Urban underground space (UUS) has developed rapidly in recent years, however, a number of problems have been revealed.
There is a certain lag in the current planning and control of underground space.
In particular, as underground space is dug out of geological bodies for construction,
the accompanying geological disturbance to the surrounding urban environment cannot be ignored,
and the impact of geological bodies on underground space must be considered in advance of planning truncation.
In this study we intend to propose a method for building the 3D geological model
from multi-source data using Kriging interpolation algorithm combined with the Parallel truncated grid.
A mapping algorithm from the 3D geological model to the numerical calculation is established
based on the idea of the numerical manifold method (NMM),
which allows complex soil properties to serve the engineering computational analysis.
We will present evaluation algorithm for geological disturbance and
idiscusse measures for eological disturbance applied to UUS planning.
The method proposed in this study will help to frontload engineering thinking in the planning phase,
to characterize the impact of geological properties on underground space development
and to provide a fast algorithm that can be applied to specific projects.
✔️ Write project application.
✔️ Develop the 3D geological modelling (3DGEO) software incorporating
heterogeneous data from multiple sources.
✔️ Propose the model mapping algorithm (3DGEO-to-FEM).
✍️ Propose evaluation algorithm for geological disturbance.
✍️ Discusses measures for geological disturbance applied to UUS planning.
Essentially, unlike above ground buildings,
underground space is built by reducing materials.
We can not see all the soil before excavation,
its complex spatial distribution and changeable properties are often the key factors
restricting the foundation pit.
Modeling is an inherent way to understand and translate difficulties.
In other words, whether it's using math, digital tools,
or creating a physical object, virtually everyone of us can model.
But multi-source data-driven modelling is not simply a linear overlay;
how the data is selected and analysed is still a form of intelligence ✍️.