GA_SVM_Adaboost model for prediction of earthquake-induced sandy soil liquefaction
-
摘要: 为快速准确地对砂土液化情况作出预测，选取地震烈度、地下水位、覆盖厚度、标贯击数、平均粒径、地貌单元、土质及不均匀系数为主要影响因素，运用相关性分析和因子分析模型对其进行分析和属性约减，采用遗传算法（GA）对支持向量机（SVM）的参数寻优，结合Adaboost迭代算法，建立预测砂土地震液化的GA_SVM_Adaboost模型。选用唐山地震砂土液化现场勘察资料中的329组数据对模型进行训练，利用该模型对剩余68组砂土液化数据进行预测。最后，将预测结果与GA_SVM和SVM模型预测结果进行比较。结果表明，3个模型的平均预测准确率分别为100%、98.04%、89.71%，基于因子分析的GA_SVM_Adaboost模型的预测准确性优于GA_SVM模型和SVM模型，是一种解决砂土地震液化预测问题的有效方法，具有一定的应用参考价值。
-
关键词:
- 砂土地震液化 /
- 因子分析(FA) /
- 支持向量机(SVM) /
- 遗传算法(GA) /
- Adaboost算法
Abstract: In order to predict the liquefaction of sand, the seismic intensity, groundwater level, covering thickness, standard number, average particle size, landform, soil quality and inhomogeneity coefficient are selected as influencing factors. Genetic algorithm(GA) is used to optimize the parameters of support vector machine(SVM) by using correlation analysis and factor analysis model, and combining with Adaboost iterative algorithm, the GA_SVM_Adaboost model for predicting the liquefaction of sand is established. 329 sets of survey data of sandy liquefaction site in Tangshan earthquake were used to train the model, and 68 samples of sandy liquefaction data were predicted by using the good model. Finally, The predicted results are compared with that of GA_SVM model and SVM model. The results show that the average prediction accuracy of three models is 100%, 98.04% and 89.71% respectively. The GA_SVM_Adaboost model based on factor analysis is better than GA_SVM model and SVM model which could improve the prediction accuracy. It is an effective method to predict the liquefaction of earthquakes. -
-
[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] RAHMAN M S,WANG Jun. Fuzzy neural network models for liquefaction prediction[J]. Soil Dynamics and Earthquake Engineering,2002,22(8):685-694.
[3] 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.
[4] 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. As-sessment of sandy soil liquefaction potential based on unascertained average clustering method[J]. Chinese Journal of Underground Space and Engineering,2017,13(2):517-524.
[5] 张紫昭,陈巨鹏,陈凯,等. 砂土地震液化预测的Bayes判别模型及其应用[J]. 桂林理工大学学报,2014,34(1):63-67. ZHANG Zizhao,CHEN Jupeng,CHEN Kai,et al. Bayes discriminant analysis model for seismic sand liquefaction[J]. Journal of Guilin University of Technology,2014,34(1):63-67.
[6] 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 & Engineering Geology,2012,39(3):129-133.
[7] 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.
[8] 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 on sand seismic liquefaction by bayesian regulated BP-neural networks[J]. Safety and Environmental Engineering,2011,18(2):23-27.
[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 Disasters,2000,9(1):72-79.
[10] 师旭超,郭志涛,韩阳. 基于支持向量机的砂土液化预测分析[J]. 西北地震学报,2009,31(4):363-366. SHI Xuchao,GUO Zhitao,HAN Yang. Analysis on sand seismic liquefaction prediction based on the support vector machine[J]. Northwestern Seismological Journal,2009,31(4):363-366.
[11] 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. Analysis of sand liquefaction potential of sand based on subtractive fuzzy neural network[J]. Journal of Earthquake Engineering and Engineering Vibration,2012,32(2):172-177.
[12] 邵良杉,李永利,赵琳琳. 露天采矿爆破振动对民房破坏的SVM预测模型[J]. 中国安全科学学报,2013,23(9):26-32. SHAO Liangshan,LI Yongli,ZHAO Linlin. SVM model for predicting residential house's damage from blasting vibration of open pit mining[J]. China Safety Science Journal,2013,23(9):26-32.
