[1]郝建斌,魏兴梅,王 芬.基于MABC-SVR的边坡可靠性分析[J].西安建筑科技大学学报(自然科学版),2020,(02):161-167.[doi:10.15986/j.1006-7930.2020.02.002]
 HAO Jianbin,WEI Xingmei,WANG Fen.Slope reliability analysis based on MABC-SVR[J].J. Xi’an Univ. of Arch. & Tech.(Natural Science Edition),2020,(02):161-167.[doi:10.15986/j.1006-7930.2020.02.002]
点击复制

基于MABC-SVR的边坡可靠性分析()
分享到:

西安建筑科技大学学报(自然科学版)[ISSN:1006-7930/CN:61-1295/TU]

卷:
期数:
2020年02期
页码:
161-167
栏目:
出版日期:
2020-04-25

文章信息/Info

Title:
Slope reliability analysis based on MABC-SVR
文章编号:
1006-7930(2020)02-0161-07
作者:
郝建斌1魏兴梅1王 芬12
(1.长安大学 地质工程与测绘学院,陕西 西安 710054; 2.西安市热力集团有限责任公司,陕西 西安 710016)
Author(s):
HAO Jianbin1WEI Xingmei1 WANG Fen12
(1.School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China; 2.Xi’an District Heating Group, Xi’an 710016, China)
关键词:
边坡可靠性 改进的人工蜂群算法 支持向量机回归 蒙特卡罗法
Keywords:
slope reliability modified artificial bee colony algorithm(MABC algorithm) Support Vector Machine Regression(SVR) Monte Carlo method
分类号:
TU43; TP18
DOI:
10.15986/j.1006-7930.2020.02.002
文献标志码:
A
摘要:
针对原始人工蜂群算法收敛较慢、易陷入局部最优的缺点,提出一种新的改进人工蜂群算法,并利用支持向量机建立了边坡安全系数预测模型(MABC-SVR).在此基础上,结合蒙特卡罗法对典型边坡算例(ACADS)进行边坡可靠性分析,计算该边坡的可靠性指标与失效概率,计算结果与已有结果基本接近,且整个建模和模拟运行过程用时不到26 s,与单纯使用蒙特卡罗法相比,用时大大缩短,可见该可靠性分析方法是科学可行的
Abstract:
Aiming at the disadvantage that the original artificial bee colony algorithm converges slowly and easily falls into local optimum, a new modified artificial bee colony algorithm(MABC)is proposed, and a slope safety factor prediction model(MABC-SVR)was established by combining the support vector machine.On this basis, a typical slope example(ACADS)is analyzed by Monte Carlo method to calculate the reliability index and failure probability of the slope.The calculation results are basically close to the existing results, and the whole modeling and simulation operation process is less than 26 s.Compared to just using the Monte Carlo method, the time is greatly shortened. It can be seen that the analytical method is scientific and feasible

参考文献/References:

