A pressure pulse is being applied to the tunnel boundary with a frequency of 4 Hz over tens of milliseconds. Quiet (i.e., viscous) boundaries have been applied to all but the top of the model, which remains a free surface.
In this tutorial we will demonstrate how to map a random point cloud with pore pressure values onto the grid points of a FLAC3D model using Python.
Field monitoring programs (e.g., convergence measurements and stress measurements in the support system) play an important role in following the response of the ground and of the support system during and after excavation. They contribute to the adaptation of the excavation and support installation method and the prediction of the long-term behavior. In the context of the Lyon–Turin link project, an access gallery (SMP2) was excavated between 2003 and 2010, and a survey gallery (SMP4) has been excavated since 2017.
This work presents a hybrid modeling approach to efficiently estimate and optimize rock movement during blasting. A small-scale continuum model simulates early-stage, near-field blasting physics and generates synthetic data to train a machine learning (ML) model. Key parameters such as expanded hole diameter, burden velocity, and gas pressure are obtained through the ML model, which then inform a discontinuum model to predict far-field muckpile formation. The approach captures essential blast physics while significantly accelerating blast design optimization.
Based on the concept of the representative elementary volume (REV) and the synthetic rock mass (SRM) modeling technique, a DFN–DEM multi-scale modeling approach is proposed for modeling excavation responses in jointed rock masses. Based on the DFN models of various scales, equivalent rock mass properties are obtained using 3DEC SRM models. A tunnel excavation simulation using data from the Äspö TAS08 tunnel is conducted to demonstrate the applicability of the proposed multi-scale modeling approach.