[13] 赵涛,于师建. 基于GA-BP神经网络算法的高密度电法非线性反演[J]. 煤田地质与勘探,2017,45(2):147-151. ZHAO Tao,YU Shijian. GA-BP neural network algorithm-based nonlinear inversion for high density resistivity method[J]. Coal Geology & Exploration,2017,45(2):147-151.
[14] 袁志刚,王宏图,胡国忠,等. 立井井筒非采动破裂的遗传-支持向量机预测模型[J]. 煤炭学报,2011,36(3):393-397. YUAN Zhigang,WANG Hongtu,HU Guozhong,et al. Forecast model of GA-SVM for shaft-lining non-mining fracture[J]. Journal of China Coal Society,2011,36(3):393-397.
[15] 乔美英,程鹏飞,刘震震. 基于GA-SVM的矿井涌水量预测[J]. 煤田地质与勘探,2017,45(6):117-122. QIAO Meiying,CHENG Pengfei,LIU Zhenzhen. Mine water inflow prediction based on GA-SVM[J]. Coal Geology & Exploration,2017,45(6):117-122.
[16] 王改革,郭立红,段红,等. 基于Elman_AdaBoost强预测器的目标威胁评估模型及算法[J]. 电子学报,2012,40(5):901-906. WANG Gaige,GUO Lihong,DUAN Hong,et al. The model and algorithm for the target threat assessment based on Elman-AdaBoost strong predictor[J]. Acta Electronica Sinica,2012,40(5):901-906.
[17] 李松,解永乐,王文旭. AdaBoost_BP神经网络在铁路货运量预测中的应用[J]. 计算机工程与应用,2012,48(6):233-234. LI Song,XIE Yongle,WANG Wenxu. Application of Ada-Boost_BP neural network in prediction of railway freight volumes[J]. Computer Engineering and Applications,2012,48(6):233-234.
[18] 李翔,朱全银. 基于Adaboost算法和BP神经网络的税收预测[J]. 计算机应用,2012,32(12):3558-3560. LI Xiang,ZHU Quanyin. Tax forecasting based on Adaboost algorithm and BP neural network[J]. Journal of Computer Applications,2012,32(12):3558-3560.
[19] 唐山地震砂土液化联合研究小组. 唐山地震砂土液化现场勘察资料研究报告[R]. 北京:北京市勘察处,1983:26-41. -
期刊类型引用(9)
1. 宁世凯，谢润泽，李斌，任玉鹏，黄俊杰，贺鑫. 砂性土路基换填碾压地基处理抗地震液化效果分析. 路基工程. 2025(01): 206-212 . 
百度学术
2. 谭静，侯涛，杜小渊，戈璞，李继伟. 基于支持向量机与因子分析的电容器状态识别研究. 铁道车辆. 2024(04): 86-91 . 百度学术
3. 吕俊超，俞社鑫，周渊. 基于循环神经网络的砂土液化预测模型. 四川建材. 2022(04): 96-98 . 百度学术
4. 叶童，李治广. 基于ELM原理的砂土液化判别模型及应用. 山西建筑. 2022(16): 7-10+73 . 百度学术
5. 邢明源，胡欣宇. 基于神经网络模型的砾性土液化预测. 华北科技学院学报. 2021(03): 75-80 . 百度学术
6. 亚生江·阿布德热合曼. 水闸砂土地基地震荷载作用下液化特征研究. 水利技术监督. 2020(03): 265-268 . 百度学术
7. 陈宇奇，施国良，张潇潇，谢泽宇. 基于修正RFM模型的高校图书馆热门图书评价体系及影响因素研究. 图书馆学研究. 2020(10): 58-68 . 百度学术
8. 葛迪，陈文韬，侯群. 高速传送带上的工业元件瑕疵检测分类系统. 电子测量技术. 2020(10): 42-47 . 百度学术
9. 朱光婷，潘晓琳. 基于因子分析和SVM的网络舆情危机预警研究. 重庆工商大学学报(自然科学版). 2020(05): 94-100 . 百度学术
其他类型引用(6)
计量
- 文章访问数: 157
- HTML全文浏览量: 4
- PDF下载量: 9
- 被引次数: 15
下载:
