基于逐步判别分析的砂土液化预测研究
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摘要
为高效地进行砂土液化的预测,运用逐步判别法,从8个液化影响因子中选择平均粒径、烈度、震中距等3个判别能力显著的影响因子,建立判别函数,并利用工程实例进行验证。研究结果表明:逐步判别分析模型预测性能良好,且能有效地选择对砂土液化起主导作用的因子。相比距离判别分析,逐步判别分析建立的判别函数更加稳定,且所需测试因子较少,节省了因试验和现场调查所耗费的大量人力、物力和时间,因此逐步判别分析是一种值得推广的砂土液化预测方法。
In order to analyze sand liquefaction problem efficiently, the stepwise discriminant method was applied to its prediction; the factors with significant discriminant ability were selected to establish discriminant function. A number of site cases were used to verify its validity and accuracy. The results show that: stepwise discriminant analysis model has excellent liquefaction predicting performance; and it can choose effective factors which play a leading role in sand liquefaction. Compared to the distance discriminant analysis, discriminant function based on stepwise discriminant analysis is more stable; and fewer factors investigated or tested are needed, which saves human and material resources for the project construction. Thus it is worthwhile to apply stepwise discriminant analysis to liquefaction prediction widely.
In order to analyze sand liquefaction problem efficiently, the stepwise discriminant method was applied to its prediction; the factors with significant discriminant ability were selected to establish discriminant function. A number of site cases were used to verify its validity and accuracy. The results show that: stepwise discriminant analysis model has excellent liquefaction predicting performance; and it can choose effective factors which play a leading role in sand liquefaction. Compared to the distance discriminant analysis, discriminant function based on stepwise discriminant analysis is more stable; and fewer factors investigated or tested are needed, which saves human and material resources for the project construction. Thus it is worthwhile to apply stepwise discriminant analysis to liquefaction prediction widely.
引文
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[2]颜可珍,刘能源,夏唐代,基于判别分析法的地震砂土液化预测研究[J].岩土力学,2007,30(7):2049-2052.YAN Ke-zhen,LIU Neng-yuan,XIA Tang-dai.Dis-criminant analysis model for sand soil liquefaction during earthquake prediction[J].Rock and Soil Mechanics,2009,30(7):2049-2052.
[3]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 Huan-xue.Fuzzy synthetic evaluation methods for sand liquefaction during earthquake[J].Chinese Journal of Geotechnical Engineering,1993,15(2):74-79.
[4]罗战友,龚晓南.基于经验的砂土液化灰色关联系统分析与评价[J].工业建筑,2002,32(11):36-39.LUO Zhan-you,GONG Xiao-nan.Grey incidence system analysis and evaluation for liquefaction of sands based on experience[J].Industrial Construction,2002,32(11):36-39.
[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):1942-1946,1950.LIU Hong-jun,XUE Xin-hua.Artificial neural network model for sand soil seismic liquefaction prediction[J].Rock and Soil Mechanics,2004,25(12):1942-1946,1950.
[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 Guo-xing,LI Fang-ming.Probabilistic estimation of sand liquefaction based on neural network model of radial basis function[J].Chinese Journal of Geo-technical Engineering,2006,28(3):301-305.
[7]周瑞林,刘燕,赵胜利.基于RBF神经网络的砂土液化预测[J].河南大学学报(自然科学版),2005,35(4):101-104.ZHOU Rui-lin,LIU Yan,ZHAO Sheng-li.Application of RBF neural network to sand liquefaction prediction[J].Journal of Henan University(Natural Science),2005,35(4):101-104.
[8]金志仁.基于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 Zhi-ren.Prediction model for sand liquefaction based on distance discriminant analysis and its application[J].Chinese Journal of Geotechnical Engineering,2008,30(5):776-780.
[9]高惠璇.实用统计方法与SAS系统[M].北京:北京大学出版社,2001.
[10]余家林,肖枝洪.多元统计及SAS应用[M].武汉:武汉大学出版社,2007.
[11]夏建中,罗战友,龚晓南,等.基于支持向量机的砂土液化预测模型[J].岩石力学与工程学报,2005,24(22):4139-4144.XIA Jian-zhong,LUO Zhan-you,GONG Xiao-nan,etal.Sand liquefaction prediction model based on Support vector machine model[J].Chinese Journal of Rock Mechanics and Engineering,2005,24(22):4139-4144.
[12]中国建筑科学研究院工程抗震研究所.工业与民用建筑抗震验算与构造措施[M].北京:中国建筑科学研究院工程抗震研究所,1986.
[13]中华人民共和国建设部.GB50011-2001建筑抗震设计规范[S].北京:建筑工业出版社,2001.
[14]任文杰,苏经宇,窦远明,等.砂土液化判别的人工神经网络方法[J].河北工业大学学报,2002,31(2):21-25.REN Wen-jie,SU Jing-Yu,DOU Yuan-ming,et al.A neural network model for predicting sand liquefaction[J].Journal of Hebei University of Technology,2002,31(2):21-25.
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