=Paper=
{{Paper
|id=Vol-3081/09paper
|storemode=property
|title=Ontological approach for LOD-sensitive BIM-data management
|pdfUrl=https://ceur-ws.org/Vol-3081/09paper.pdf
|volume=Vol-3081
|authors=Janakiram Karlapudi,Prathap Valluru,Karsten Menzel
|dblpUrl=https://dblp.org/rec/conf/ldac/KarlapudiVM21
}}
==Ontological approach for LOD-sensitive BIM-data management==
https://ceur-ws.org/Vol-3081/09paper.pdf
Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
Ontological approach for LOD-sensitive BIM-data
management
Janakiram Karlapudi1[0000-0003-4236-6492], Prathap Valluru1 and Karsten Menzel1
1 Institute of Construction Informatics, Technische Universität Dresden, Dresden, Germany
janakiram.karlapudi@tu-dresden.de
Abstract. The construction industry is a collaborative environment with the in-
volvement of multiple disciplines and activities throughout the Building Lifecy-
cle Stages. The collaboration requires the iterative and coordinated exchange of
information for significant improvement of the building design, construction
and management. The successful representation of these information refine-
ments enables the identification of the required level of detail (LOD) for data
sharing parameters between the multiple disciplines. Since the last decade,
LOD is a promising approach for efficient representation of semantically rich
BIM data in different levels. Despite the improvement, there is a lack of effi-
cient implementation in building lifecycle functionalities, because of their fun-
damental heterogeneity, versatility and adaptability. The proposed approach en-
ables the representation of LOD-sensitive BIM data through the formal defini-
tion of ontologies. The paper validates this approach based on the concept of
competency questions and their respective SPARQL queries. With the demon-
stration and validation, the paper provides the conceptual proof for the practical
application of the developed approach. The proposed solution can also be easily
adaptable and applicable to the present BIM process since the representation of
BIM data in different ontologies (BOT, ifcOWL, etc.) are within reach.
Keywords: LOD, ontologies, competency questions, SPARQL queries, BIM.
1 Introduction and Background
The Architecture, Engineering, Construction, and Operation (AECO) industry is a
collaborative environment and requires the iterative and cooperated exchange of in-
formation between multiple disciplines at different stages of the building lifecycle
process. The collaboration enables the efficient and economical design of the building
and its management [1–3]. The collaboration is also crucial in identifying the needed
information for a specific discipline and restricting the information exchange to the
identified level of need [4, 5].
Building Information Modelling (BIM) is an emerging approach in the construc-
tion industry to describe and digitally represent information [6–8]. The concept like
Information Delivery Manual aims to enhance the business application of the BIM
process through the definition of discipline-based process maps and the information
requirements for their execution [5]. Similarly, the approaches like LOD concepts are
Copyright 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0)
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introduced in the BIM process for specifying the set of the required level of data ex-
port or import parameters between the multiple disciplines [9]. The defined LOD
levels from the different national standards or publications describe the granularity
and the sequential refinement of both the geometric and semantics information about
an object [10]. These technical developments in the BIM process significantly im-
prove the collaboration through effective sharing of the dedicated data to the specific
domain requirements. The process and practice of representing the information in
different levels of detail, depending on the purpose, increase the quality and reliability
of the BIM data at various stages of the construction process. This LOD-based BIM
data representation enables to define the characteristics of each BIM object at differ-
ent levels of detail and allows the stakeholder to understand the usability and limita-
tion of the information.
There are different standards of publications to represent the building object’s ge-
ometry and attribute information in different LOD levels. The publications from USA
UK, Italy, Netherlands, Denmark, etc. are introduced similar concepts for LOD in
terms of Level of Development, Level of Detail, Level of Geometry, Level of Infor-
mation, Level of Completeness, Level of Reliability, etc. It is true that no such LOD
levels define a set of pre-requirements about the data within the level but provide a
language to define these requirements based on the project, location and organization.
However, despite the improvements, there is a lack of successful implementation and
management of LOD functionalities within the existing BIM solutions [11]. It is
mainly because of confusion on data requirements at certain LOD levels and the in-
sufficient understanding of diverse frameworks for the adoption and representation of
LOD levels.
Moreover, the uncertainty in the defined data requirements for each LOD level
may also severely affect the collaboration in AECO projects. The versatility of data
requirements needed for a specific process is also not allowed to specify a specific
LOD level for a BIM model. Although different LOD systems are available for re-
quired data representation, these complexities brought a need in the AECO industry to
define a solution that provides common model deliveries [12]. The research in this
publication aimed to develop a common and flexible LOD framework that can ac-
commodate different LOD systems in BIM data management. The framework allows
different practitioners and organizations to work under a common platform irrespec-
tive of project, location and requirements. The framework also aimed to enable the
user-based or project-based requirement specification for each LOD level.
The development of the LOD framework is majorly based on the linked data and
ontology concepts, which brings flexibility, compliance and alignment capabilities
through logical reasoning and knowledge inferencing. Furthermore, the structured
representation of building data in ontologies (triples or graphs) enables the stakehold-
ers to semantically interpret (update, extract or delete data using queries) data for
various domain-specific operations with minimal human interventions [13]. The ca-
pabilities of linked data also ensure the linking of contextual information with the
BIM data through the concept of IRI’s. This ontological approach is also easily adapt-
able and applicable to the present BIM process since the representation of BIM data
in different ontologies (BOT, ifcOWL, etc.) are within reach [14–16]. Further discus-
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sions regarding the ontology-based framework development and BIM data manage-
ment are elaborated in the coming sections of the paper.
2 State-of-art-analysis
2.1 LOD systems
The LOD levels of BIM data should be generally defined for different stages of pro-
jects when data sharing takes place. This is a pragmatic approach to indicate the gran-
ularity of the BIM data and data refinements throughout the project progression over
multiple stages of the building. Furthermore, this would allow stakeholders to verify
that project information in detailed enough to meet their requirements, and enabling
them to decide whether to proceed to the next project stages or not.
Different countries have developed dedicated LOD standards generating a complex
situation at an international level (Table 1). The abbreviation “LOD” is used in vari-
ous meanings in different countries, such as the USA - BIMForum Specification [17],
UK - BS 1192-1 [18] and PAS 1192-2, 3 [19], and Italy UNI 11337 part 4 [20] (see
also Table 1). In the terminology used by the U.S. legislators since 2013, LOD has
assumed the meaning of Level of Development. In the USA context, there is no for-
mal difference between geometric and non-geometric information. Nevertheless, this
distinction is embedded in the two reference documents of the BIMForum Specifica-
tion where Part I identifies the element geometry and Part II identifies the attribute
information.
Table 1. LOD system according to the different standards of specifications.
Country LOD means Subtype Scale
LOD 100, LOD 200, LOD 300, LOD
Level of LOD: As Designed
USA 350, LOD 400
Development
LOD: As Built LOD 500
LOD: Level of De- LOD 1, LOD 2, LOD 3, LOD 4,
Level of tail LOD 5, LOD 6
UK
Definition LOI: Level of Infor- LOI 1, LOI 2, LOI 3, LOI 4, LOI 5,
mation LOI 6
LOG – Geometrical LOG A, LOG B, LOG C, LOG D,
Level of
Objects LOG E, LOG F, LOG G
Italy Development
LOI – Information LOI A, LOI B, LOI C, LOI D, LOI
of Objects
Objects E, LOI F, LOI G
According to the UK standards, LOD has the general meaning of Level of Defini-
tion, which includes the two distinct parts of the Level of Detail and Level of Infor-
mation. The level of detail represents the description of the graphic contents at each
stage, while the level of information represents the description of non-geometric con-
tents. Simultaneously, the Italian norm UNI 11337:2017 defines the LOD as the Level
of Development of the objects and is further divided into LOG, Level of development
of objects – Geometric Attributes, and LOI, Level of development of the object –
Informational Attributes.
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2.2 Information Management
In the area of information management, the support for LODs centres around the chal-
lenge to represent (1) various LOD systems, (2) multiple versions of information
about the same objects and (3) the connections of LOD-specific data to processes over
the building lifecycle. A proper representation not only allows the users to access and
work inside one specific LOD but enable various cross-LOD functions: to access the
history of values, to utilize the links, annotations, and other enrichments of previous
LOD objects with those in subsequent LODs, to check the consistency and possible
deviations between objects at different LODs, and to determine what kinds of adjust-
ments to previous LOD models would be needed. Moreover, it helps to connect LODs
to other aspects of building information: to keep track of the origin of information and
to maintain the rules for validating it against the requirements of subsequent activities.
This analysis of LOD requirements suggests at least the following areas where ontol-
ogy definitions are needed to properly support LODs. For more clear applicability and
understanding a set of Competency Questions (CQ) are defined based on the require-
ments for each area.
1. LOD frameworks: The representation of various levels, their relations, and the
links to associated definitions.
i. CQ1 - What is the relation between the LOD sub-type to the LOD scale?
ii. CQ2 – How do LOD levels (LOD scale) are related to each other?
iii. CQ3 - How do LOD scales are defined based on the LOD system?
2. LOD sensitive BIM data: The representation of versioned properties of objects to
capture the data at multiple different levels in an organized manner.
i. CQ4 - What is the value for the object properties in previous levels?
3. Connection of LOD framework to processes: The representation of LOD-specific
data by activities along with the sources.
i. CQ5 - From which source the value of “Wall width” is extracted?
ii. CQ6 - What level of properties are required for a specific activity?
3 Ontology-based LOD representation
3.1 LOD framework
The development of an ontology framework for LOD representation is progressed
towards providing all the answers for the above-identified requirements. From the
general analysis of different LOD systems, a methodological ontology schema is de-
veloped and illustrated in Fig. 1. Since different construction projects can adopt dif-
ferent LOD systems, the developed ontological structure for the LOD framework can
accommodate the different standards of representations detailed in Table 1. The
methodological idea is to represent LOD systems and their levels as classes, which
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can then be instantiated on a project-to-project basis. The class dicl1:LODFramework
can be instantiated with the frameworks called USA BIMForum, UK LOD, Italian
LOD, etc. (refer to Table 1). Similarly, the levels in different frameworks are added
as instances to the class dicl:LODLevel. Later on, the link between the framework and
its respective levels are generated using the object property dicl:hasLevel and its in-
verse property dicl:isLevelOf.
Fig. 1. Ontology-based LOD framework
Furthermore, relationships between the levels of a framework are indicated using the
transitive object properties dicl:hasNextLevel, dicl:hasSubLevel and their inverse
properties dicl:hasPreviousLevel, dicl:hasSuperLevel respectively. A sub-property
chain axiom is assigned to the object properties dicl:hasLevel to define semantic in-
terpretation between the LOD levels and LOD subtypes. An exemplary demonstration
is presented in the below subsection to elaborate on the functionalities of the devel-
oped ontology framework.
3.2 Exemplary demonstration
For the demonstration, the BIMForum LOD framework (see in Table 1) is considered
and align to the developed ontological schema. As represented in Fig. 2, the instances
inst:AsDesigned and inst:AsBuilt are assigned as levels for the instance
inst:USA_BIMForum using dicl:hasLevel object property. Since this object proper-
ty’s (dicl:hasLevel) domain and range are fixed to the classes dicl:LODFramework
and dicl:LODLevel respectively, the inferencing engine automatically inference the
new knowledge by saying the instance inst:USA_BIMForum belongs to the class
dicl:LODFramework and the other instances are belonging to the class
dicl:LODLevel. This inferenced knowledge is illustrated in Fig. 2 and Fig. 3 using the
grey dashed lines and the defined relationships are represented with solid black lines.
1 Prefix dicl:
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Similarly, the same representational concepts can also be used for the remaining LOD
systems mentioned in Table 1.
Fig. 2. Relationship between the LOD Sub-types
Moreover, the relationship between the instances inst:AsDesigned and inst:AsBuilt is
assigned using dicl:hasNextLevel transitive object property. As defined in the ontolo-
gy framework, the dicl:hasNextLevel object property has assigned an inverse relation-
ship with dicl:hasPreviousLevel. Which substantially generates the new knowledge
between these instances concerning the previous stage relationship. The inferenced
knowledge from the inverse relationships is represented by using yellow dashed lines
in Fig. 2 and Fig. 3.
Fig. 3. Relationship between the LOD scales
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Furthermore, the LOD scale information (LOD levels) regarding each LOD sub-
type is defined by using dicl:hasSubLevel transverse object property and its inverse
property dicl:hasSuperLevel. Because of the transverse property nature of these object
properties, any further sub-levels of a LOD scale is also considered as a LOD level.
Similarly, due to the defined axiom to dicl:hasLevel property, all these LOD scales
are inferred as Levels to the dicl:LODFramework (inst:USA_BIMForum). Also, the
relationship between the LOD levels is developed by using dicl:hasNextLevel object
property. Along with this defined information, a new relationships are generated be-
tween LOD levels, for example, between inst:LOD350 and inst:LOD500, which is
clearly represented with blue dashed lines in Fig. 3. These generated relationships are
because of the transitive property characteristic of the object properties
dicl:hasNextstage and dicl:hasPreviousStage.
4 BIM data management
4.1 LOD sensitive BIM data
The representation of BIM data in a LOD-sensitive manner is based on the ideology
that (1) Identifiers of objects are not LOD sensitive, but (2) All properties of objects
can be LOD sensitive. That means, objects are not associated with a specific LOD
level but their property values can be. It is not possible to ask which LOD an object
belongs to because the object can simultaneously have properties belonging to many
different LODs.
Fig. 4. LOD-sensitive BIM data representation
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The developed ontological schema in Fig. 4 express the same ideology and establish
relationships between BIM object data and its respective LOD levels. In the devel-
oped ontology framework, a class dice2:BuildingObject represents the building ele-
ments. Similarly, the class dicv3:Property in the ontology framework make it possible
to add an ultimate number of properties to building objects using the object property
dicv:hasProperty. Also, the class dicv:PropertyState is defined to indicate the growth
of these properties accuracy throughout the project life-cycle. The framework also
supports defining meta-data attributes for each object (e.g. label and ID), property
(e.g. label, value, unit, and source) and property state (e.g. label, source, timestamp,
value, etc.). The class dicl:LODlevel is connected to dicv:PropertyState to indicate its
level of growth by representing a specific LOD level. The object property
dicl:hasLODLevel is used to develop this relationship between dicv:ProperyState and
dicl:LODLevel. A sample BIM data representation is illustrated in Fig. 5 according to
the developed ontology framework.
Fig. 5. Example BIM data representation at different LOD levels
4.2 LOD framework to processes
LOD data by activities. The connections of LODs to processes happen through the
information objects created in activities and/or their requirement to execute activities.
Different activities require different object properties at a specific level of detail. In
this ontological framework, an explicit mapping methodology is specified to represent
these requirements. The framework enumerates the information interms of (1) Object
type (e.g. wall, slab, etc.), (2) Property name (e.g. a single property or grouping of
properties), (3) Nature of data needed (e.g. a LOD level or other specification) for
each activity. Based on the adopted methodology, the class dice:BuildingObject is
further interrelated to the Activities (dicp4:Activity) in the project using the object
property dicl:hasObject. As illustrated in Fig. 4, the adopted ontology framework is
2 Prefix dice:
3 Prefix dicv:
4 Prefix dicp:
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completely fulfilling these connection requirements between LOD levels and the ac-
tivities within the project.
Sources of LOD data. The possible sources of LOD data are different kinds of in-
formation objects. These are primarily BIM models but can also include drawings,
documents, messages and events/notifications. In some cases also the information
contained in various information management systems can be relevant. Each infor-
mation object – explicitly or implicitly – provides information at certain LOD levels,
and therefore the data should primarily be converted in a LOD sensitive manner. Ac-
cording to the developed framework, the sources of information can represent by
using the class dici5:InformationContainer. The connection of each property in the
LOD sensitive representation to the source of data can be attained with an object
property dicl:isDerivedFrom (Fig. 4).
5 Framework validation
The validation of the developed framework is performed by running the SPARQL
queries based on the competency questions listed and used for the framework devel-
opment. As represented in Fig. 2 and Fig. 3, the information related to instances and
relations (solid black lines) is developed to the main default framework ontology.
Similarly, an example BIM data (in Fig. 5) is populated to the developed framework
to verify the BIM data management process using LOD’s. After the population of
instances, a reasoner called Pellet has used to inference the new knowledge and to
check the consistency, correctness of the developed ontology. Thereafter performed
several queries to extract the generated information to check the consistency and qual-
ity by comparing it with the original information from the standards (see Table 1).
Some of the developed query profiles and their results are presented in Table 2.
Table 2. Queries and their results for listed competency questions
SPARQL Query Query Results
Query1:- What are the LOD scale for USA Level of Development (LOD) system?
SELECT ?System ?Sub_type ?Scale System Sub_type Scale
Where{ USA_BIMForum AsDesigned LOD200
? System dicl:hasLevel ?Sub_type. USA_BIMForum AsDesigned LOD300
?Sub_type dicl:hasSubLevel ?Scale . USA_BIMForum AsDesigned LOD350
FILTER(?System USA_BIMForum AsDesigned LOD400
=:USA_BIMForum).} USA_BIMForum AsBuilt LOD500
Query 2:- What is the relationship between LOD200 and LOD500?
SELECT ?Level1 ?relation ?Level2 Level1 relation Level2
Where{?Level1 ?relation ?Level2 .
FILTER(?Level1=:LOD200 && LOD200 dicl:hasNextLevel LOD500
?Level2=:LOD500)}
5 Prefix dici:
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SPARQL Query Query Results
Query 3:- What is the relationship between the USA LOD system and LOD Scale?
SELECT ?System ?realtionship ?Scale System relationship Scale
Where{ USA_BIMForum dicl:hasLevel LOD200
?System dicl:hasLevel ?Sub_type. USA_BIMForum dicl:hasLevel LOD300
?Sub_type dicl:hasSubLevel ?Scale .
USA_BIMForum dicl:hasLevel LOD350
?System ?realtionship ?Scale .
FILTER(?System=:USA_BIMForum)} USA_BIMForum dicl:hasLevel LOD400
USA_BIMForum dicl:hasLevel LOD500
Query 4:- What is the value of “Wall width” in different LOD levels?
SELECT ?Wall ?Width ?Level Wall Width Level
Where{ ?Object dice:hasLabel ?Wall.
?Object BasicWall:500+.. 352 LOD300
dicv:hasProperty/dicv:hasPropertyState
?PS.
?PS dicv:hasValue ?Width . BasicWall:500+.. 348 LOD350
?PS dicl:hasLODLevel ?Level .}
6 Conclusion and Future work
The paper explains the developed ontological framework for the effective representa-
tion of various LOD systems and their corresponding levels through the demonstra-
tion example. The development process is carried out based on the analysed require-
ments (CQ), which should be fulfilled by a LOD framework. With the defined and
inherited knowledge capabilities of the developed ontology framework represents its
applicability and functionalities in terms of representing the BIM data in different
LOD levels. The ontological representation of the LOD systems brings flexibility,
compliance and alignment capabilities through logical reasoning and knowledge in-
ferencing. The paper also explains the framework capabilities corresponding to the
BIM data management in terms of LOD sensitive data representation and its connec-
tion to processes. The developed ontological framework is also easily adaptable and
applicable to the present BIM process since the representation of BIM data in differ-
ent ontologies (BOT, ifcOWL, etc.) are within reach. Finally, the validation and eval-
uation of the developed LOD framework are performed based on the SPARQL que-
ries, which represents the framework requirements.
As represented in the paper, the developed methodological framework is only ad-
dressing the attribute information about the object but not the geometrical infor-
mation. The further possible extension of the methodology is corresponding to the
representation and management of building object’s geometrical data. The research in
this paper is limited to the representation of LOD-sensitive BIM data. The future fo-
cus of this research is on the validation of existing BIM data in the context of differ-
ent LOD levels using SHACL constraints.
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7 Acknowledgement
This research is part of the EU project entitled “BIM4EEB – BIM-based fast toolkit
for the Efficient rEnovation in Buildings” which has received funding from European
Union’s H2020 research and innovation program under grant agreement No 820660.
The authors gratefully acknowledge the support and funding from the European Un-
ion. The content of this publication reflects the author view only and the Commission
is not responsible for any use that may be made of the information it contains.
8 References
1. Abualdenien, J., Borrmann, A.: Multi-LOD model for describing uncertainty and checking
requirements in different design stages. In: Karlshøj, J., Scherer, R. (eds.) eWork and
eBusiness in Architecture, Engineering and Construction, pp. 187–195. CRC Press. Lon-
don (2018). DOI: https://doi.org/10.1201/9780429506215-24.
2. Abualdenien, J., Borrmann, A.: A meta-model approach for formal specification and con-
sistent management of multi-LOD building models. Advanced Engineering Informatics
40(4), pp. 135–153 (2019). DOI: https://doi.org/10.1016/j.aei.2019.04.003.
3. Karlapudi, J., Menzel, K., Törmä, S., Hryshchenko, A., Valluru, P.: Enhancement of BIM
Data Representation in Product-Process Modelling for Building Renovation. In: Nyffeneg-
ger, F., Rivest, L., Ríos, J., Bouras, A. (eds.) Product lifecycle management enabling smart
X. PLM 2020, Rapperswil, Switzerland, vol. 594, pp. 738–752. Springer. Cham (2020).
DOI: https://doi.org/10.1007/978-3-030-62807-9_58.
4. Mangialardi, G., Di Biccari, C., Pascarelli, C., Lazoi, M., Corallo, A.: BIM and PLM As-
sociations in Current Literature. In: Ríos, J., Bernard, A., Bouras, A., Foufou, S. (eds.)
Product lifecycle management and industry of the future. PLM 2017, Seville, Spain, vol.
517, pp. 345–357. Springer. Cham (2017). DOI: https://doi.org/10.1007/978-3-319-72905-
3_31.
5. Xu, Z., Abualdenien, J., Liu, H., Kang, R.: An IDM-Based Approach for Information Re-
quirement in Prefabricated Construction. Advances in Civil Engineering 2020, pp. 1–21
(2020). DOI: https://doi.org/10.1155/2020/8946530.
6. Keller, M., O'Donnel, J., Menzel, K., Keane, M.: Integrating the Specification, Acquisition
and Processing of Building Performance Information. Tsinghua Science and Technology
13(1), pp. 1–6 (2008).
7. Zadeh, P. A., Wang, G., Cavka, H. B., Staub-French, S., Pottinger, R.: Information Quality
Assessment for Facility Management. Advanced Engineering Informatics 33, pp. 181–205
(2017). DOI: https://doi.org/10.1016/j.aei.2017.06.003.
