基于PSO-SVM的砂土地震液化预测模型

doi:10.16265/j.cnki.issn1003-3033.2018.03.005

中国安全科学学报 ›› 2018, Vol. 28 ›› Issue (3): 25-30.doi: 10.16265/j.cnki.issn1003-3033.2018.03.005

基于PSO-SVM的砂土地震液化预测模型

毛志勇副教授, 黄春娟, 路世昌教授

辽宁工程技术大学工商管理学院, 辽宁葫芦岛 125105

收稿日期:2017-12-17 修回日期:2018-02-05 出版日期:2018-03-28 发布日期:2020-11-09
作者简介:毛志勇 (1976—),男,陕西汉中人,博士,副教授,硕士生导师,主要从事数据挖掘、信息系统、采矿工程等方面的研究。
基金资助:
国家自然科学基金资助(70971059)。

PSO-SVM based model for prediction of sandy soil liquefaction

MAO Zhiyong, HUANG Chunjuan, LU Shichang

School of Business Administration,Liaoning Technical University,Huludao Liaoning 125105,China

Received:2017-12-17 Revised:2018-02-05 Online:2018-03-28 Published:2020-11-09

摘要/Abstract

摘要： 为提高砂土地震液化预测的准确性和可靠性,根据其特点,选取地震烈度、地下水位、标准贯入击数、平均粒径、不均匀系数、上覆有效压力、动剪应力比等7个因子作为判别依据,采用粒子群算法(PSO)对支持向量机(SVM)的参数寻优,建立预测砂土地震液化的PSO-SVM模型;选用 50个样本训练模型,并用该模型预测14个测试样本,并回判所有样本。结果表明: 模型预测准确率为100%,该模型在解决砂土地震液化预测问题中,分类效果较好、效率较高。

关键词: 地震, 砂土液化, 数据归一化, 支持向量机(SVM), 粒子群算法(PSO)

Abstract: To improve the accuracy and reliability of sand liquefaction prediction, according to its characteristics,7 factors including the seismic intensity, groundwater level, standard penetration number, average particle size, non-uniform coefficient, overburden effective pressure and dynamic shear stress ratio were selected as a basis for discrimination. PSO was used to optimize the parameters of SVM, and a PSO-SVM model was built for predicting sand liquefaction. Fifty samples were chosen to train the model. The model was used to predict 14 test samples and all the samples were returned to the test. The prediction accuracy was 100%. The result shows that the PSO-SVM model is better in classification and higher in efficiency in solving the problem of sand liquefaction prediction.

Key words: earthquake, sandy soil liquefaction, data normalization, support vector machines (SVM), particle swarm optimization (PSO)

中图分类号:

X915.5

毛志勇, 黄春娟, 路世昌. 基于PSO-SVM的砂土地震液化预测模型[J]. 中国安全科学学报, 2018, 28(3): 25-30.

MAO Zhiyong, HUANG Chunjuan, LU Shichang. PSO-SVM based model for prediction of sandy soil liquefaction[J]. China Safety Science Journal, 2018, 28(3): 25-30.

