基于距离判别分析方法的砂土液化预测模型及应用
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摘要
基于马氏距离判别分析理论,分析了影响砂土液化的因素,选取平均粒径、不均匀系数、标准贯入击数、地下水埋深、砂层埋深、剪应力与有效上覆应力比、烈度、震中距等8个实测指标作为液化判别指标,建立距离判别分析模型对砂土液化进行预测。研究结果表明,距离判别分析模型预测性能良好,验证了该模型的科学性、高效性,并较规范法、Seed简化法等传统方法具有更高的预测精度。距离判别分析理论是解决砂土液化预测问题的有效方法之一,可以在实际工程中进行推广。
Based on the principle of Mahalanobis distance discriminant analysis,the distance analytic model was applied to the prediction of sand liquefaction with eight factors listed as follows: mean diameter,coefficient of non-uniformity,blow number of standard penetration test,underground water depth,sand depth,ratio of shearing stress to effective overburden stress,seismic intensity,epicenteral distance.Through computing practical examples and assessing the model,the model was manifested to be scientific and effective with much more accurate results than the normalization methods and simplified Seed method.It was shown that the present method was efficient in solving the prediction of sand liquefaction and could be applied to practical engineering.
Based on the principle of Mahalanobis distance discriminant analysis,the distance analytic model was applied to the prediction of sand liquefaction with eight factors listed as follows: mean diameter,coefficient of non-uniformity,blow number of standard penetration test,underground water depth,sand depth,ratio of shearing stress to effective overburden stress,seismic intensity,epicenteral distance.Through computing practical examples and assessing the model,the model was manifested to be scientific and effective with much more accurate results than the normalization methods and simplified Seed method.It was shown that the present method was efficient in solving the prediction of sand liquefaction and could be applied to practical engineering.
引文
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[16]任文杰.人工神经网络在地基土液化判别中的应用[D].天津:河北工业大学,2002.(REN Wen-jie.Application of artificial neural network in estimation and grade evaluation of foundation soil liquefaction[D].Tianjin:Hebei University of Technology,2002.(in Chinese))
[17]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.(Institute of Engineering EarthquakeResistance of China Academy of Architectural Engineering,Earthquake resistance calculation and construction measuresfor industrial and civil construction[M].Beijing:Institute of Engineering Earthquake Resistance of China Academy of Architectural Engineering,1986.(in Chinese))
[2]黄志全,姚海慧,王玲玲,等.砂土液化判别的液化系数法[J].华北水利水电学院学报,2005,26(3):47–50.(HU Zhi-quan,YAO Hai-hui,WANG Ling-ling,et al.Liquefaction coefficient method on the judgment of sandy soil liquefaction[J].Journal of North China Institute of Water Conservancy and Hydroelectric Power,2005,26(3):47–50.(in Chinese))
[3]薛新华,张我华,刘红军.基于遗传神经网络的地震砂土液化判别研究[J].西北地震学报,2006,28(1):42–45.(XUE Xin-hua,ZHANG Wo-hua,LIU Hong-jun.Research on sand liquefaction based on the genetic neural network[J].Northwestern Seismological Journal,2006,28(1):42–45.(in Chinese))
[4]李方明,陈国兴.基于BP神经网络的饱和砂土液化判别方法[J].自然灾害学报,2005,14(2):108–114.(LI Fang-ming,CHEN Guo-xing.Saturated sand liquefaction potential estimation method based on BP neural network[J].Journal of Natural Disasters,2005,14(2):108–114.(in Chinese))
[5]马骥,傅光翮,罗国煜.基于MATLAB的BP神经网络在砂土液化评价中的应用[J].水文地质工程地质,2004(2):54–58.(MA Ji,FU Guang-he,LUO Guo-yu.Application of BP neural network based on MATLAB to the evaluation of sandy soil seismic liquefaction[J].Hydrological Geology&Engineering Geology,2004(2):54–58.(in Chinese))
[6]佘跃心.砂土液化判别方法可靠性评价[J].岩土力学,2004,25(5):803–807.(YU Yue-xin.Probability evaluation of liquefaction distinguishing method of sands[J].Rock and Soil Mechanics,2004,25(5):803–807.(in Chinese))
[7]GB50011—2001建筑抗震设计规范[S].(GB50011—2001 Architectural aseismatic design criterion[S].(in Chinese))
[8]陈文化,张弥.广州地铁砂土层液化判别[J].土木工程学报,2006,39(3):118–122.(CHEN Wen-hua,ZHANG Mi.Evaluation of seismic liquefaction of sandy for Guangzhou metro[J].China Civil Engineering Journal,2006,39(3):118–122.(in Chinese))
[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 foe predicting sand liquefaction[J].Chinese Journal of Rock Mechanics and Engineering,2005,24(22):4139–4144.(in Chinese))
[10]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 Geotechnical Engineering,2006,28(3):301–305.(in Chinese))
[11]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):1942–1950.(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):1942–1950.(in Chinese))
[12]宫凤强,李夕兵.膨胀土等级分类中的距离判别分析法[J].岩土工程学报,2007,29(3):463–466.(GONG Feng-qiang,LI Xi-bing.Distance discriminant analysis method to the classification of the grade of shrink and expansion for the expansive soils[J].Chinese Journal of Geotechnical Engineering,2007,29(3):463–466.(in Chinese))
[13]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.Distance discriminant analysis method to the classification of engineering quality of rock masses[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(1):190–194.(in Chinese))
[14]范金城,梅长林.数据分析[M].北京:科学出版社,2002.(FAN Jin-cheng,MEI Chang-lin.Data analysis[M].Beijing:Science Press,2002.(in Chinese))
[15]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[J].Industrial Architecture,2002,32(11):36–39.(in Chinese))
[16]任文杰.人工神经网络在地基土液化判别中的应用[D].天津:河北工业大学,2002.(REN Wen-jie.Application of artificial neural network in estimation and grade evaluation of foundation soil liquefaction[D].Tianjin:Hebei University of Technology,2002.(in Chinese))
[17]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.(Institute of Engineering EarthquakeResistance of China Academy of Architectural Engineering,Earthquake resistance calculation and construction measuresfor industrial and civil construction[M].Beijing:Institute of Engineering Earthquake Resistance of China Academy of Architectural Engineering,1986.(in Chinese))
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