An acoustic emission landslide early warning system for communities in low-income and middle-income countries

This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/Early warning systems for slope instability are needed to alert users of accelerating slope deformation behaviour, enable evacuation of vulnerable people, and conduct timely repair and maintenance of critical infrastructure. Communities exposed to landslide risk in low- and middle-income countries seldom currently instrument and monitor slopes to provide a warning of instability because existing techniques are complex and prohibitively expensive. Research and field trials have demonstrated conclusively that acoustic emission (AE) monitoring can be an effective approach to detect accelerating slope movements and to subsequently communicate warnings to users. The objective of this study was to develop and assess a simple, robust, low-cost AE monitoring system to warn of incipient landslides, which can be widely deployed and operated by communities globally to help protect vulnerable people. This paper describes a novel AE measurement sensor that has been designed and developed with the cost constrained to a few hundred dollars (US). Results are presented from physical model experiments that demonstrate performance of the AE system in measuring accelerating deformation behaviour, with quantifiable relationships between AE and displacement rates. Exceedance of a pre-determined trigger level of AE can be used to communicate an alarm to users in order to alert them of a slope failure. Use of this EWS approach by communities worldwide would reduce the number of fatalities caused by landslides

New, simplified and improved interpretation of the Vaiont landslide mechanics

Both the occurrence and behaviour of the Vaiont landslide have not been satisfactorily explained previously because of difficulties arising from the assumption that the failure surface was ‘chair’ shaped. It is now known that there was no ‘chair’, which means that the 1963 landslide could not have been a reactivated ancient landslide because the residual strength of the clay interbeds would have been insufficient for stability prior to 1963. Furthermore, the moderately translational geometry reduces the influence of reservoir-induced groundwater and hence of submergence. Standard stability analyses now show that prior to 1960, the average shear strength must have significantly exceeded the peak shear strength of the clay interbeds known to have formed the majority of the failure surface. Three-dimensional stability analyses confirm these results and show that at the time of the first significant movements in 1960, the rising reservoir level had a negligible effect on the Factor of Safety. According to these results, the Vaiont landslide was most likely initiated by pore water pressures associated with transient rainfall-induced ‘perched’ groundwater above the clay layers, in combination with a smaller than hitherto assumed effect of reservoir impounding, then developed by brittle crack propagation within the clay beds, thus displaying progressive failure. Further, very heavy rainfall accelerated the process, possibly due to reservoir-induced groundwater impeding drainage of the rainwater, until the limestone beds at the northeast margin failed. With the shear strength suddenly reduced to residual throughout, the entire mass was released and was able to accelerate as observed

Identification of debris flow initiation zones using topographic model and airborne laser scanning data

Empirical multivariate predictive models represent an important tool to estimate debris flow initiation areas. Most of the approaches used in modelling debris flows propagation and deposit phases required identifying release (starting point) area or source area. Initiation areas offer a good overview to point out where field investigation should be conducted to establish a detailed hazard map. These zones, usually, are arbitrarily chosen which affect the model outputs; hence, there is a need to have accurate and automated means of identifying the release area. In addition to this, the resolution of the terrain dataset also affects the results of the detection of source areas. In this study, airborne laser scanning (ALS) data was used because of its robustness in providing detailed terrain attributes at high resolution. Primary and secondary conditioning parameters were derived from digital elevation model (DEM) as input into the modelling process. Three models were executed at different spatial resolution scales: 5, 10 and 15 m, respectively. MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. A statistics validation was calculated to estimate the optimal pixel size, 1200 randomly sample data were generated from existing inventory data. Debris flows and no-debris flows were categorized, and the transform to continuous integer (1 and 0), respectively. To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). The three most influencing topographic parameters identified are topographic roughness index (TRI), slope angle, and specific catchment area (SCA) with the percentage values of participation in the model of 100, 93, and 86%, respectively. The chosen function appeared to describe the analysed correlation sufficiently well. Consequently, three stages of optimization were made to determine the optimized source areas is possible with 10 m pixel size, 200 maximum basis functions and 3 maximum interactions, resulting into 82% ROC train and 80% test, GCV 0.189 and 85% correlation coefficient. The result will be of great contribution to the advancement of a broad understanding of the dynamics of debris flows hazard and mitigations at regional level which; that is resourceful for comprehensive slope management for safe urban planning decision-making process and debris flow disaster management

