Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Eng    2022, Vol. 9 Issue (1) : 104-116    https://doi.org/10.1007/s42524-020-0111-7
RESEARCH ARTICLE
Semantics for linking data from 4D BIM to digital collaborative support
Calin BOJE1(), Veronika BOLSHAKOVA2, Annie GUERRIERO1, Sylvain KUBICKI1, Gilles HALIN3
1. Luxembourg Institute of Science and Technology (LIST), 5 Avenue des Hauts-Forneaux, Esch-sur-Alzette L-4362, Luxemburg
2. UMR n°3495 Modèles et Simulations pour l’Architecture et le Patrimoine Centre de Recherche en Architecture et Ingénierie (MAP-CRAI), 1 Rue Bastien Lepage, Nancy 54000, France; Centre National de la Recherche Scientifique (CNRS), 3 Rue Michel-Ange, Paris 75794, France
3. UMR n°3495 Modèles et Simulations pour l’Architecture et le Patrimoine Centre de Recherche en Architecture et Ingénierie (MAP-CRAI), 1 Rue Bastien Lepage, Nancy 54000, France; Université de Lorraine (UL), Nancy 54000, France
 Download: PDF(1806 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Synchronous collaboration sessions within the context of 4D BIM position construction professionals into a complex socio–technical system. This system includes hardware, software, people, and broader community aspects. This article strictly focuses on the ontology representation of synchronous collaboration sessions with collocated collective decision-making. The model is designed by considering various 4D BIM model uses while a digital multiuser touch table facilitates the collaboration between actors. The outlined ontological model aims to improve interoperability and to move toward a knowledge-driven, smart-built environment paradigm. A knowledge engineering methodology is outlined, by virtue of which the semantics of the presented model are defined and discussed. Concepts from nearby knowledge fields, especially from the Industry Foundation Classes, are reused. Several examples on querying the knowledge base according to the project meeting requirements are outlined to demonstrate the benefits of using the model. Although 4D BIM model data can be imported by using standard formats, capturing data about the social context remains a challenge in the future. This is expected to change the ontology model structure by considering user ergonomics, data modeling requirements, as well as technical implementation constraints.
Keywords 4D BIM      ontology      IFC      decision-making      linked data      collaboration      planning     
Corresponding Author(s): Calin BOJE   
Just Accepted Date: 11 April 2020   Online First Date: 14 May 2020    Issue Date: 14 February 2022
 Cite this article:   
Calin BOJE,Veronika BOLSHAKOVA,Annie GUERRIERO, et al. Semantics for linking data from 4D BIM to digital collaborative support[J]. Front. Eng, 2022, 9(1): 104-116.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-020-0111-7
https://academic.hep.com.cn/fem/EN/Y2022/V9/I1/104
Fig.1  Linking heterogeneous distributed data via the SW paradigm to improve collaboration during planning meetings.
Fig.2  Methodology steps employed for the design and testing of the 4DCollab ontology.
Ontology/Schema Reference Concept fields
Geometry Time Scheduling Collaboration Resource LOD
IFC4 ADD2 TC1 schema BuildingSMART International (2017) √ √ √ √
IFC2 ´3 TC1 schema BuildingSMART International (2007) √ √ √ √
PROMONT ontology Abels et al. (2006) √ √ √
Microsoft Project schema Microsoft (2019) √ √
W3C Time ontology W3C (2017)) √
Collaboration ontology Knoll et al. (2010) √
Ontology for Property Management (OPM) Rasmussen et al. (2018) √
Building Topology ontology (BOT) Schneider (2017) √
Ontology for Management of Geometry (OMG) Wagner et al. (2019) √ √
Tab.1  Identified relevant ontologies classified by major concept fields
Fig.3  Comparing the model differences for representing planning tasks in successive IFC schema versions.
Fig.4  Initial 4DCollab ontology with imported (external) IfcOwl classes (IFC4 version).
Scope Example SPARQL query
Session data
(Fig. 5)		 “What is the data attached to the current session?”