8. Karlapudi, J., Shetty, S.: A methodology to determine and classify data sharing require-
ments between OpenBIM models and energy simulation models. In: Sternal, M.,
Ungureanu, L.-C., Böger, L., Bindal-Gutsche, C. (eds.) 31. Forum Bauinformatik, pp.
331–338. Universitätsverlag der TU Berlin. Berlin (2019). https://nbn-
resolving.org/urn:nbn:de:bsz:14-qucosa2-735487.
9. Treldal, N., Vestergaard, F., Karlshoj, J.: Pragmatic Use of LOD – a Modular Approach.
In: Christodoulou, S., Scherer, R. (eds.) EWork and eBusiness in Architecture, Engineer-
ing and Construction. ECPPM 2016, Limassol, Cyprus, vol. 2016, pp. 129–136. CRC
Press. London (2016).
https://backend.orbit.dtu.dk/ws/files/127947941/ecppm2016_paper_54.pdf.
113
Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
10. Hooper, M.: Automated model progression scheduling using level of development. Con-
struction Innovation 15(4), pp. 428–448 (2015). DOI: https://doi.org/10.1108/CI-09-2014-
0048.
11. Papadonikolaki, E., Leon, M., Mahamadu, A. M.: BIM solutions for construction lifecycle:
A myth or a tangible future? In: Karlshøj, J., Scherer, R. (eds.) eWork and eBusiness in
Architecture, Engineering and Construction, pp. 321–328. CRC Press. London (2018).
DOI: https://doi.org/10.1201/9780429506215-40.
12. van Berlo, L. A. H. M., Bomhof, F.: Creating the Dutch National BIM Levels of Devel-
opment. In: Issa, R. I., Flood, I. (eds.) Computing in Civil and Building Engineering
(2014), pp. 129–136. American Society of Civil Engineers (2014). DOI:
https://doi.org/10.1061/9780784413616.017.
13. Karlapudi, J., Menzel, K.: Analysis on automatic generation of BEPS models from BIM
model. In: Monsberger, M., Hopfe, C. J., Krüger, M., Passer, A. (eds.) BauSIM 2020 - 8th
Conference of IBPSA Germany and Austria, pp. 535–542. Verlag der Technischen
Universität Graz. Austria (2020). https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-
735477.
14. Beetz, J., van Leeuwen, J., Vries, B. de: IfcOWL: A case of transforming EXPRESS
schemas into ontologies. Artificial Intelligence for Engineering Design, Analysis and
Manufacturing 23(1), pp. 89–101 (2009). DOI:
https://doi.org/10.1017/S0890060409000122.
15. Pauwels, P., Zhang, S., Lee, Y.-C.: Semantic web technologies in AEC industry: A litera-
ture overview. Automation in Construction 73, pp. 145–165 (2017). DOI:
https://doi.org/10.1016/j.autcon.2016.10.003.
16. Chen, W., Chen, K., Cheng, J. C. P.: Towards an Ontology-based Approach for Infor-
mation Interoperability Between BIM and Facility Management. In: Smith, I. F. C.,
Domer, B. (eds.) Advanced computing strategies for engineering. 25th EG-ICE Interna-
tional Workshop 2018, Lausanne, Switzerland, vol. 10864, pp. 447–469. Springer. Cham,
Switzerland (2018). DOI: https://doi.org/10.1007/978-3-319-91638-5_25.
17. BIMFORUM: Level of Development (LOD) Specification Part I & Commentary - For
Building Information Models and Data (2020). BIMFORUM.
18. PAS 1192-2: Specification for information management for the capital/delivery phase of
construction pro-jects using building information modelling (2013). BSI Standards Lim-
ited.
19. PAS 1192-3: Specification for information management for the operational phase of assets
using building information modelling (2014). BSI Standards Limited.
20. UNI 11337-4: Building and civil engineering works – Digital management of building in-
formation process – Part 4: Evolution and development of information within models doc-
uments and objects (2017). UNI.
114