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

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.

Reinforcement learning for the face support pressure of tunnel boring machines

“…Some promising research domains for machine learning in tunnelling are the geological prognosis ahead of the face, the interpretation of monitoring results, automation and maintenance [32]. At present, however, research appears to be focussed on the following topics: prediction of TBM operational parameters [34,[39][40][41][42][43][44][45][46], penetration rate [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], porewater pressure [64], ground settlement [65][66][67], disc cutter replacement [68][69][70], jamming risk [71,72] and geological classification [73][74][75][76]). Few authors estimated the face support pressure of TBMs with machine learning [35,52].…”