SELECT ?subject?predicate?object
WHERE { ?subject?predicate?object.
FILTER (?subject= res:Session1) }
Building walls
(Fig. 6)		 “Which are the wall building elements within the IFC model?”
SELECT ?wall?type?wallType
WHERE { ?wall?type?wallType.
FILTER (?type= ifc:IfcWall ||?type= ifc:IfcWallStandardCase) }
Meeting
preparation		 “What are the imported documents for the current session?”
SELECT ?session?document
WHERE { ?model res:importedFrom?document.
?session res:usesModel?model.
?session res:startDate?date.
FILTER (?date= “29/03/2019”) }
Model
filtering		 “What are the physical model objects and their IFC ID within the model?”
SELECT ?object?id
WHERE { ?object rdf:type?class.
?object ifc:globalIf_IfcRoot?guid.
?guid express:hasString?id.
FILTER (?class= 4dOnto:PhysicalObject) }
Interaction
analysis		 “Which are the annotations performed by users during the last session?”
SELECT ?user?annotation?session
WHERE { ?session res:hasParticipant?user.
?user res:performsInteraction?annotation.
?annotation rdf:type?class.
?session res:startDate?date.
FILTER (?date= “16/06/2016” &&?class= 4dOnto:Annotation) }
Tab.2  Example SPARQL queries and their equivalent natural language questions
Fig.5  Sample query for selecting session data and its results outputted on the system prototype.
Fig.6  Sample query for selecting building walls and its results outputted on the system prototype.
1 F H Abanda, J H M Tah, R Keivani (2013). Trends in built environment semantic web applications: Where are we today? Expert Systems with Applications, 40(14): 5563–5577
https://doi.org/10.1016/j.eswa.2013.04.027
2 S Abels, F Ahlemann, A Hahn, K Hausmann, J Strickmann (2006). PROMONT—A project management ontology as a reference for virtual project organizations. In: OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”. Springer, 4277: 813–823
3 J Beetz, J van Leeuwen, B de Vries (2009). IfcOWL: A case of transforming EXPRESS schemas into ontologies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 23(1): 89–101
https://doi.org/10.1017/S0890060409000122
4 C Boje, V Bolshakova, A Guerriero, S Kubickia, G Halin (2019). Ontology assisted collaboration sessions on 4D BIM. In: Creative Construction Conference 2019, 591–596
5 V Bolshakova, A Guerriero, G Halin (2018). Identification of relevant project documents to 4D BIM uses for a synchronous collaborative decision support. In: Creative Construction Conference 2018. Ljubljana, 1036–1043
6 V Bolshakova, G Halin, P Humbert, C Boton (2017). Digital synchronous collaboration workspace and 3D interactions for an AEC project. Decision-making scenario evaluation. In: International Conference on Cooperative Design, Visualization, and Engineering. Springer, 168–176
7 C Boton, S Kubicki, G Halin (2011). Method to design coordinated multiple views adapted to user’s business requirements in 4D collaborative tools in AEC. In: Proceedings of the 15th International Conference on Information Visualisation. London: IEEE, 96–101
8 C Boton, S Kubicki, G Halin (2013). Designing adapted visualization for collaborative 4D applications. Automation in Construction, 36: 152–167
https://doi.org/10.1016/j.autcon.2013.09.003
9 C Boton, S Kubicki, G Halin (2015). 4D/BIM simulation for pre-construction and construction scheduling: Multiple levels of development within a single case study. In: Creative Construction Conference 2015. Diamond Congress Ltd., 500–505
10 BuildingSMART International (2007). IFC2×3 Technical Corrigendum 1
11 BuildingSMART International (2017). IFC4 Addendum 2 Technical Corrigendum 1
12 B Butkovic, D Heesom, D Oloke (2019). The need for multi-LOD 4D simulations in construction projects. Journal of Information Technology in Construction (ITcon), 24: 256–272
13 CIC (2011). BIM project execution planning guide. Version 2.1. Pennsylvania: The Computer Integrated Construction (CIC) Research Group, Department of Architecture Engineering, The Pennsylvania State University
14 4DCollab (2020). 4DCollab-project. Available at: 4dcollab-project.eu
15 L Ding, Y Zhou, B Akinci (2014). Building Information Modeling (BIM) application framework: The process of expanding from 3D to computable nD. Automation in Construction, 46: 82–93
