Retrieval Model of Slope Stability Evaluation System Based on Cluster Analysis and Genetic Algorithm

2010 ◽  
pp. 1-9
Author(s):  
Li Mei ◽  
Xia Yuan-you
Keyword(s):  
Genetic Algorithm ◽  
Cluster Analysis ◽  
Slope Stability ◽  
Evaluation System ◽  
Stability Evaluation ◽  
Retrieval Model

scholarly journals Development of web-based slope stability evaluation system (SSE-web)

2005 ◽  
Vol 14 (0) ◽  
pp. 239-248
Author(s):  
Hirohito Kojima ◽  
Shigeyuki Obayashi ◽  
Takehiro Ichikawa ◽  
Yuki Mukai
Keyword(s):  
Slope Stability ◽  
Evaluation System ◽  
Stability Evaluation ◽  
Web Based

scholarly journals Development of web-based slope stability evaluation system (SSE-web)

2004 ◽  
Vol 13 (0) ◽  
pp. 11-20
Author(s):  
Hirohito Kojima ◽  
Shigeyuki Obayashi ◽  
Takehiro Ichikawa ◽  
Yuki Mukai
Keyword(s):  
Slope Stability ◽  
Evaluation System ◽  
Stability Evaluation ◽  
Web Based

Inferring hillslope response under seismic loading and rainfall: A case study from the SE Carpathian, Romania

2021 ◽  
Author(s):  
Vipin Kumar ◽  
Léna Cauchie ◽  
Anne-Sophie Mreyen ◽  
Philippe Cerfontaine ◽  
Mihai Micu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Slope Stability ◽  
Surface Runoff ◽  
Dynamic Condition ◽  
Seismic Loading ◽  
Seismic Stability ◽  
Seismic Load ◽  
Stability Evaluation ◽  
Rainfall Events ◽  
Year 2000

<p>Seismic stability evaluation plays a crucial role in landslide disaster risk reduction. Related modeling also has to consider the potential influences of the rainfall on the hillslopes. This study aims at understanding the relative influence of the seismic loading and extreme cumulative rainfall on a massive active landslide in the seismically active Vrancea-Buzau region of the Romanian Carpathians (45° 30' 23" N, 26° 25' 05" E). This region has been subjected to more than 700 earthquakes (M>4) events with the highest magnitude of 7.2 (M<sub>w</sub>) during the year 1960-2019. Rainfall data of the year 2000-2019 revealed the occurrence of relatively intense rainfall events, especially during the last ten years. The landslide has an aerial dimension of ~9.1 million m². It hosts the small village of Varlaam at the toe along the Bisca River. The slope (with an average gradient of 15-20°) is covered by shrubs and scattered trees near its borders and is relatively barren in the central part. Shales with some intercalated sandstone layers belonging to the Miocene thrust belt constitute the rocks of the slope.   </p><p>A first survey involving the multi-station array and related Horizontal-to-Vertical noise Spectral Ratio (HVSR) measurements was completed in summer 2019. The findings of the HVSR were processed using the inversion process to infer the shear wave velocity distribution with depth and to detect the sliding surface of the landslide. These velocities were further used to estimate the geotechnical properties of the subsurface using the empirical equations. The HVSR based depth profiles and the Unmanned Air Vehicle based topographic information were used to take four 2D slope sections. These sections were considered for 2D discrete element modeling based stability evaluation under static and dynamic condition along with sensitivity analysis. Static simulation was used to determine the Factor of Safety (FS) using the shear strength reduction approach. Ricker wavelet was used as input seismic load in the dynamic simulation. Potential run-out and flow characteristics of the slope material were explored using the Voellmy rheology based RAMMS software. The relationship between rainfall, surface runoff, and soil moisture was also explored to understand the hydrogeological influence on slope stability.</p><p>Though the slope reveals meta-stability (1.0<FS<2.0) condition under static loading, displacement in the soil reaches up to 1.5 m that further increases to 2.8 m under dynamic loading. According to the topographic characteristics of the slope and to the presence of landslide material or intact bedrock near the surface, acceleration along the slope reaches a Peak Ground Acceleration in the range of 0.6 to 1.3g. Eight extreme rainfall events (>50mm/24 hours) during the year 2000-2019 are noted to temporally coincide with enhanced surface runoff and increased soil moisture in the region. Debris flow runout modeling indicated that the slope material may attain a maximum flow height and flow velocity of 13±0.8 m and 5±0.5 m/sec, respectively, along the river channel.</p><p><strong>Keywords: </strong>Landslide;<strong> </strong>Earthquake; Slope stability; Runout; SE Carpathian</p>

scholarly journals Identification of Novel Classes for Patients With Lupus Nephritis Using Two-step Cluster Model

2021 ◽  
Author(s):  
Ting Huang ◽  
Shasha Xie ◽  
Liqing Ding ◽  
Hui Luo
Keyword(s):  
Cluster Analysis ◽  
Evaluation System ◽  
Demographic Data ◽  
White Blood Cells ◽  
Damage Index ◽  
Cross Sectional Study ◽  
Laboratory Findings ◽  
Cross Sectional ◽  
Mild Disease ◽  
Cluster 2

Abstract Objectives: To identify and reclassify the patients in the LN cohort, and to further analyze the prominent clinical features and clinical significance of each cluster of patients.Methods: This is a cross-sectional study of a cohort of 635 LN patients from the Rheumatology Department of Xiangya Hospital of Central South University. Demographic data, laboratory findings and clinical evaluation system include physician’s global assessment and the SLICC/ACR Damage Index were collected. Using two-step cluster analysis, patients with similar clinical property were identified and compared.Results: Among the 635 LN patients, 599 patients (94.3%) were female. The mean age of the patients were 33.8 ± 10.4 years. Three subgroups were identified by two-step cluster analysis. Cluster 1 included 130 (20.5%) patients, Cluster 2 included 132(20.8%) patients and Cluster 3 included 373 (58.7%) patients. Cluster 3 was the largest group of mild disease activity, patients in this cluster had lower white blood cells, neutrophils, lymphocytes and mean SDI scores compared to those in the other two clusters. Cluster 1 was the smallest group of severe damage, patients in this cluster had multiple positive auto-antibodies, higher SDI scores and lower complement level. Patients of cluster 2 had the highest levels of granulocytes, but the results of other laboratory tests were roughly between the cluster 1 and cluster 3.Conclusions: This study reclassified three groups of LN patients in a large cohort. Our research shows that the multiple positive ANA antibody may be related to the high SDI score of LN patients. Clinicians can identify patients at different stages through cluster analysis to better implement prognosis.

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