Assessing implicit knowledge in BIM models with machine learning

Conference Contribution

Krijnen, T.F. & Tamke, M. (2015). Assessing implicit knowledge in BIM models with machine learning. In M. Tamke, B. Faircloth, M. Ramsgaard Thomsen, F. Schreuder & C. Gengnagel (Eds.), Modelling behaviour. Design Modelling Symposium (pp. 397-406). Springer International Publishing.

Read more: DOI      Medialink/Full text

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.