An accurate numerical simulation of a mechanised tunnelling process in an urban area must consider the complex interactions between subsoil, the tunnel boring machine and ground constructions. Of course, due to the natural origin of the geomaterials, their characteristics deal with randomness. The considerably high level of associated uncertainty inherent in geomaterials may lead to notable deviations in geotechnical structures' prognosis. To address this issue, a model adaptation framework is presented in subproject C2, which intends to minimise the involved uncertainties in mechanised tunnelling simulation. In this framework, first parameter identification techniques are developed to reach an adequate soil model for numerical simulations based on measurements. Accordingly, the concept for an optimal measurement campaign is introduced. The optimum observation design is supposed to identify a sensor arrangement which provides the least uncertainty in the parameter identification process. The concept of model adaptation is further developed by involving the field data during an intermediate boring phase in the other advancement phases' reliability assessments. Nevertheless, due to the infeasibility of performing an extensive geotechnical site characterisation in a spacious project as tunnelling, some geological alternation might be overlooked. Here, the adaptation framework proposes a supervised machine learning methodology to predict the geological changes ahead of the TBM. The classification is performed based on supervised learning algorithms that assign the obtained characteristics to the predefined geological conditions.
System and Parameter Identification Methods for Ground Models in Mechanized Tunneling
Sub-project, SFB 837
Chair of Computing in Engineering
Prof. Dr.-Ing. Markus König
Bldg. IC, Room 6-59