参考文献

[1] JUANG C H, ROSOWSKY D V, TANG W H. Reliability-based method for assessing liquefaction potential of soils[J]. Journal of Geotechnical and Geoenvironmental Engineering, 1999, 125(8): 684-689.
[2] 潘建平,曾庆筠,宋应潞,等.尾矿坝地震液化侧向位移分析方法及应用[J].中国安全科学学报,2015,25(12): 99-104.
PAN Jianping, ZENG Qingyun, SONG Yinglu, et al.An analysis method of seismic liquefaction-induced lateral displacement for tailings dam and its application [J].China Safety Science Journal, 2015,25 (12): 99-104.
[3] 周燕国,谭晓明,梁甜,等.利用地震动强度指标评价场地液化的离心模型试验研究[J].岩土力学,2017,38(7): 1 869-1 877.
ZHOU Yanguo, TAN Xiaoming, LIANG Tian, et al.Evaluation of soil liquefaction by ground motion intensity index by centrifuge model test [J]. Rock and Soil Mechanics, 2017,38 (7): 1 869-1 877.
[4] 禹建兵,刘浪.不同判别准则下的砂土地震液化势评价方法及应用对比[J].中南大学学报:自然科学版,2013, 44(9): 3 849-3 856.
YU Jianbing, LIU Lang. Two multiple discriminant methods to evaluate sand seismic siquefaction potential and its comparison[J] Journal of Central South University:Science and Technology, 2013,44 (9): 3 849-3 856.
[5] 刘章军,叶燎原,彭刚.砂土地震液化的模糊概率评判方法[J].岩土力学,2008,29(4): 876-880.
LIU Zhangjun, YE Liaoyuan, PENG Gang.Fuzzy probability comprehensive evaluation method for sand liquefaction during earthquake [J]. Rock and Soil Mechanics, 2008,29(4): 876-880.
[6] 林志红,项伟.基于贝叶斯正则化BP神经网络的砂土地震液化研究[J].安全与环境工程,2011,18(2): 23-27.
LIN Zhihong, XIANG Wei.Analysis of sand seismic liquefaction by Bayesian regulated BP-neural networks [J]. Safety and Environmental Engineering, 2011,18(2): 23-27.
[7] 薛新华,杨兴国.基于减法聚类模糊神经网络的砂土液化势判别[J].地震工程与工程振动,2012,32(2): 172-177.
XUE Xinhua, YANG Xingguo.Application of fuzzy neural network to the prediction of sand liquefaction based on subtraction clustering [J]. Journal of Earthquake Engineering and Engineering Vibration, 2012,32(2): 172-177.
[8] 刘红军,薛新华.砂土地震液化预测的人工神经网络模型[J].岩土力学,2004,25(12):1 942-1 946,1 950.
LIU Hongjun, XUE Xinhua.Artificial neural network model for prediction of seismic liquefaction of sand soil [J]. Rock and Soil Mechanics, 2004,25(12): 1 942-1 946,1 950.
[9] 赵艳林,杨绿峰,吴敏哲.砂土液化的灰色综合评判[J].自然灾害学报,2000,9(1): 72-79.
ZHAO Yanlin, YANG Lyufeng, WU Minzhe.Grey synthetic evaluation of liquefaction of sands [J].Journal of Natural Disaster, 2000,9(1): 72-79.
[10] 赵小敏,曹丽文.砂土液化预测的Fisher判别分析模型及应用[J].水文地质工程地质,2012,39(3): 129-133.
ZHAO Xiaomin, CAO Liwen.Fisher discriminant analysis model and its application to sand liquefaction prediction [J]. Hydrogeology and Engineering Geology, 2012,39(3): 129-133.
[11] 薛新华,钟声. 基于Fisher判别法的砂土液化势判别[J]. 沈阳建筑大学学报:自然科学版,2016,32(6): 1 070-1 074.
XUE Xinhua, ZHONG Sheng.Sand liquefaction prediction using fisher discriminant analysis method [J].Journal of Shenyang Jianzhu University: Natural Science, 2016,32(6): 1 070-1 074.
[12] 邵良杉,徐波.岩溶塌陷倾向性等级的KPCA-SVM预测模型[J].中国安全科学学报,2015,25(3): 60-65.
SHAO Liangshan, XU Bo.KPCA-SVM model for predicting karst collapse tendency level [J].China Safety Science Journal, 2015,25 (3): 60-65.
[13] 施式亮,李润求,罗文柯.基于EMD-PSO-SVM的煤矿瓦斯涌出量预测方法及应用[J].中国安全科学学报,2014,24(7): 43-49.
SHI Shiliang, LI Runqiu, LUO Wenke.Method for predicting coal mine gas emission based on EMD-PSO-SVM and its application [J].China Safety Science Journal, 2014,24 (7): 43-49.
[14] 李润求,施式亮,念其锋,等.基于PSO-SVM的煤矿瓦斯爆炸灾害风险模式识别[J].中国安全科学学报,2013,23(5): 38-43.
LI Runqiu, SHI Shiliang, NIAN Qifeng, et al. Research on pattern recognition of gas explosion disaster risk in coal mines based on PSO-SVM [J]. China Safety Science Journal, 2013,23 (5): 38-43.
[15] 文畅平,陈宗辉,孙政,等. 基于未确知均值聚类分析的砂土地震液化评价[J]. 地下空间与工程学报,2017,13(2): 517-524.
WEN Changping, CHEN Zonghui, SUN Zheng, et al.Assessment of sandy soil liquefaction potential based on unascertained average clustering method [J].Chinese Journal of Underground Space and Engineering, 2017,13(2): 517-524.
[16] 任金刚. 砂土地震液化的神经网络预测[D].天津:天津大学,2007.
REN Jingang. Prediction of sandy liquefaction during earthquakes by the neural network [D]. Tianjin: Tianjin University, 2007.

