不同判别准则下的砂土地震液化势评价方法及应用对比
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摘要
借鉴统计学理论,提出采用不同判别准则的多元判别方法对砂土液化势进行识别。选取震级M、地面加速度最大值gmax、标准贯入击数N63.5、比贯入阻力Ps、相对密实度Dr、平均粒径D50、地下水位dw这7个实测指标作为砂土液化势预测的主要影响因子,搜集唐山大地震和广东三水等25组典型案例作为样本数据库,以其中20组数据作为训练样本,依据不同判别准则以及Bayes判别分析(BDA)和Fisher线性判别分析(FDA)方法,分别建立2个研究区砂土液化势的Bayes判别分析(BDA)判识模型和Fisher线性判别分析(FDA)判识模型,并利用该模型对另外5组砂土液化实例进行仿真测试。研究结果表明:BDA方法和FDA方法对这2个研究区测试样本的误判率分别为26%和28%,对学习样本的错判率分别为4%和5%,说明在唐山大地震和广东三水地区砂土液化势识别中,BDA法比FDA法判识准确性更高,适用性更强,可考虑在实际工程中推广。
Based on the statistical theory,the different criterion of multiple discriminant approach to identify the potential for sand liquefaction was presented.Seven measured indicators of soil liquefaction potential were selected as key impacting indicators to predict the main impact factor,including magnitude M,the maximum ground acceleration gmax,standard penetration blow count N63.5,specific penetration resistance Ps,relative density Dr,average particle size of D50 and water table dw,were selected to predict the main impact factor,Firstly,25 groups typical cases were collected from the Tangshan earthquake and Guangdong Sanshui area as sample database.Based on different criteria of Bayes discriminant analysis(BDA) and Fisher discriminant analysis(FDA) method,liquefaction potential BDA discriminating model and FDA discriminating model were established in two research areas,respectively,and the proposed models were used for the remaining samples of soil liquefaction simulation examples test.The results show that the test samples false positive rates obtained by BDA and FDA are 26% and 28%,respectively,and the false-positive rates samples are 4% and 5%,respectively.Thus,in the Tangshan earthquake and the liquefaction potential of Sanshui in Guangdong region recognition,BDA law discriminating method has higher accuracy and applicability than FDA,and can be considered to be used in the actual project.
Based on the statistical theory,the different criterion of multiple discriminant approach to identify the potential for sand liquefaction was presented.Seven measured indicators of soil liquefaction potential were selected as key impacting indicators to predict the main impact factor,including magnitude M,the maximum ground acceleration gmax,standard penetration blow count N63.5,specific penetration resistance Ps,relative density Dr,average particle size of D50 and water table dw,were selected to predict the main impact factor,Firstly,25 groups typical cases were collected from the Tangshan earthquake and Guangdong Sanshui area as sample database.Based on different criteria of Bayes discriminant analysis(BDA) and Fisher discriminant analysis(FDA) method,liquefaction potential BDA discriminating model and FDA discriminating model were established in two research areas,respectively,and the proposed models were used for the remaining samples of soil liquefaction simulation examples test.The results show that the test samples false positive rates obtained by BDA and FDA are 26% and 28%,respectively,and the false-positive rates samples are 4% and 5%,respectively.Thus,in the Tangshan earthquake and the liquefaction potential of Sanshui in Guangdong region recognition,BDA law discriminating method has higher accuracy and applicability than FDA,and can be considered to be used in the actual project.
引文
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[14]Pal M.Support vector machines-based modelling of seismic liquefaction potential[J].International Journal for Numerical and Analytical Methods in Geomechanics,2006,30:983-996.
[15]金志仁.距离判别分析方法的砂土液化预测模型及应用[J].岩土工程学报,2008,30(5):776-780.JIN Zhiren.Prediction of sand liquefaction based on discriminant analysis and its application[J].Chinese Journal Geotechnical Engineering,2008,30(5):776-780.
[16]汪明武,李丽,金菊良.基于盲数理论的液化等级风险评价模型[J].岩土工程学报,2010,32(2):303-307.WANG Mingwu,LI Li,JIN Juliang.Application of unascertained number theory to risk evaluation of liquefaction grade[J].Chinese Journal of Geotechnical Engineering,2010,32(2):303-307.
[17]梅长林,范金城.数据分析方法[M].北京:高等教育出版社,2006:79-96.MEI Changlin,FAN Jincheng.Data analysis method[M].Beijing:Higher Education Press,2006:79-96.
[18]史秀志,周健,杜坤,等.爆破振动对民房破坏效应预测的BDA模型及应用[J].振动与冲击,2010,29(7):60-65.SHI Xiuzhi,ZHOU Jian,DU Kun,et al.BDA model for predicting destructive effect of blast vibration on housing[J].Journal of Vibration and Shock,2010,29(7):60-65.
[19]陈红江,李夕兵,刘爱华,等.用Fisher判别法确定矿井突水水源[J].中南大学学报:自然科学版,2009,40(4):1114-1120.CHEN Hongjiang,LI Xibing,LIU Aihua,et al.Identifying of mine water inrush sources by Fisher discriminant analysis method[J].Journal of Central South University:Science and Technology,2009,40(4):1114-1120.
[20]史秀志,周健.冲击地压危险性等级预测的Fisher判别分析方法[J].采矿与安全工程学报,2010,27(4):562-567.SHI Xiuzhi,ZHOU Jian.Reliability assessment for mine ventilation system safety using Fisher discriminant analysis[J].Journal of Mining&Safety Engineering,2010,27(4):562-567.
