Slope Stability Forecast of LS-SVM Based on Chaos Genetic Algorithm Optimization

Abstract:

As the relationship between geotechnical slope stability and influencing factors is complex and nonlinear, the least squares support vector machines (LS-SVM) is used to establish the nonlinear relation between slope stability and influencing factors. And in consideration of that parameters selection of LS-SVM exerts a major influence on modeling results, the parameters of LS-SVM are optimized by chaos genetic algorithm (CGA). Thus the CGA LSSVM is proposed for forecasting slope stability. Through the comparison between the method and the simple genetic algorithm (SGA) parameters optimization. The result shows that parameters optimization of the CGA has better faster convergence speed, higher prediction precision. And the model is applied to predict the safety factor of the actual slope engineering and the results are well consistent with the actual situation. It is shown that the model is reasonable and feasible.

• Periodical: Applied Mechanics and Materials (Volumes 405-408)
• Pages: 2384-2390
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.405-408.2384
• Online since: September 2013
• Authors: Zheng Long Cai, Yong Dong Meng*, Rui Yao Wang, Wei Ping Lu
• Keywords: Chaos Genetic Algorithm Optimization, Least Squares Support Vector Machines (Ls-SVM), Parameter Optimization, Slope Stability Forecast

References

[1] LI Xiuzhen, KONG Jiming, XIE Jianxun. Support vector machine method for stability prediction of rock slopes in hydropower engineering regions[J]. Journal of China Coal Society, 2011, 36(Supp. 2): 259-263.

[2] FENG Xiating. Introduction to Intelligent rock mechanics[M]. Beijing: Science Press of China, (2000).

[3] HE Keqiang, LEI Jianhe. A predication of the slope stability based on neural network[J]. Geology and Prospecting, 2001, 37(6): 72-75.

[4] XUE Xinhua, ZHANG Wohua, LIU Hongjun. Evaluation of slope stability based on genetic algorithm and fuzzy neural network[J]. Rock and Soil Mechanics, 2007, 28(12): 2643-2648.

[5] VAPNIK V N. The nature of statistical learning theory [M]. New York: Springer-Verlag, (1995).

[6] MA Wentao. Evaluation of rock slope stability based on PSO and LSSVM[J]. Rock and Soil Mechanics, 2009, 30(3): 845-848.

[7] XU Fei, XU Weiya, WANG Ke. Slope stability analysis using least square support vector machine optimized with ant colony algorithm [J]. Journal of Engineering Geology, 2009, 17(2): 253－257.

[8] LUO Zhanyou, YANG Xiaojun, GONG Xiaonan. Support vector machine model in slope stability evaluation[J]. Chinese Journal of Rock Mechanics and Engineering, 2005, 24(1): 144-148.

[9] SUYKENS J A K, VANDEWALLE J. Least squares support vector machines classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300.

[10] LI Xingbao, PAN Feng. Parameter optimization and application of LSSVM based on genentic algorithm and chaos optimization[J]. Computer and Applied Chemistry, 2010, 27(10): 1380-1382.

[11] Southwest Jiaotong University. Report of dynamic stability and monitoring feedback analysis of right bank slope of jinping first stage hydroelectric power station[R], (2008).