基于PSO-SVM的砂土地震液化预测模型

PSO-SVM based model for prediction of sandy soil liquefaction

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	杨文臣, 周燕宁, 田毕江, 郭凤香, 胡澄宇. 基于聚类分析和SVM的二级公路交通事故严重度预测[J]. 中国安全科学学报, 2022, 32(5): 163-169.
[2]	丁茜, 赵晓东, 吴鑫俊, 张泰丽, 徐振涛. 基于RBF核的多分类SVM滑塌易发性评价模型[J]. 中国安全科学学报, 2022, 32(3): 194-200.
[3]	吴孟龙, 叶义成, 胡南燕, 王其虎, 李文, 江慧敏. 基于模糊信息粒化的矿业安全生产态势区间预测^*[J]. 中国安全科学学报, 2021, 31(9): 119-127.
[4]	白春, 王来贵, 刘书贤, 路沙沙, 聂伟. 煤矿采动对RC框架结构抗震性能影响试验研究^*[J]. 中国安全科学学报, 2021, 31(9): 174-183.
[5]	赵挺生, 庞奇志, 姜雯茜. 基于数据库与支持向量机的施工升降机安全风险预测[J]. 中国安全科学学报, 2021, 31(4): 11-17.
[6]	骆正山, 田珮琦. RS-PSO-ELM下腐蚀管道失效压力预测[J]. 中国安全科学学报, 2021, 31(3): 28-34.
[7]	刘大山, 陈晓春, 多英全, 师立晨. 化工园区地震Na-Tech事件快速风险评估方法[J]. 中国安全科学学报, 2021, 31(2): 127-134.
[8]	韩林, 赵旭东, 陈志龙, 龚华栋. 地震灾害下城市供水网络韧性评估及优化研究[J]. 中国安全科学学报, 2021, 31(2): 135-142.
[9]	刘阳, 张建经, 罗宏森, 于海莹, 向波. 基于贝叶斯网络的Fuzzy-SVM路基震害预测模型^*[J]. 中国安全科学学报, 2021, 31(11): 171-178.
[10]	郑晓亮, 来文豪, 薛生. MI和SVM算法在煤与瓦斯突出预测中的应用[J]. 中国安全科学学报, 2021, 31(1): 75-80.
[11]	李岩峰, 尹家骁, 刘朝峰, 王威, 于小晨, 李子芃. 基于投影寻踪聚类的供水管网地震韧性评估[J]. 中国安全科学学报, 2020, 30(6): 152-157.
[12]	马辉, 吕航, 栾瑞斌. 基于空间单元分析的装配式建筑施工安全预警模型[J]. 中国安全科学学报, 2020, 30(5): 27-32.
[13]	宋英华, 吴昊, 刘丹, 王喆. 基于D-S证据理论的地震应急救援群决策[J]. 中国安全科学学报, 2020, 30(5): 163-168.
[14]	武琳玉, 梁伟, 沙夺林. 油气站场典型设备的焊缝缺陷检测与识别方法[J]. 中国安全科学学报, 2020, 30(11): 108-113.
[15]	李宇, 陈明, 赵国辉. 脉冲型近场地震下HDR隔震简支梁桥参数研究[J]. 中国安全科学学报, 2020, 30(10): 141-148.