Assessing Implicit Knowledge in BIM Models with Machine Learning

  • Thomas Krijnen
  • Martin Tamke
Chapter

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Thomas Krijnen
    • 1
  • Martin Tamke
    • 2
  1. 1.Department of the Built EnvironmentEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Centre for IT and ArchitectureRoyal Danish Academy of Fine Art, School of ArchitectureCopenhagenDenmark

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