Study on the Soil Slope Stability Based on the Support Vector Machine

2011 ◽  
Vol 291-294 ◽  
pp. 2746-2749
Author(s):  
Yun Fei Wang ◽  
Li Ping Wang ◽  
Fu Ping Zhong ◽  
Huai Bao Chu
Keyword(s):  
Complex System ◽  
Support Vector Machine ◽  
Slope Stability ◽  
Easy Access ◽  
Support Vector ◽  
Soil Slope ◽  
Multiple Factors ◽  
Project Construction ◽  
The Stability ◽  
Important Basis

The stability of the slope is a complex system affected by many factors, with the characteristics of randomness and fuzziness. In the paper established the model of the support vector machine, which make use of the support vector machine considering the multiple factors affected the slope stability, and select the indicators with the characteristic of common and easy access. Through the actual inspection verified the validity of the model, shows that the model can be well applied to the analysis of slope stability with similarity, it may provide an important basis for the slope project construction.

Slope Stability Analysis under the Condition of Seepage

2012 ◽  
Vol 204-208 ◽  
pp. 241-245
Author(s):  
Yang Jin
Keyword(s):  
Finite Element Method ◽  
Shear Strength ◽  
Slope Stability ◽  
Negative Impact ◽  
Strength Reduction ◽  
Soil Slope ◽  
Failure State ◽  
Calculation Results ◽  
The Stability ◽  
Shear Strength Reduction

The stability of soil slope under seepage is calculated and analyzed by using finite element method based on the technique of shear strength reduction. When the condition of seepage or not is considered respectively, the critical failure state of slopes and corresponding safety coefficients can be determined by the numerical analysis and calculation. Besides, through analyzing and comparing the calculation results, it shows that seepage has a negative impact on slope stability.

Soil Strength Reduction Method Induced by Variation of Water Content

2012 ◽  
Vol 170-173 ◽  
pp. 847-852
Author(s):  
Peng Ming Jiang ◽  
Zhong Lei Yan ◽  
Peng Li
Keyword(s):  
Slope Stability ◽  
Unsaturated Soil ◽  
Unsaturated Soils ◽  
Characteristic Curve ◽  
Strength Reduction ◽  
Actual State ◽  
Soil Slope ◽  
Simple Method ◽  
The Common ◽  
The Stability

As the complexity of unsaturated soil theory, and it must have a long test period when we study the unsaturated soils, so the conventional design analysis software does not provide such analysis, so we can imagine that such a slope stability analysis does not accurately reflect the actual state of the slope. Based on the known soil moisture content，this paper use the soil water characteristic curve and strength theory of unsaturated soil to calculate the strength reduction parameters of soil which can calculate the stability of the soil slope when using the common calculation method. It is noticeable that this method can be extended and applied if we establish regional databases for this simple method, and these databases can improve the accuracy of the calculation of slope stability.

Predicts Based on Multi Modality Support Vector of the Settlement of Composite Foundation

2014 ◽  
Vol 919-921 ◽  
pp. 716-722
Author(s):  
Li Hua Zhang ◽  
Hai Bo Liu ◽  
Feng Ping An
Keyword(s):  
Support Vector Machine ◽  
Composite Foundation ◽  
Good Effect ◽  
Support Vector ◽  
Foundation Settlement ◽  
Modal Synthesis ◽  
Mode Decomposition ◽  
Calculation Results ◽  
The Stability ◽  
High Degree

Composite foundation settlement is affected by many factors, and settlement data is a non-linear changing process with complexity, suddenness and progressive nature and so on. So we must analyze and predict the stability of the foundation settlement. Because empirical mode decomposition (EMD) provides a new way for foundation settlement prediction, we can extract modal signals associated with the foundation settlement mechanisms by decomposing the monitoring data of settlement by EMD and use the support vector machine ( Support Vector Machine, SVM) modal to predict the obtained signal, Calculation results of the modal synthesis and accumulation of foundation settlement, get the evolution of Change with the time of foundation settlement. Combined with the engineering example for the application ,shows that the prediction model has good effect in multi modality support vector, a high degree of agreement with the monitoring values, indicating that this method has a promotional value.

scholarly journals Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

2019 ◽  
Vol 9 (21) ◽  
pp. 4638 ◽  
Author(s):  
Moayedi ◽  
Bui ◽  
Kalantar ◽  
Foong
Keyword(s):  
Machine Learning ◽  
Slope Stability ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Stability Assessment ◽  
K Nearest Neighbor ◽  
The Stability ◽  
Intelligent Models ◽  
Improved Support Vector Machine

In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques.

