5:15 PM - 7:15 PM
[HDS07-P01] Tracking Landslide Motion: A Kinematic Approach Using Velocity-Acceleration Phase Plane
★Invited Papers
Keywords:Landslides, Acceleration, Phase plane, Kinematic analysis
Landslide displacement is a critical factor in establishing early warning systems and evaluating the effectiveness of mitigation measures. Conventionally, landslide activity has been assessed based on time-series displacement data, which provide limited insights into landslide dynamics. This study proposes a kinematic approach to visualize and evaluate landslide movement by analyzing the relationship between velocity and acceleration through a phase plane representation.
Three landslide monitoring sites were analyzed: (A) Shirishizu and (B) Senposhi in Hokkaido Prefecture, and (C) Busuno in Niigata Prefecture. Sites (A) and (B) experienced rapid movements within 24 hours, with high-precision data (+/- 0.1 mm, 5-minute intervals), while site (C) exhibited slow movements over several years with lower data precision (+/- 0.3 mm, daily intervals). Velocity and acceleration were derived from displacement data and plotted in a velocity-acceleration phase plane to track motion trajectories over time.
The results indicate that, for all sites, the trajectories exhibit a clockwise pattern relative to the zero-acceleration axis, reflecting the transition from movement initiation to cessation. In the phase plane, positive acceleration phases signify a state of increasing movement where driving forces exceed resisting forces, indicating high landslide risk independent of velocity. Conversely, negative acceleration phases represent decelerating motion, suggesting a reduced risk. When trajectories remain on the zero-acceleration axis, landslides move at a constant velocity, indicating that the driving and resisting forces are in equilibrium. This equilibrium represents a quasi-steady state.
The quality of observational data significantly influenced the interpretation of the motion characteristics. Higher-precision and short-interval data from sites (A) and (B) provided smooth and clear trajectories, facilitating detailed assessments of landslide motion. In contrast, the lower-precision and long-interval data from site (C) resulted in complex trajectories with abrupt changes, yet offered potential for long-term anomaly detection in landslide risk management.
This work was supported by JSPS KAKENHI Grant Numbers JP20H01984,JP22H01309,JP23K22580.
Three landslide monitoring sites were analyzed: (A) Shirishizu and (B) Senposhi in Hokkaido Prefecture, and (C) Busuno in Niigata Prefecture. Sites (A) and (B) experienced rapid movements within 24 hours, with high-precision data (+/- 0.1 mm, 5-minute intervals), while site (C) exhibited slow movements over several years with lower data precision (+/- 0.3 mm, daily intervals). Velocity and acceleration were derived from displacement data and plotted in a velocity-acceleration phase plane to track motion trajectories over time.
The results indicate that, for all sites, the trajectories exhibit a clockwise pattern relative to the zero-acceleration axis, reflecting the transition from movement initiation to cessation. In the phase plane, positive acceleration phases signify a state of increasing movement where driving forces exceed resisting forces, indicating high landslide risk independent of velocity. Conversely, negative acceleration phases represent decelerating motion, suggesting a reduced risk. When trajectories remain on the zero-acceleration axis, landslides move at a constant velocity, indicating that the driving and resisting forces are in equilibrium. This equilibrium represents a quasi-steady state.
The quality of observational data significantly influenced the interpretation of the motion characteristics. Higher-precision and short-interval data from sites (A) and (B) provided smooth and clear trajectories, facilitating detailed assessments of landslide motion. In contrast, the lower-precision and long-interval data from site (C) resulted in complex trajectories with abrupt changes, yet offered potential for long-term anomaly detection in landslide risk management.
This work was supported by JSPS KAKENHI Grant Numbers JP20H01984,JP22H01309,JP23K22580.