Displacement Trends of Slow-moving Landslides: Classification and Forecasting

A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one or several existing shear surfaces. The framework is developed based on a thorough analysis of the scientific literature and with reference to significant reported case studies for which a consistent dataset of continuous displacement measurements is available. Three distinct trends of movement are defined to characterize the kinematic behavior of the active stages of slow-moving landslides in a velocity-time plot: a linear trend-type I, which is appropriate for stationary phenomena; a convex shaped trend-type II, which is associated with rapid increases in pore water pressure due to rainfall, followed by a slow decrease in the groundwater level with time; and a concave shaped trend-type III, which denotes a non-stationary process related to the presence of new boundary conditions such as those associated with the development of a newly formed local slip surface that connects with the main existing slip surface. Within the proposed framework, a model is developed to forecast future displacements for active stages of trend-type II based on displacement measurements at the beginning of the stage. The proposed model is validated by application to two case studies.

Long-term kinematics and mechanism of a deep-seated slow-moving debris slide near Wudongde hydropower station in Southwest China

Long-term kinematic research of slow- moving debris slide is rare despite of the widespread global distribution of this kind. This paper presents a study of the kinematics and mechanism of the Jinpingzi debris slide located on the Jinsha river bank in southwest China. This debris slide is known to have a volume of 27×106 ms in active state for at least one century. Field survey and geotechnical investigation were carried out to define the structure of the landslide. The physical and mechanical properties of the landslide materials were obtained by in-situ and laboratory tests. Additionally, surface and subsurface displacements, as well as groundwater level fluctuations, were monitored since 2005. Movement features, especially the response of the landslide movement to rainfall, were analysed. Relationships between resisting forces and driving forces were analysed by using the limit equilibrium method assuming rigid-plastic frictional slip. The results confirmed a viscous comoonent in the long-term continuous movement resulting in the quasioverconsolidated state of the slip zone with higher strength parameters than some other types of slowmoving landslides. Both surface and subsurface displacements showed an advancing pattern by the straight outwardly inclined （rather than gently or reversely inclined） slip zone, which resulted in low resistance to the entire sliding mass. The average surface displacement rate from 2005 to 2016 was estimated to be 0.19-0.87 mm/d. Basal sliding on the silty clay seam accounted for most of the deformation with different degrees of internal deformation in different parts. Rainfall was the predominant factor affecting the kinematics of Jinpingzi landslide while the role of groundwater level, though positive, was not significant. The response of the groundwater level to rainfall infiltration was not apparent. Unlike some shallow slow-moving earth flows or mudslides, whose behaviors are directly related to the phreatic groundwater level, the mechanism for Jinpingzi landslide kinematics is more likely related to the changing weight of the sliding mass and the downslope seepage pressure in the shallow soil mass resulting from rainfall events.

The behavioral characteristics of slow-mover in urban waterfront space

The waterfront space is a specific and perfect open space for people to experience the city.Daily entertainment,leisure,shopping,sports and other activities can be carried out in the waterfront slow-motility space.There are three types of slow-mover in urban waterfront space namely walking,stop or stay,and riding.Analysis of their behavioral characteristics and the difference of different people can help to clarify the design requirements and produce a waterfront space that will better meets people’s functional needs.

Traffic Organization of Urban Waterfront

Both of slow-moving traffic and motor traffic have an impact on waterfront development. Waterfront traffic has to based on analysis of local transport and land- scaping, road analysis vertically, horizontally and cross-section, as well as road space, structuring and the beauty of art. The target is to build waterfront traffic full of city characters.

Towards hydrometeorological thresholds of reservoir-induced landslide from subsurface strain observations

Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displacements have been widely investigated.However,the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide acceleration.In this paper,we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir(TGR)region,China,spanning a whole hydrologic year since February 2021.The spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers,indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation perspective.Considering the time lag effect,we reexamined and quantified potential controls of accelerated movements using a data-driven approach,which reveals immediate response of landslide deformation to extreme rainfall with a zero-day shift.To identify critical hydrometeorological rules in accelerated movements,accounting for the dual effect of rainfall and reservoir water level variations,we thus construct a landslide prediction model that relies upon the boosting decision tree(BDT)algorithm using a dataset comprising daily rainfall,rainfall intensity,reservoir water level,water level fluctuations,and slip zone strain time series.The results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels(i.e.,<169.700 m)and large-amount and high-intensity rainfalls(i.e.,daily rainfall>57.9 mm and rainfall intensity>24.4 mm/h).Moreover,this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring dataset.Standing on the shoulder of this landslide case,our study informs a practical and reliable pathway for georisk early warning based on subsurface observations,particularly in the context of enhanced extreme weather events.