https://doi.org/10.1016/j.autcon.2014.04.009
16 dotNetRDF (2019). dotNetRDF—An open source. NET library for RDF
17 G Fidan, I Dikmen, A M Tanyer, M T Birgonul (2011). Ontology for relating risk and vulnerability to cost overrun in international projects. Journal of Computing in Civil Engineering, 25(4): 302–315
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000090
18 J, Gao M Fischer (2008). Framework & case studies comparing implementations & impacts of 3D/4D modeling across projects. Technical Report. Stanford
19 A Ghaffarianhoseini, J Tookey, A Ghaffarianhoseini, N Naismith, S Azhar, O Efimova, K Raahemifar (2017). Building Information Modelling (BIM) uptake: Clear benefits, understanding its implementation, risks and challenges. Renewable & Sustainable Energy Reviews, 75: 1046–1053
https://doi.org/10.1016/j.rser.2016.11.083
20 A Guerriero, S Kubicki, F Berroir, C Lemaire (2017). BIM-enhanced collaborative smart technologies for LEAN construction processes. In: 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC). Funchal: IEEE, 1023–1030
21 H Hamledari, B McCabe, S Davari, A Shahi (2017). Automated schedule and progress updating of IFC-based 4D BIMs. Journal of Computing in Civil Engineering, 31(4): 04017012
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000660
22 I Horrocks, B Parsia, P Patel-Schneider, J Hendler (2005). Semantic web architecture: Stack or two towers? In: International Workshop on Principles and Practice of Semantic Web Reasoning. Springer, 37–41
23 S Howell, Y Rezgui (2018). Beyond BIM: Knowledge Management for a Smarter Built Environment. BRE Electronic Publications
24 Hu Z, Zhang J (2011). BIM- and 4D-based integrated solution of analysis and management for conflicts and structural safety problems during construction: 2. Development and site trials. Automation in Construction, 20(2): 167–180
25 Immersion (2020). Meetiiim collaborative working platform, 2020. Available at: shariiing.com/en
26 ISO (2019). ISO 9241-210:2019 Ergonomics of human–system interaction—part 210: Human-centred design for interactive systems
27 M Kassem, N Dawood, R Chavada (2015). Construction workspace management within an Industry Foundation Class-Compliant 4D tool. Automation in Construction, 52: 42–58
https://doi.org/10.1016/j.autcon.2015.02.008
28 S Knoll, T Plumbaum, J F, de Luca E W Hoffmann (2010). Collaboration ontology: Applying collaboration knowledge to a generic group support system. In: Proceedings of the Group Decision and Negotiation Meeting (GDN). Omaha, NE, 12–26
29 R G Kreider, J I Messner (2013). The uses of BIM: Classifying and selecting BIM uses (Version 0.9). University Park, PA: Penn State Computer Integrated Construction
30 S Kubicki, A Guerriero, L Schwartz, E Daher, B Idris (2019). Assessment of synchronous interactive devices for BIM project coordination: Prospective ergonomics approach. Automation in Construction, 101: 160–178
https://doi.org/10.1016/j.autcon.2018.12.009
31 M Laakso, A O Kiviniemi (2012). The IFC standard: A review of history, development, and standardization. Journal of Information Technology in Construction (ITcon), 17(9): 134–161
32 A Lee, S Wu, A Marshall-Ponting, G Aouad, J Tah, R Cooper, C Fu (2005). nD modelling—A driver or enabler for construction improvement?  RICS Research Paper Series, 5: 6
33 Y C Lee, C M Eastman, J K Lee (2015). Validations for ensuring the interoperability of data exchange of a building information model. Automation in Construction, 58: 176–195
https://doi.org/10.1016/j.autcon.2015.07.010
34 K McKinney, J Kim, M Fischer, C Howard (1996). Interactive 4D-CAD. Computing in Civil Engineering, 20: 383–389
35 S Mehrbod, S Staub-French, N Mahyar, M Tory (2019). Characterizing interactions with BIM tools and artifacts in building design coordination meetings. Automation in Construction, 98: 195–213
https://doi.org/10.1016/j.autcon.2018.10.025
36 Microsoft (2019). Microsoft Office Project 2007 XML Data Interchange Schema
37 H Moon, N Dawood, L Kang (2014). Development of workspace conflict visualization system using 4D object of work schedule. Advanced Engineering Informatics, 28(1): 50–65
