The complexity of geotechnical engineerings such as slopes and Embankment is reflected not only in the change of various geotechnical parameters but also in the implicit, non-analytical, and even unverifiable nature of its functional models. Due to this feature, an example of a slope limit equilibrium model in algorithm development with an easy application for direct solution of slope engineering stability and reliability is investigated in this paper. First, the slope limit equilibrium model will be called to obtain a suitable example of the basic rock and soil parameters and the corresponding slope stability coefficient; Then, the anisotropic correlation mapping method of the Kriging model is used in ground statistics to express the value of slope performance function as a random process and process control variables are determined through samples, then Monte Carlo simulation and active learning methods together are combined. The test specimens are set according to the search rules and determine the most probable region of rupture on the slope where the slope function represented by the random process is obtained through an iterative loop, finally, the random process function is used to obtain the probability of slope rupture through simple and direct calculation in this field. Engineering analysis and calculation results show that the accuracy of this method is equivalent to the Monte Carlo simulation method, but the calculation process is simpler and has a lower and more economical calculation cost.
Published in | Journal of Civil, Construction and Environmental Engineering (Volume 7, Issue 4) |
DOI | 10.11648/j.jccee.20220704.13 |
Page(s) | 63-72 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
Slope Stability, Random Process Kriging Model, Monte Carlo Method, Active Review
[1] | Liu Ning, Shao Guojian, Wang Yuan. Reliability calculation of anchorage stability of cavern surrounding rock under combined action of in-situ stress and seepage [J]. Chinese Journal of Geotechnical Engineering, 2000, 22 (6): 711 – 715. (LIU Ning, SHAO Guo-jian, WANG Yuan. Reliability assessment of rockbolt reinforced underground structures influenced by seepage and underground stress field [J]. Chinese Journal of Geotechnical Engineering, 2000, 22 (6): 711– 715. |
[2] | Su Yonghua, Li Xiang, Ding Yun, et al. Tunnel surrounding rock stability reliability method based on quadratic orthogonal experiment optimization [J]. Chinese Journal of Geotechnical Engineering, 2012, 34 (2): 326 – 332. (SU Yong-hua, LI Xiang, DING Yun, et al. Reliability degree method for stability of surrounding rock of tunnels based on quadratic orthogonal experimental optimization [J]. Chinese Journal of Geotechnical Engineering, 2012, 34 (2): 326– 332. |
[3] | Li Dianqing, Jiang Shuihua, Zhou Chuangbing. Reliability analysis of underground caverns based on non-intrusive stochastic finite element method [J]. Chinese Journal of Geotechnical Engineering, 2012, 34 (1): 123 – 129. (LI Dian-qing, JIANG Shui-hua, ZHOU Chuang-bing. Reliability analysis of underground rock caverns using non-intrusive stochastic finite element method [J]. Chinese Journal of Geotechnical Engineering, 2012, 34 (1): 123– 129. |
[4] | Tan Xiaohui, Wang Jianguo. Slope elastoplastic finite element reliability analysis [J]. Chinese Journal of Geotechnical Engineering, 2007, 29 (1): 44– 50. (TAN Xiao-hui, WANG Jian-guo. Slope reliability analysis using elasto-plastic finite element method [J]. Chinese Journal of Geotechnical Engineering, 2007, 29 (1): 44– 50. |
[5] | Tan Xiaohui, Wang Jianguo, Wu Linian, et al. Research on accelerating convergence algorithm of nonlinear stochastic finite element for slope stability [J]. Chinese Journal of Geotechnical Engineering, 2007, 29 (7): 1030– 1034. (TAN Xiao-hui, WANG Jian-guo, WU Li-nian, et al. Studies on accelerating convergence method in nonlinear stochastic finite element analysis of slope stability [J]. Chinese Journal of Geotechnical Engineering, 2007, 29 (7): 1030– 1034. |
[6] | Zheng Junjie, Guo Jia, Li Fuhao. Geotechnical engineering reliability analysis based on immune algorithm [J]. Chinese Journal of Geotechnical Engineering, 2007, 29 (5): 785– 788. (ZHENG Jun-jie, GUO Jia, LI Fu-hao. Immune algorithm for reliability analysis of geotechnical engineering [J]. Chinese Journal of Geotechnical Engineering, 2007, 29 (5): 785 – 788. |
[7] | Su Guoshao, Xiao Yilong. Gaussian process method for slope reliability analysis [J]. Chinese Journal of Geotechnical Engineering, 2011, 33 (6): 916– 920. (SU Guo-shao, XIAO YI-Long. Gaussian process method for slope reliability analysis [J]. Chinese Journal of Geotechnical Engineering, 2011, 33 (6): 916– 920. |
