Building Blocks works seamlessly with the FLAC3D 6.0 extruder tool and new Model Pane. Building Blocks includes a library of model primates and users can also add and load their own building block sets.
In this tutorial we will go over meshing, from the creation of a 2D mesh and how to import it to MINEDW, to the inclusion of topography, layers, and pinch-outs to different areas of interest in the model.
In this example, a pile of earth is modeled overlying undulating ground. This tutorial demonstrates how a FLAC3D model mesh can be easily created using DXF geometries and the ZONE DENSIFY command. How to differentiate parts of the model into separate GROUPs using DXF geometries and the GEOMETRY-SPACE range logic is also demonstrated.
Lahars represent natural phenomena that can generate severe damage in densely populated urban areas. The evaluation of pressures generated by these mass flows on constructions (buildings, infrastructure…) is crucial for civil protection and assessment of physical vulnerability. The existing tools to model the spread of flows at large scale in densely populated urban areas remain inaccurate in the estimation of mechanical efforts. A discrete numerical model is developed for evaluating debris flow (DF) impact pressures at the local scale of one structure.
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.
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.