https://doi.org/10.1016/j.aei.2013.12.001
38 National Building Specification (2014). NBS national BIM report 2014. RIBA Enterprise Ltd.
39 M Niknam, S Karshenas (2015). Integrating distributed sources of information for construction cost estimating using Semantic Web and Semantic Web Service technologies. Automation in Construction, 57: 222–238
https://doi.org/10.1016/j.autcon.2015.04.003
40 M Niknam, S Karshenas (2016). Integrating BIM and project schedule information using semantic web technology. In: Construction Research Congress 2016. San Juan, 689–697
41 N F Noy, D L McGuinness (2001). Ontology development 101: A guide to creating your first ontology. Technical Report. Stanford, CA
42 M Oh, J Lee, S W Hong, Y Jeong (2015). Integrated system for BIM-based collaborative design. Automation in Construction, 58: 196–206
https://doi.org/10.1016/j.autcon.2015.07.015
43 P Pauwels, J Oraskari, L J McGibbney (2016). IFCtoRDF.
44 P Pauwels, S Zhang, Y C Lee (2017). Semantic web technologies in AEC industry: A literature overview. Automation in Construction, 73: 145–165
https://doi.org/10.1016/j.autcon.2016.10.003
45 J Porkka, K Kähkönen (2007). Software development approaches and challenges of 4D product models. In: 24th CIB W78 Conference—Bringing ITC Knowledge to Work, 85–90
46 M H Rasmussen, M Lefrançois, M Bonduel, C A Hviid, J Karlshø (2018). OPM: An ontology for describing properties that evolve over time. In: CEUR Workshop Proceedings, 2159: 23–33
47 G F Schneider (2017). Towards aligning domain ontologies with the building topology ontology. In: Proceedings of 5th Linked Data in Architecture and Construction (LDAC) Workshop
48 O B Sezer, E Dogdu, M Ozbayoglu, A Onal (2016). An extended IOT framework with semantics, big data, and analytics. In: IEEE International Conference on Big Data. Washington, DC, 1849–1856
49 G Steinberg (2012). Natural user interfaces. In: ACM SIGCHI Conference on Human Factors in Computing Systems, Austin, TX
50 N M Tomašević, M Č Batić, L M Blanes, M M Keane, S Vraneš (2015). Ontology-based facility data model for energy management. Advanced Engineering Informatics, 29(4): 971–984
https://doi.org/10.1016/j.aei.2015.09.003
51 Y Turkan, F Bosche, C T Haas, R Haas (2012). Automated progress tracking using 4D schedule and 3D sensing technologies. Automation in Construction, 22: 414–421
https://doi.org/10.1016/j.autcon.2011.10.003
52 M Venugopal, C M Eastman, J Teizer (2015). An ontology-based analysis of the industry foundation class schema for building information model exchanges. Advanced Engineering Informatics, 29(4): 940–957
https://doi.org/10.1016/j.aei.2015.09.006
53 A Wagner, M Bonduel, P Pauwels, R Uwe (2019). Relating geometry descriptions to its derivatives on the web. In: Proceedings of the 2019 European Conference for Computing in Construction. Chania, Crete, 304–313
54 W3C (2017). Time Ontology in OWL. Spatial Data on the Web Working Group. World Wide Web Consortium (W3C)
55 H L Wood, P Piroozfar, E R P Farr (2014). Understanding complexity in the AEC industry. In: Smith S D, Ahiaga-Dagbui D D, eds. Proceedings of 29th Annual Association of Researchers in Construction Management Conference. Reading, 2: 859–869
56 S Zhang, F Boukamp, J Teizer (2015). Ontology-based semantic modeling of construction safety knowledge: Towards automated safety planning for job hazard analysis (JHA). Automation in Construction, 52: 29–41
https://doi.org/10.1016/j.autcon.2015.02.005
[1] Kaile ZHOU, Zenghui ZHANG, Lu LIU, Shanlin YANG. Energy storage resources management: Planning, operation, and business model[J]. Front. Eng, 2022, 9(3): 373-391.