[21]刘恢先.唐山大地震震害[M].北京:地震出版社,1985:135-144.LIU Huixian.Seismic damage of Tangshan great earthquake[M].Beijing:Earthquake Press,1985:135-144.
[2]Seed H B,Idriss I M.Simplified procedure for evaluating soil liquefaction potential[J].Journal of Geotechnical Engineering,1971,97(9):1249-1273.
[3]GB 50011—2001,建筑抗震设计规范[S].GB 50011—2001,Code for seismic design of buildings[S].
[4]Juang C H,Chen C J,Tien Y M.Appraising cone penetration test based liquefaction resistance evaluation methods:Artificial neural network approach[J].Canadian Geotechnical Journal,1999,36:443-454.
[5]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):1942-1946.LIU Hongjun,XUE Xinhua.Artificial neural network model for prediction of seismic liquefaction of sand soil[J].Rock and Soil Mechanics,2004,25(12):1942-1946.
[6]陈国兴,李方明.基于径向基函数神经网络模型的砂土液化概率判别方法[J].岩土工程学报,2006,28(3):301-305.CHEN Guoxing,LI Fangming.Probabilistic estimation of sand liquefaction based on neural network model of radial basis function[J].Chinese Journal of Geotechnical Engineering,2006,28(3):301-305.
[7]董贤哲.基于补偿模糊神经网络的砂土液化势评价研究[D].北京:中国地质大学工程技术学院,2005:102-115.DONG Xianzhe.Research on assessment of sand liquefaction potential based on compensative fuzzy neural network[D].Beijing:China University of Geosciences.School of Engineering and Technology,2005:102-115.
[8]安宁.路基砂土液化等级的BP网络预测研究[J].土工基础,2007,21(4):45-48.AN Ning.Pre-determination of sand liquefaction in road subgrade by BP network[J].Soil Eng and Foundation,2007,21(4):45-48.
[9]薛新华,陈群.基于GRNN的砂土液化危害等级评价模型研究[J].四川大学学报:工程科学版,2010,42(1):42-47.XUE Xinhua,CHEN Qun.Study on hazard degree evaluation of sand liquefaction based on the generalized regression neural network[J].Journal of Sichuan University:Engineering Science Edition,2010,42(1):42-47.
[10]翁焕学.砂土地震液化模糊综合评判实用方法[J].岩土工程学报,1993,15(2):74-79.WENG Huanxue.Saturated sand liquefaction potential estimation method based on fuzzy comprehensive evaluation[J].Chinese Journal Geotechnical Engineering,1993,15(2):74-79.
[11]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.
[12]汪明武,罗国煜.可靠性分析在砂土液化势评价中的应用[J].岩土工程学报,2000,22(5):542-544.WANG Mingwu,LUO Guoyu.Application of reliability analysis to assessment of sand liquefaction potential[J].Chinese Journal of Geotechnical Engineering,2000,22(5):542-544.
[13]陈荣淋.基于可拓学和支持向量机理论的砂土液化势综合评价研究[D].厦门:华侨大学.土木工程学院,2006:24-63.CHEN Ronglin.Research on assessment of sand liquefaction potential based on extenics and support vector machine[D].Xiamen:Huaqiao University.College of Civil Engineering,2006:24-63.
[14]Pal M.Support vector machines-based modelling of seismic liquefaction potential[J].International Journal for Numerical and Analytical Methods in Geomechanics,2006,30:983-996.
[15]金志仁.距离判别分析方法的砂土液化预测模型及应用[J].岩土工程学报,2008,30(5):776-780.JIN Zhiren.Prediction of sand liquefaction based on discriminant analysis and its application[J].Chinese Journal Geotechnical Engineering,2008,30(5):776-780.
[16]汪明武,李丽,金菊良.基于盲数理论的液化等级风险评价模型[J].岩土工程学报,2010,32(2):303-307.WANG Mingwu,LI Li,JIN Juliang.Application of unascertained number theory to risk evaluation of liquefaction grade[J].Chinese Journal of Geotechnical Engineering,2010,32(2):303-307.
[17]梅长林,范金城.数据分析方法[M].北京:高等教育出版社,2006:79-96.MEI Changlin,FAN Jincheng.Data analysis method[M].Beijing:Higher Education Press,2006:79-96.
[18]史秀志,周健,杜坤,等.爆破振动对民房破坏效应预测的BDA模型及应用[J].振动与冲击,2010,29(7):60-65.SHI Xiuzhi,ZHOU Jian,DU Kun,et al.BDA model for predicting destructive effect of blast vibration on housing[J].Journal of Vibration and Shock,2010,29(7):60-65.
[19]陈红江,李夕兵,刘爱华,等.用Fisher判别法确定矿井突水水源[J].中南大学学报:自然科学版,2009,40(4):1114-1120.CHEN Hongjiang,LI Xibing,LIU Aihua,et al.Identifying of mine water inrush sources by Fisher discriminant analysis method[J].Journal of Central South University:Science and Technology,2009,40(4):1114-1120.
[20]史秀志,周健.冲击地压危险性等级预测的Fisher判别分析方法[J].采矿与安全工程学报,2010,27(4):562-567.SHI Xiuzhi,ZHOU Jian.Reliability assessment for mine ventilation system safety using Fisher discriminant analysis[J].Journal of Mining&Safety Engineering,2010,27(4):562-567.
[21]刘恢先.唐山大地震震害[M].北京:地震出版社,1985:135-144.LIU Huixian.Seismic damage of Tangshan great earthquake[M].Beijing:Earthquake Press,1985:135-144.
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