[8] | Wang Yu, Wang Chunlei, Wang Can, et al. Research and application of vector projection response surface for slope reliability evaluation [J]. Chinese Journal of Geotechnical Engineering, 2011, 33 (9): 1434– 1439. (WANG Yu, WANG Chun-lei, WANG Can, et al. Reliability evaluation of slopes based on vector projection response surface and its application [J]. Chinese Journal of Geotechnical Engineering, 2011, 33 (9): 1434 – 1439. |
[9] | Li Dianqing, Tang Xiaosong, Zhou Chuangbing. Cognitive clustering partition method for slope reliability analysis with related non-normal variables [J]. Chinese Journal of Geotechnical Engineering, 2011, 33 (6): 875 – 882. (LI Ding-qing, TANG Xiao-song, ZHOU Chuang-bing. Reliability analysis of slope stability involving correlated non-normal variables using knowledge-based clustered partitioning method [J]. Chinese Journal of Geotechnical Engineering, 2011, 33 (6): 875 – 882. |
[10] | Su Yonghua, Zhao Minghua, Li Qinghai, et al. Approximate calculation method of slope reliability with stability coefficient as implicit function [J]. Chinese Journal of Geotechnical Engineering, 2006, 28 (10): 1198– 1203. (SU Yong -hua, ZHAO Ming-hua, LI Qing-hai, et al. Approximative method to calculate reliability of slope with stability coefficient to be implicit expression [J]. Chinese Journal of Geotechnical Engineering, 2006, 28 (10): 1198– 1203. |
[11] | KRISHNAMURTHY T. Comparison of response surface construction methods for derivative estimation using moving least squares, Kriging and radial basis functions [R]. Austin: American Institute of Aeronautics and Astronautics, 2005. |
[12] | GOMES H M, AWRUCH A M. Comparison of response surface and neural network with other methods for structural reliability analysis [J]. Structural Safety, 2004, 26 (1): 49– 57. |
[13] | Zhao Hongbo. Reliability analysis of slope based on support vector machine [J]. Chinese Journal of Geotechnical Engineering, 2007, 29 (6): 819– 823. (ZHAO Hong-bo. Reliability analysis of slope based on support vector machine [J]. Chinese Journal of Geotechnical Engineering, 2007, 29 (6): 819– 823. |
[14] | CHERKASSKY V, MA Y. Practical selection of SVM parameters and noise estimation for SVM regression [J]. Neural Networks, 2004, 17 (1): 113– 126. |
[15] | Mikroutsikos A, Theocharis AI, Koukouzas NC, Zevgolis IE. Slope stability of deep surface coal mines in the presence of a weak zone. Geomech Geophys Geo-Energy Geo-Resources. 2021; 7 (3). |
[16] | Rahimi M, Wang Z, Shafieezadeh A, Wood D, Kubatko EJ. An Adaptive Kriging-Based Approach with Weakly Stationary Random Fields for Soil Slope Reliability Analysis. In 2019. p. 148–57. |
[17] | Li X, Liu Y, Yang Z, Meng Z, Zhang L. Efficient slope reliability analysis using adaptive classification-based sampling method. Bull Eng Geol Environ. 2021; 80 (12): 8977–93. |
[18] | Zhang TL, Zeng P, Li T Bin, Sun XP. System reliability analyses of slopes based on active-learning radial basis function. Yantu Lixue/Rock Soil Mech. 2020; 41 (9): 3098–108. |
[19] | KAYMAZ I. Application of kriging method to structural reliability problems [J]. Structural Safety, 2005, 27 (2): 133– 151. |
[20] | Zhang Qi, Li Xingsi. Importance sampling approach in structural reliability analysis [J]. Engineering Mechanics, 2007, 24 (1): 33– 36. (ZHANG Qi, LI Xing-si. Importance sampling approach in structural reliability analysis based on kriging simulation [J]. Engineering Mechanics, 2007, 24 (1): 33– 36. |
[21] | Xie Yanmin, Yu Huping, Chen Jun, et al. Reliability calculation based on Kriging model [J]. Journal of Shanghai Jiaotong University, 2007, 41 (2): 177– 180. (XIE Yan-min, YU Hu- ping, CHEN Jun, et al. The reliability estimation based on kriging model [J]. Journal of Shanghai Jiaotong University, 2007, 41 (2): 177– 180. |
[22] | ECHARD B, GAYTON N, LEMAIRE M. AK-MCS: An active learning reliability method combining Kriging and MonteCarlo Simulation [J]. Structural Safety, 2011, 33 (2): 145– 154. |
[23] | SOREN N Lophaven, HANS Bruun Nielsen, JACOB Sondergaard. Aspects of the matlab toolbox DACE [C]// Report IMM-TR-2002-12, Informatics and Mathematical Modelling [DB/OL]. Lyngby: Technical University of Denmark, 2002. |
[24] | Gong Jinxin. Calculation method for reliability degree of engineering structure [M]. Dalian: Dalian University of Technology Press, 2003. (GONG Jin-xin. Calculation method for reliability degree of engineering structure [M]. Dalian: Press of Dalian Technology University, 2003. |
APA Style
