Automated geological model updates during TBM operation – An approach based on probabilistic machine learning concepts

Geomechanics and Tunnelling, vol. 15, pp. 635–641

Abstract

Abstract Geological models are commonly used to predict the position of relevant geological features, such as rock types or faults in the subsurface. These models can contain significant uncertainties, as the geological input parameters are often not perfectly known. Predictions of geological features, for example, on the level of a tunnel during an excavation process, are therefore uncertain. This work shows how these uncertainties can be estimated using probabilistic concepts. Furthermore, an approach is presented to automatically adjust the geological model predictions using measurements of the tunnel boring machine (TBM) operation. To this aim, the geological forward model is combined with a measurement model and both are integrated in a probabilistic machine learning framework. This integration enables a Bayesian inference process using computational methods, enabling an update of the parameters of the geological and measurement models. Based on the inferred parameter distributions, the model predictions on the tunnel level are subsequently updated. The application of the concept in a simple schematic application shows that such a combination can accurately and precisely predict features ahead of the operation within the limits of the model capabilities. In future work, the methods need to be tested with a real case study to evaluate the accuracy of predictions using real‐world TBM data and more complex geological models. The framework presented here could directly be extended to this purpose.

Authors 4

1. Florian Wellmann corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Computational Geoscience, Geothermics and Reservoir Geophyiscs Aachen Germany

   RWTH Aachen University, Computational Geoscience, Geothermics and Reservoir Geophyiscs, Aachen, Germany

2. Florian Amann Aachen Chair of Engineering Geology

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Chair of Engineering Geology Aachen Germany

   RWTH Aachen University, Chair of Engineering Geology, Aachen, Germany

3. Miguel de la Varga
   Affiliation as printed

   Terranigma Solutions GmbH Aachen Germany

   Terranigma Solutions GmbH, Aachen, Germany

4. Nils Chudalla Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Computational Geoscience, Geothermics and Reservoir Geophyiscs Aachen Germany

   RWTH Aachen University, Computational Geoscience, Geothermics and Reservoir Geophyiscs, Aachen, Germany