Abstract
The promise, which comes along with Building Information Models, is that they are information rich, machine readable and represent the insights of multiple building disciplines within single or linked models. However, this knowledge has to be stated explicitly in order to be understood. Trained architects and engineers are able to deduce non-explicitly explicitly stated information, which is often the core of the transported architectural information. This paper investigates how machine learning approaches allow a computational system to deduce implicit knowledge from a set of BIM models.
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References
Beetz J et al (2014) D3.3 semantic digital archive prototype. http://duraark.eu/wp-content/uploads/2014/08/DURAARK_D3_3_3.pdf. Accessed May 2015
buildingSMART (2014) buildingSMART. http://www.buildingsmart.org/. Accessed May 2015
Corney J et al (2002) Coarse filters for shape matching. IEEE Comput Graph Appl 22(3):65–74
Daum S, Borrmann A (2014) Processing of topological BIM queries using boundary representation based methods. Adv Eng Inform 28(4):272–286
Davis D (2014) Quantitatively Analysing Parametric Models. Int J Archit Comput 12(3):307–320
DURAARK (2015) DURAARK Project. http://duraark.eu/. Accessed May 2015
Geospatial Media and Communications (2014) GeoBim. http://geo-bim.org/. Accessed May 2015
Krijnen T (2015a) DURAARK/pyIfcExtract. https://github.com/DURAARK/pyIfcExtract. Accessed May 2015
Krijnen T (2015b) IfcOpenShell. https://ifcopenshell.org. Accessed May 2015
Mazairac W, Beetz J (2013) BIMQL—An open query language for building information models. Adv Eng Inform 27(4):444–456
Mitchell T (1997) Machine learning. McGraw Hill, New York City
NIBS (2013) Buildingsmart alliance common building information model files and tools—national institute of building sciences. http://www.pythonocc.org/. Accessed May 2015
Paviot T (2014) Pythonocc, 3D CAD/CAE/PLM development framework for the python programming language. https://github.com/DURAARK/pyIfcExtract. Accessed May 2015
Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Schaul T et al (2010) PyBrain. J Mach Learn Res 11:743–746
Solibri (2015) Solibri model checker. http://www.solibri.com/products/solibri-model-checker/. Accessed May 2015
Stasiuk D, Thomsen MR (2014) Learning to be a vault—implementing learning strategies for design exploration in inter-scalar systems. Newcastle upon Tyne, England, pp 381–390
Tamke M et al (2014a) D7.7.1—Current state of 3D object processing in architectural research and practice. http://duraark.eu/wp-content/uploads/2014/06/duraark_d7.7.1.pdf
Tamke M et al (2014b) Building information deduced—state and potentials for information query in building information modelling. Newcastle upon Tyne, England, pp 375–384
Zhang C, Beetz J, Weise M (2014) Interoperable validation for IFC building models using open standards. ITcon vol 20, Special Issue ECPPM 2014—10th European conference on product and process modelling, pp 24–39
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Krijnen, T., Tamke, M. (2015). Assessing Implicit Knowledge in BIM Models with Machine Learning. In: Thomsen, M., Tamke, M., Gengnagel, C., Faircloth, B., Scheurer, F. (eds) Modelling Behaviour. Springer, Cham. https://doi.org/10.1007/978-3-319-24208-8_33
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DOI: https://doi.org/10.1007/978-3-319-24208-8_33
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