Perspectives of 3D Probabilistic Subsoil Modeling for BIM

Abstract

Building information modeling (BIM) in the planning and construction of infrastructure projects, such as roads, tunnels, and excavations, requires the generation of comprehensive 3D subsoil models that encompass relevant geological and geotechnical information. Presently, this process relies on the deterministic interpolation of discrete data points obtained from exploratory boreholes and soundings, resulting in a single deterministic prediction. Commonly employed interpolation methods for this purpose include radial basis function and kriging. This contribution introduces probabilistic methods for quantifying prediction uncertainty. The proposed modeling approach is illustrated using simple examples, demonstrating how to use sequential Gaussian and Indicator Simulation techniques to model sedimentary processes such as erosion and lenticular bedding. Subsequently, a site in Munich serves as a case study. The widely used industry foundation classes (IFC) schema allows the integration of the model into the BIM environment. A mapping procedure allows transferring voxel models to the IFC schema. This article discusses the significance of incorporating uncertainty quantification into subsoil modeling and shows its integration into the BIM framework. The proposed approach and its efficient integration with evolving BIM standards and methodologies provides valuable insights for the planning and construction of infrastructure projects.