Stability Analysis of Mountain Slope Based on Finite Element and Discrete Element

2012 ◽  
Vol 170-173 ◽  
pp. 1087-1090
Author(s):  
Wei Bin Yuan ◽  
Cheng Min Ye ◽  
Ji Yao ◽  
Lie De Wang
Keyword(s):  
Finite Element ◽  
Stability Analysis ◽  
Slope Stability ◽  
Discrete Element Method ◽  
Discrete Element ◽  
Soil Slope ◽  
Safety Factors ◽  
Mountain Slope ◽  
The Stability ◽  
Element Method

In recent year, the foundations of the stability analysis of slope were provided by the development of finite element and discrete element method. Using finite element and discrete element method, the stability analysis of three typical slopes of shiwu thorp of Quzhou was carried out. The safety factors of slope profile were obtained. Based on the judgment criterion of slope stability，the slopes stability of shiwu thorp was judged. The results showed that the way to analyze the stability of soil slope is feasible.

scholarly journals Strength Characteristics and Slope Stability of Expansive Soil from Pingdingshan, China

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Ju-yun Zhai ◽  
Xiang-yong Cai
Keyword(s):  
Shear Strength ◽  
Slope Stability ◽  
Expansive Soil ◽  
Friction Angle ◽  
Natural Soil ◽  
Soil Slope ◽  
The Finite Element Method ◽  
Shear Strength Parameters ◽  
Strength Parameters ◽  
The Stability

By analyzing the characteristics of expansive soil from Pingdingshan, China, the shear strength parameters at different water contents, dry densities, and dry-wet cycles of expansive soil are obtained. It is found that, at higher soil-water content, the internal friction angle is 0° and the shallow layer of expansive soil slope will collapse and destroy; this has nothing to do with the height of the slope and the size of the slope. The parameters of soil influenced by atmosphere are the ones which have gone through dry-wet cycles, and the parameters of soil without atmospheric influence are the same as those of natural soil. In the analysis of slope stability, the shear strength parameters of soil can be determined by using the finite element method, and the stability coefficient of the expansive soil slope can be calculated.

Self-Adjusting Fuzzy Support Vector Machine Based on Analysis of Potential Support Vector Sample Point

2019 ◽  
Vol 33 (10) ◽  
pp. 1959035
Author(s):  
Huaping Zhou ◽  
Huangli Qin
Keyword(s):  
Support Vector Machine ◽  
Time Complexity ◽  
Comparison Method ◽  
Experimental Comparison ◽  
Support Vector ◽  
Adjustment Factor ◽  
Fuzzy Support Vector Machine ◽  
Experimental Part ◽  
Good Classification ◽  
The Stability

Fuzzy support vector machine (FSVM) is a part of machine learning with its good classification effect. So far, there are two most commonly used FSVM models: FSVM on account of class core and fuzzy support vector machine on account of hyperplane that is over class core. Each has its own problems: FSVM on account of class core are dependent on the geometric shape of sample sets. Although FSVM on account of hyperplane that is over class core can solve the above problems to some extent. However, this algorithm has low generalization ability and high time complexity. Therefore, Inspired by these two common models, the paper proposes an improved membership function method. By analyzing and calculating the potential support vector sample points, adjustment factor is obtained, which drives the class core to adjust along the direction away from the outliers. In this way, membership of noise and outliers are reduced and the membership of support vector will also increase to some extent. In this paper, a new experimental comparison method is used, which can make the comparison of classification effect more obvious and convincing. The experimental part compares the proposed FSVM model with the above two FSVM models. It shows that the proposed algorithm improves the stability and classification accuracy to some extent.

scholarly journals Support Vector Machine Classifier for Accurate Identification of piRNA

2018 ◽  
Vol 8 (11) ◽  
pp. 2204 ◽  
Author(s):  
Taoying Li ◽  
Mingyue Gao ◽  
Runyu Song ◽  
Qian Yin ◽  
Yan Chen
Keyword(s):  
Support Vector Machine ◽  
Cell Function ◽  
Nucleotide Composition ◽  
Peptide Sequence ◽  
Single Type ◽  
Support Vector ◽  
Transcriptional Gene Silencing ◽  
Sequence Information ◽  
Accurate Identification ◽  
The Stability

Piwi-interacting RNA (piRNA) is a newly identified class of small non-coding RNAs. It can combine with PIWI proteins to regulate the transcriptional gene silencing process, heterochromatin modifications, and to maintain germline and stem cell function in animals. To better understand the function of piRNA, it is imperative to improve the accuracy of identifying piRNAs. In this study, the sequence information included the single nucleotide composition, and 16 dinucleotides compositions, six physicochemical properties in RNA, the position specificities of nucleotides both in N-terminal and C-terminal, and the proportions of the similar peptide sequence of both N-terminal and C-terminal in positive and negative samples, which were used to construct the feature vector. Then, the F-Score was applied to choose an optimal single type of features. By combining these selected features, we achieved the best results on the jackknife and the 5-fold cross-validation running 10 times based on the support vector machine algorithm. Moreover, we further evaluated the stability and robustness of our new method.

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