基于判别分析法的地震砂土液化预测研究
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摘要
将距离判别分析方法应用于砂土液化的预测问题中,建立了砂土液化预测的距离判别模型。选用震级、研究深度、震中距、标贯击数、地下水位及地震持续时间等6项指标作为判别因子,以大量的工程实例数据作为学习样本进行训练,建立了线性判别函数对待评样本进行了评价。研究结果表明,距离判别分析模型判别砂土液化效果良好,预测准确度高,回判估计误判率低,可望成为砂土液化预测的有效手段。
Based on statistics theory, Mahalanobis distance discriminant analysis model for prediction of sand soil liquefaction is established, including the main six factors that have great influence on sand soil liquefaction as follows: seismic intensity, epicenter distance, groundwater table, sand depth, blow number of standard penetration test, duration. Linear discriminant function is obtained through training a large set of sand soil liquefaction samples. Then the model is applied to the practical engineering. The results show that the prediction model of diatance dicriminant analysis has excellent performance,high prediction accuracy, and might be referred to as an effective technique for prediction of sand soil liquefaction.
Based on statistics theory, Mahalanobis distance discriminant analysis model for prediction of sand soil liquefaction is established, including the main six factors that have great influence on sand soil liquefaction as follows: seismic intensity, epicenter distance, groundwater table, sand depth, blow number of standard penetration test, duration. Linear discriminant function is obtained through training a large set of sand soil liquefaction samples. Then the model is applied to the practical engineering. The results show that the prediction model of diatance dicriminant analysis has excellent performance,high prediction accuracy, and might be referred to as an effective technique for prediction of sand soil liquefaction.
引文
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[3]翁焕学.砂土地震液化模糊综台评判实用方法[J].岩土工程学报,1993,15(2):74-79.WENG Huan-xue.Fuzzy synthetic methods of appraisal of sand liquefaction during earthquake[J].Chinese Journal of Geotechnical Engineering,1993,15(2):74-79.
[4]蔡煜东,宫家文,姚林声.砂土液化预测的人工神经网络模型[J].岩土工程学报,1993,15(6):53-58.CAI Yu-dong,GONG Jia-wen,YAO Lin-sheng.Artificial neural network model of prediction of sandy liquefaction[J].Chinese Journal of Geotechnical Engineering,1993,15(6):53-58.
[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 prediction of seismic liquefaction of sand soil[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]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 of liquefaction of sands based on experience[J].Industrial Construction,2002,32(11):36-39.
[8]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 Prediction of Sands Liquefaction Potential[J].Journal of Henan University(Natural Science),2005,35(4):101-104.
[9]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,et al.Support vector machine model for prediction sand liquefaction[J].Chinese Journal of Rock Mechanics and Engineering,2005,24(22):4139-4144.
[10]李志雄.基于最小二乘支持向量机的砂土液化预测方法[J].西北地震学报,2007,29(2):133-136,155.LI Zhi-xiong.Prediction method of sand liquefaction based on least square support vector machine[J].Northwestern Seismological Journal,2007,29(2):133-136,155.
[11]韩天锡,蒋淳,魏雪丽,等.多元统计组合模型在地震综合预报中的应用[J].地震学报,2004,26(5):523-528.HAN Tian-xi,JIANG Chun,WEI Xue-li,et al.Joint multivariate statistical model and its applications to synthetic earthquake prediction[J].Acta Seismologica Sinica,2004,26(5):523-528.
[12]宫凤强,李夕兵.距离判别分析法在岩体质量等级分类中的应用[J].岩石力学与工程学报,2007,26(1):190-194.GONG Feng-qiang,LI Xi-bing.Application of distance discrimniant analysis method to classification of engineering quality rock masses[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(1):190-194.
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