NDLI: Critical geometric parameters of slope and their sensitivity analysis: a case study in Jilin, Northeast China

Content Provider	Springer Nature Link
Author	Chen, Mo Lu, Wenxi Xin, Xin Zhao, Haiqing Bao, Xinhua Jiang, Xue
Copyright Year	2016
Abstract	An excessively high or steep slope is one of the main causes of slope instability. This research examined the slope of the waste dump of a limestone mine in Wangqing County of northeast China’s Jilin Province. Support vector regression (SVR) and a radial basis function neural network (RBFNN) were used to train and test 76 groups of collected data, and the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) values indicated that the SVR model is better than the RBFNN model. Then, 12 × 17 combinations of angles and heights for the waste dump slope were designed, and used SVR to define β $_{ cr }$ and H $_{ cr }$ by predicting the factor of safety (FS) under different conditions. The results demonstrated that the SVR method can be used to calculate β $_{ cr }$ and H $_{ cr }$ accurately. Moreover, the effects of the parameters on FS and β $_{ cr }$ and H $_{ cr }$ were analyzed by sensitivity analysis. The sensitivity analysis results showed that the internal friction angle (φ) is the most effective parameter acting on β $_{ cr }$ and H $_{ cr }$, followed by the unit weight (γ) and cohesion (c). Furthermore, H $_{ cr }$ is more sensitive to γ, c, and φ than β $_{ cr }$. These results could be useful and convenient for engineers involved in slope design and management.
Starting Page	1
Ending Page	11
Page Count	11
File Format	PDF
ISSN	18666280
Journal	Environmental Earth Sciences
Volume Number	75
Issue Number	9
e-ISSN	18666299
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-05-05
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Slope stability Support vector regression Radial basis function neural network Critical slope angle Critical slope height Sensitivity analysis Geology Hydrology/Water Resources Geochemistry Environmental Science and Engineering Terrestrial Pollution Biogeosciences
Content Type	Text
Resource Type	Article
Subject	Global and Planetary Change Earth-Surface Processes Soil Science Environmental Chemistry Pollution Geology Water Science and Technology

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