2. Table of contents
Introduction
Cassas Landslide
Input data
Velocity data prediction
Past displacement prediction
Mitigative measures
Other applications
End
3. Introduction
"AI LANDSLIDE" analyses
monitoring data and allows
reliable predictions of landslide
velocity based on past inclino-
metric data and rainfall.
Civil Protection Alerts can be
triggered with more reliability,
thus avoiding many costlycostly
crisis planning errorscrisis planning errors.
7. The learning phase encompassed 40 points.
A.I. LANDSLIDE can already effectively
predict velocities after a rather short learning
phase!
Prediction: Velocity data 98-02
12. Displacement
Real displacement Nov-98 to Ago-02
(46 months) Inclinometer n°3 ≈ 20.5 cm
≈ 20.5 cm
Calculated displacement Jan-91 to Nov-98
(94 months) Inclinometer n°3 ≈ 21.1 cm
≈ 21.1cm
13. According to the « AI Landslide » model the need of
implementing mitigative measures has clearly emerged. The
main scope being the depression of the ground water table
Three alternatives have been studied with a risk management
approach:
Deep drainage by vertical shafts and submerged
pumps
600 m long tunnel in stable ground with ascending
drainage boreholes
150 m long 3 x 3 m drainage tunnel in the
sliding mass with subhorizontal drains
« AI Landslide » engineering
application
14. The system « AI Landslide » can be custom tailored to study
other events and phenomena than landslides.
Practically, any activity in which the driving parameters can be
measured and identified can be studied with « AI Landslide ».
However the model has to be custom tailored for each
phenomenon:
- Levels of water in a river or creek
- Deformations and settlements
- Tunnel’s convergence
- Rock mass movements
- Dispersion of contaminants
- Etc.
Other « AI Landslide » applications