淮安市砂土地震液化势的综合评估
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摘要
建立了BP神经网络模型LM算法的砂土地震液化判别方法,采用LVQ模式分类网络对数据进行分类,选取唐山地震时76个场地、320组现场液化勘察资料为研究对象,训练和检验网络模型的数据各160组,结果表明,该方法的砂土液化预测准确度为96.8%。根据淮安市典型场地土的钻孔资料,采用《建筑抗震设计规范法》液化判别法(规范法)、美国国家地震研究中心建议的液化判别简化方法(NCEER法)和谢君斐-陈国兴液化判别方法(谢-陈法)等3种液化判别方法及本文提出的BP神经网络模型液化判别方法对22个钻孔、120标贯点进行了液化判别,结果表明:14种地震液化判别方法的计算结果基本一致;2NCEER法比谢-陈法的计算结果要保守;3谢-陈法与规范法的计算结果很吻合,预测正确率相差不大;4规范法相对BP神经网络模型法的计算结果要保守;5BP神经网络模型法比其他3种方法的预测结果更符合实际的液化规律。最后,综合4种方法的液化判别结果给出了该地区地震液化势的评估结果,并给出了部分典型地质剖面土层地震液化势分布图。
LM algorithm of BP neural network model for estimation of sand seismic liquefaction potential is built in the paper. Sorting the data with LVQ model sorting net and selecting 320 groups of field liquefaction data of 76 sites during Tangshan earthquake in China, data of each train and test neural network model are organized in 160 groups. It shows that the estimation veracity of the method is 96.8%. Based on the drill data of typical sites in Huai′an city, the sand liquefaction potential estimation method of the BP neural network model suggested in this paper, the method of code for seismic design of building in China (Code method), the method suggested by the National Center for Earthquake Engineering Research of American (NCEER method) and the method suggested by Xie Junfei-Chen Guoxing (Xie-Chen method) are applied to the seismic liquefaction estimation of 120 standard penetration test data of 22 drills. According to the liquefaction estimation results, it can be shown that: ① the results obtained by the above four evaluation methods of sand seismic liquefaction potential are almost uniform; ② the result obtained by NCEER method is more conservative than that obtained by Xie-Chen method; ③ the result obtained by Xie-Chen method is close to that obtained by code method, and the correctness rates of the two methods are almost coincident; ④ the Code method is more conservative than the method of BP neural network model; ⑤ the results obtained by the method of BP neural network model is more identical with the field liquefaction rule than those obtained by other three methods. Considering the different estimation results of four methods, the comprehensive evaluation of sand seismic liquefaction potential in Huai′an city are obtained, enclosing the sand seismic liquefaction potential evaluation charts of some typical geology sections.
LM algorithm of BP neural network model for estimation of sand seismic liquefaction potential is built in the paper. Sorting the data with LVQ model sorting net and selecting 320 groups of field liquefaction data of 76 sites during Tangshan earthquake in China, data of each train and test neural network model are organized in 160 groups. It shows that the estimation veracity of the method is 96.8%. Based on the drill data of typical sites in Huai′an city, the sand liquefaction potential estimation method of the BP neural network model suggested in this paper, the method of code for seismic design of building in China (Code method), the method suggested by the National Center for Earthquake Engineering Research of American (NCEER method) and the method suggested by Xie Junfei-Chen Guoxing (Xie-Chen method) are applied to the seismic liquefaction estimation of 120 standard penetration test data of 22 drills. According to the liquefaction estimation results, it can be shown that: ① the results obtained by the above four evaluation methods of sand seismic liquefaction potential are almost uniform; ② the result obtained by NCEER method is more conservative than that obtained by Xie-Chen method; ③ the result obtained by Xie-Chen method is close to that obtained by code method, and the correctness rates of the two methods are almost coincident; ④ the Code method is more conservative than the method of BP neural network model; ⑤ the results obtained by the method of BP neural network model is more identical with the field liquefaction rule than those obtained by other three methods. Considering the different estimation results of four methods, the comprehensive evaluation of sand seismic liquefaction potential in Huai′an city are obtained, enclosing the sand seismic liquefaction potential evaluation charts of some typical geology sections.
引文
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[6]郭晶,杨章玉.MATLAB6.5辅助神经网络分析与设计[M].北京:电子工业出版社,2004.64-69
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[2]T.L.Youd,I.M.Idriss.Liquefaction Resistance of soils:Summary Reprot form the1996NCEER and1998NCEER/NSF workshops on evaluation of lique-faction resistance of soils[J].Journal of Geotechnical and Geoenvionmental Engeering.2001,(4):297-311
[3]陈国兴,胡庆兴,刘雪珠.关于砂土液化判别的若干意见[J].地震工程与工程振动,2002,(1):141-151
[4]陈国兴,张克绪,谢君斐.液化判别的可靠性研究[J].地震工程与工程振动,1991,11(2):85-96
[5]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:铁道部铁道科学研究所,1983.26-41
[6]郭晶,杨章玉.MATLAB6.5辅助神经网络分析与设计[M].北京:电子工业出版社,2004.64-69
[7]闻新.MATLAB神经网络应用设计[M].北京:北京科学出版社,2000.89-109
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