Semko Arefpanah, Alireza Sharafi, Fatemeh Salehi. (2022). The Soil Slope Stability in Failure with the Use of the Random Process Based on the Kriging’s Interpolation Model. Journal of Civil, Construction and Environmental Engineering, 7(4), 63-72. https://doi.org/10.11648/j.jccee.20220704.13
ACS Style
Semko Arefpanah; Alireza Sharafi; Fatemeh Salehi. The Soil Slope Stability in Failure with the Use of the Random Process Based on the Kriging’s Interpolation Model. J. Civ. Constr. Environ. Eng. 2022, 7(4), 63-72. doi: 10.11648/j.jccee.20220704.13
@article{10.11648/j.jccee.20220704.13, author = {Semko Arefpanah and Alireza Sharafi and Fatemeh Salehi}, title = {The Soil Slope Stability in Failure with the Use of the Random Process Based on the Kriging’s Interpolation Model}, journal = {Journal of Civil, Construction and Environmental Engineering}, volume = {7}, number = {4}, pages = {63-72}, doi = {10.11648/j.jccee.20220704.13}, url = {https://doi.org/10.11648/j.jccee.20220704.13}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jccee.20220704.13}, abstract = {The complexity of geotechnical engineerings such as slopes and Embankment is reflected not only in the change of various geotechnical parameters but also in the implicit, non-analytical, and even unverifiable nature of its functional models. Due to this feature, an example of a slope limit equilibrium model in algorithm development with an easy application for direct solution of slope engineering stability and reliability is investigated in this paper. First, the slope limit equilibrium model will be called to obtain a suitable example of the basic rock and soil parameters and the corresponding slope stability coefficient; Then, the anisotropic correlation mapping method of the Kriging model is used in ground statistics to express the value of slope performance function as a random process and process control variables are determined through samples, then Monte Carlo simulation and active learning methods together are combined. The test specimens are set according to the search rules and determine the most probable region of rupture on the slope where the slope function represented by the random process is obtained through an iterative loop, finally, the random process function is used to obtain the probability of slope rupture through simple and direct calculation in this field. Engineering analysis and calculation results show that the accuracy of this method is equivalent to the Monte Carlo simulation method, but the calculation process is simpler and has a lower and more economical calculation cost.}, year = {2022} }
TY - JOUR T1 - The Soil Slope Stability in Failure with the Use of the Random Process Based on the Kriging’s Interpolation Model AU - Semko Arefpanah AU - Alireza Sharafi AU - Fatemeh Salehi Y1 - 2022/07/13 PY - 2022 N1 - https://doi.org/10.11648/j.jccee.20220704.13 DO - 10.11648/j.jccee.20220704.13 T2 - Journal of Civil, Construction and Environmental Engineering JF - Journal of Civil, Construction and Environmental Engineering JO - Journal of Civil, Construction and Environmental Engineering SP - 63 EP - 72 PB - Science Publishing Group SN - 2637-3890 UR - https://doi.org/10.11648/j.jccee.20220704.13 AB - The complexity of geotechnical engineerings such as slopes and Embankment is reflected not only in the change of various geotechnical parameters but also in the implicit, non-analytical, and even unverifiable nature of its functional models. Due to this feature, an example of a slope limit equilibrium model in algorithm development with an easy application for direct solution of slope engineering stability and reliability is investigated in this paper. First, the slope limit equilibrium model will be called to obtain a suitable example of the basic rock and soil parameters and the corresponding slope stability coefficient; Then, the anisotropic correlation mapping method of the Kriging model is used in ground statistics to express the value of slope performance function as a random process and process control variables are determined through samples, then Monte Carlo simulation and active learning methods together are combined. The test specimens are set according to the search rules and determine the most probable region of rupture on the slope where the slope function represented by the random process is obtained through an iterative loop, finally, the random process function is used to obtain the probability of slope rupture through simple and direct calculation in this field. Engineering analysis and calculation results show that the accuracy of this method is equivalent to the Monte Carlo simulation method, but the calculation process is simpler and has a lower and more economical calculation cost. VL - 7 IS - 4 ER -