[2] Wei XIONG, Hongmiao FAN, Liang MA, Chen WANG. Challenges of human–machine collaboration in risky decision-making[J]. Front. Eng, 2022, 9(1): 89-103.
[3] Yanrong LI, Shizhe PENG, Yanting LI, Wei JIANG. A review of condition-based maintenance: Its prognostic and operational aspects[J]. Front. Eng, 2020, 7(3): 323-334.
[4] Fupei LI, Minglei YANG, Wenli DU, Xin DAI. Development and challenges of planning and scheduling for petroleum and petrochemical production[J]. Front. Eng, 2020, 7(3): 373-383.
[5] Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY. Big Data to support sustainable urban energy planning: The EvoEnergy project[J]. Front. Eng, 2020, 7(2): 287-300.
[6] Lizzette PÉREZ LESPIER, Suzanna LONG, Tom SHOBERG, Steven CORNS. A model for the evaluation of environmental impact indicators for a sustainable maritime transportation systems[J]. Front. Eng, 2019, 6(3): 368-383.
[7] Sujesh F. SUJAN, Arto KIVINIEMI, Steve W. JONES, Jacqueline M. WHEATHCROFT, Eilif HJELSETH. Common biases in client involved decision-making in the AEC industry[J]. Front. Eng, 2019, 6(2): 221-238.
[8] Zeshui XU, Shen ZHANG. An overview on the applications of the hesitant fuzzy sets in group decision-making: theory, support and methods[J]. Front. Eng, 2019, 6(2): 163-182.
[9] Gainanov Damir N., Mladenovic NENAD, Rasskazova V. A.. Maximum independent set in planning freight railway transportation[J]. Front. Eng, 2018, 5(4): 499-506.
[10] Braulio BRUNAUD, Maria Paz OCHOA, Ignacio E. GROSSMANN. Product decomposition strategy for optimization of supply chain planning[J]. Front. Eng, 2018, 5(4): 466-478.
[11] Muhammad Tariq SHAFIQ, Jane MATTHEWS, Steve LOCKLEY, Peter E.D. LOVE. Model server enabled management of collaborative changes in building information models[J]. Front. Eng, 2018, 5(3): 298-306.
[12] Gang FU, Pedro A. Castillo CASTILLO, Vladimir MAHALEC. Impact of crude distillation unit model accuracy on refinery production planning[J]. Front. Eng, 2018, 5(2): 195-201.
[13] Donald KENNEDY, Simon P. PHILBIN. The imperative need to develop guidelines to manage human versus machine intelligence[J]. Front. Eng, 2018, 5(2): 182-194.
[14] Yongling ZHU, Qianqian SHI, Qian LI, Zhimei YIN. Decision-making governance for the Hong Kong-Zhuhai-Macao Bridge in China[J]. Front. Eng, 2018, 5(1): 30-39.
[15] Braulio BRUNAUD, Ignacio E. GROSSMANN. Perspectives in multilevel decision-making in the process industry[J]. Front. Eng, 2017, 4(3): 256-270.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed