tauscher, Salman Khalili Araghi, Chen Kok Kiong and Amol Konde. I would also like to acknowledge the project collaborators, Ordnance Survey International for its contribution to the research and development, the Housing & Development Board and Building & Construction Authority for collaborating with us on use cases and providing us with their feedback, and Bentley Systems, Graphisoft and Trimble for the insightful discussions we have had on IFC and BIM. Additionally, I would like to acknowledge the feedback we received on the project developments from the Government Technology Agency of Singapore,

9 A TRIPLE GRAPH GRAMMAR APPROACH TO MAPPING IFC MODELS INTO CITYGML BUILDING MODELS 49 Singapore Land Authority, and the Urban Redevelopment Authority. Figure 6. A collection of triple graph rules, both feature rules (left) and relation rules (right), to recursively convert a city model, its buildings, building parts, rooms and boundary surfaces of these rooms. (Author: Helga Tauscher). References Safe Software : Available from < (accessed 28th September 2017). Virtual Singapore : Available from < apore> (accessed 12th December 2017). Benner, J., Geiger, A. and Häfele, K.-H.: 2010, Concept for building licensing based on standardized 3D geo information, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-4/W15, Benner, J., Geiger, A. and Leinemann, K.: 2005, Flexible generation of semantic 3D building models, Proc of the 1st Intern. Workshop on Next Generation 3D City Models, Cheng, J., Deng, Y. and Anumba, C.: 2015, Mapping BIM schema and 3D GIS schema semi-automatically utilizing linguistic and text mining techniques, Journal of Information

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