[1] 何婷婷,尚岳全,吕庆,等.边坡可靠度分析的支持向量机法[J].岩土力学,2013,34(11):3269-3276.
HE Tingting, SHANG Yuequan, Lü Qing, et al.Slope Reliability Analysis Using Support Vector Machine[J]. Rock and Soil Mechanics,2013,34(11):3269-3276.
[2] 温廷新,于凤娥,邵良杉,等.基于GA-SVM的隧道围岩分类研究[J].公路交通科技,2018,35(9):63-70.
WEN Tingxin, YU Fenge, SHAO Liangshan, et al. Study on classification of tunnel surrounding rock based on GA-SVM[J].Journal of Highway and Transportation Research and Development,2018,35(9):63-70.
[3] 俞俊平,陈志坚,武立军,等.基于蚁群算法优化支持向量机的边坡位移预测[J].长江科学院院报,2015,32(4):22-27.
YU Junping, CHEN Zhijian, WU Lijun, et al. Forecasting slope displacement based on support vector machine optimized by Ant Colony algorithm[J]. Journal of Yangtze River Scientific Research Institute,2015,32(4):22-27.
[4] 康飞, 李俊杰, 马震岳. 基于人工蜂群算法的边坡最危险滑动面搜索[J]. 防灾减灾工程学报,2011, 31(2):166-172.
KANG Fei, LI Junjie, MA Zhenyue.Searching for critical slip surface of slops based on artificial Bee Colony Algorithm[J].Journal of Disaster Prevention and Mitigation Engineering,2011, 31(2):166-172.
[5] 洪勇,邵珠山,马力.支持向量机在边坡稳定分析预测的应用[J].沈阳建筑大学学报(自然科学版),2017,33(6):1004-1010.
HONG Yong, SHAO Zhushan, MA Li.Application of a support vector machine for analysis and prediction of slope stability[J]. Journal of Shenyang Jianzhu University( Natural Science),2017,33(6):1004-1010.
[6] ZHU Guopu, KWONG S.Gbest-guided artificial bee colony algorithm for numerical function optimization[J]. Applied Mathematics and Computation,2010,217(7): 3166-3173.
[7] 王冰. 基于局部最优解的改进人工蜂群算法[J]. 计算机应用研究, 2014, 31(4):1023-1026.
WANG Bing.Improved Artificial Bee Colony algorithm based on local best solution[J].Application Research of Computers, 2014, 31(4):1023-1026.
[8] KANG F, HANS, SALGADO R, et al. System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling[J]. Computers & Geotechnics, 2015, 63:13-25.
[9] 赵国藩, 金伟良, 贡金鑫. 结构可靠度理论[M]. 北京:中国建筑工业出版社, 2000:47-158.
ZHAO Guofan, JIN Weiliang, GONG Jinxin. Theory of structural reliability [M]. Beijing: China Building Industry press, 2000:47-158.
[10]王芬. MABC-SVR在边坡可靠性分析中的应用研究[D].西安:长安大学,2019.
WANG Fen.Application of MABC-SVR in slope reliability analysis[D].Xi’an: Chang’an University,2019.
[11]李新平, 郭运华, 彭元平等. 基于FLAC3D的改进边坡极限状态确定方法[J]. 岩石力学与工程学报, 2005, 24(S2):5287-5291.
LI Xinping, GUO Yunhua, PENG Yuanping, et al. Improved method to determine the critical state of slopes based on FLAC3D method [J]. Journal of Rock Mechanics and Engineering, 2005, 24(S2):5287-5291.
[12]YEH W C,SU J C P,HSIEH T J,et al.Approximate re1iability function based on wavelet Latin hypercube sampling and bee recurrent neural network[J].IEEE Transaction on Reliability,2011,60(2):404-414.
[13]邓乃扬,田英杰.支持向量机:理论、算法与拓展[M].北京:科学出版社, 2009:134-155.
DENG Naiyang,TIAN Yingjie. Support vector machine: theory,algorithm and extension[M].Beijing:Science Press,2009:134-155.
[14]罗战友, 杨晓军, 龚晓南.基于支持向量机的边坡稳定性预测模型[J].岩石力学与工程学报,2005, 24(1):144-148.
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.
[15]VAPNIC V N. The Nature of Statistical Learning Theory[M]. New York: Springer, 1995:126-178.
[16]秦全德,程适,李丽,等.人工蜂群算法研究综述[J].智能系统学报,2014,9(2):127-135.
QIN Quande, CHENG Shi, LI Li, et al. Artificial bee colony algorithm: a survey[J].CAAI Transactions on Intelligent Systems,2014,9(2):127-135.
[17]刘佳,梁秋丽,王军峰,等.改进的蜂群算法在边坡稳定性分析中的应用[J].建筑科学,2014,30(7):28-31.
LIU Jia, LIANG Qiuli, WANG Junfeng, et al. Application of improved artificial Bee Colony algorithm to slope stability analysis[J].Building Science,2014,30(7):28-31.
[18]王芬,刘阳,郝建斌,等.基于MABC-SVR的边坡安全系数预测模型[J].安全与环境工程,2019,26(2):178-182.
WANG Fen, LIU Yang, HAO Jianbin,et al.Prediction model of slope safety factor based on MABC-SVR[J].Safety and Environmental Engineering, 2019,26(2):178-182.
[19]JI J, LOW B K. Stratified response surfaces for system probabilistic evaluation of slopes[J].Journal of Geotechnical and Geoenviromental Engineering, 2012, 138(11):1398-1406.
[20]ZHANG J, HUANG H W,JUANG C H, et al. Extension of Hassan and Wolff method for system reliability analysis of soil slopes[J]. Engineering Geology, 2013, 160:81-88.
[21]KANG F, LI J, XU Q. System reliability analysis of slopes using multilayer perceptron and radial basis function networks[J]. International Journal for Numerical and Analytical Methods in Geomechanics, 2017, 41(18):1962-1978.
[22]FEI K, XU Q, LI J J. Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence[J]. Applied Mathematical Modelling, 2016, 40(11-12):6105-6120.
[23]赵洪波. 岩土力学与工程中的支持向量机分析[M].北京:煤炭工业出版社, 2008:109-117.
ZHAO Hongbo. Analysis of support vector machine in rock mechanic and engineering[M].Beijing:China Coal Industry Press,2008:109-117.

备注/Memo

备注/Memo:
收稿日期:2019-04-11 修改稿日期:2020-03-25
基金项目:国家自然科学基金(41472266)
第一作者:郝建斌(1975-),女,博士,教授,主要研究方向为岩土体稳定及安全性评价. E-mail: haojb@chd.edu.cn
通信作者:王 芬(1994-),女,硕士,主要研究方向为可靠性分析. E-mail:617047534@qq.com
更新日期/Last Update: 2020-04-25