A model adaptation framework for mechanized tunneling: Subsoil uncertainty consideration from observation to construction

Abstract An accurate numerical simulation of a mechanized tunneling 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 the prognosis of geotechnical structures. To address this issue, a model adaptation framework is presented, which intends to minimize the involved uncertainties in the simulation of mechanized tunneling. 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 optimized measurement concept is developed employing sensitivity indices and other probabilistic tools including Bayesian updating methods. The concept of model adaptation is further developed by involving the field data during an intermediate boring phase in the reliability assessments of the other advancement phases. To do this, a Bayesian updating concept is combined with a Markov chain Monte-Carlo method to evaluate the updated reliability measures, considering the ground settlement as the limit state. Nevertheless, due to the infeasibility of performing an extensive geotechnical site characterization in a spacious project as tunneling, 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 different supervised learning algorithms, which assign the obtained characteristics to the predefined geological conditions.