Identification of debris flow initiation zones using topographic model and airborne laser scanning data

Empirical multivariate predictive models represent an important tool to estimate debris flow initiation areas. Most of the approaches used in modelling debris flows propagation and deposit phases required identifying release (starting point) area or source area. Initiation areas offer a good overview to point out where field investigation should be conducted to establish a detailed hazard map. These zones, usually, are arbitrarily chosen which affect the model outputs; hence, there is a need to have accurate and automated means of identifying the release area. In addition to this, the resolution of the terrain dataset also affects the results of the detection of source areas. In this study, airborne laser scanning (ALS) data was used because of its robustness in providing detailed terrain attributes at high resolution. Primary and secondary conditioning parameters were derived from digital elevation model (DEM) as input into the modelling process. Three models were executed at different spatial resolution scales: 5, 10 and 15 m, respectively. MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. A statistics validation was calculated to estimate the optimal pixel size, 1200 randomly sample data were generated from existing inventory data. Debris flows and no-debris flows were categorized, and the transform to continuous integer (1 and 0), respectively. To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). The three most influencing topographic parameters identified are topographic roughness index (TRI), slope angle, and specific catchment area (SCA) with the percentage values of participation in the model of 100, 93, and 86%, respectively. The chosen function appeared to describe the analysed correlation sufficiently well. Consequently, three stages of optimization were made to determine the optimized source areas is possible with 10 m pixel size, 200 maximum basis functions and 3 maximum interactions, resulting into 82% ROC train and 80% test, GCV 0.189 and 85% correlation coefficient. The result will be of great contribution to the advancement of a broad understanding of the dynamics of debris flows hazard and mitigations at regional level which; that is resourceful for comprehensive slope management for safe urban planning decision-making process and debris flow disaster management

Landslides Triggered by the MW 7.8 14 November 2016 Kaikoura Earthquake, New Zealand

The MW 7.8 14 November 2016 Kaikoura earthquake generated more than 10000 landslides over a total area of about 10000 km2, with the majority concentrated in a smaller area of about 3600 km2. The largest landslide triggered by the earthquake had an approximate volume of 20 (±2) M m3, with a runout distance of about 2.7 km, forming a dam on the Hapuku River. In this paper, we present version 1.0 of the landslide inventory we have created for this event. We use the inventory presented in this paper to identify and discuss some of the controls on the spatial distribution of landslides triggered by the Kaikoura earthquake. Our main findings are (1) the number of medium to large landslides (source area ≥10000 m2) triggered by the Kaikoura earthquake is smaller than for similar sized landslides triggered by similar magnitude earthquakes in New Zealand; (2) seven of the largest eight landslides (from 5 to 20 x 106 m3) occurred on faults that ruptured to the surface during the earthquake; (3) the average landslide density within 200 m of a mapped surface fault rupture is three times that at a distance of 2500 m or more from a mapped surface fault rupture ; (4) the “distance to fault” predictor variable, when used as a proxy for ground-motion intensity, and when combined with slope angle, geology and elevation variables, has more power in predicting landslide probability than the modelled peak ground acceleration or peak ground velocity; and (5) for the same slope angles, the coastal slopes have landslide point densities that are an order of magnitude greater than those in similar materials on the inland slopes, but their source areas are significantly smaller

Making a clinical device from a laboratory prototype: from Prosper to Prosper OR

International audienceDesign of a system for robotized prostate brachytherapy: the paper presents a new version of the Prosper robot and justifies modifications related to experimental work with the first prototype and to safety and regulation for clinical use

Making a clinical device from a laboratory prototype: from Prosper to Prosper OR

International audienceDesign of a system for robotized prostate brachytherapy: the paper presents a new version of the Prosper robot and justifies modifications related to experimental work with the first prototype and to safety and regulation for clinical use