基于GRNN的砂土液化危害等级评价模型研究
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摘要
影响砂土地震液化的因素复杂且具有随机性和不确定性。神经网络方法不仅能考虑定量因素,而且能考虑定性因素的影响,因而神经网络方法适用于解决非确定性的砂土地震液化评价问题。在分析广义回归神经网络的基本原理和算法基础上,建立了砂土液化危害等级评价的广义回归神经网络模型。然后用收集到的工程实例样本对该模型进行训练和检验,并与BP神经网络判别结果进行对比。结果表明,广义回归神经网络性能良好、预测精度高,是砂土地震液化危害等级评价的一种有效方法。
The factors which control and affect sand liquefaction,are random and uncertain.Because artificial neural network can consider both quantitative and qualitative factors,it is suitable for solving uncertain problem of the evaluation of sand soil liquefaction.A generalized regression neural network model for evaluating sand liquefaction hazard degree was established based on the basic principles and algorithms,and was trained and checked with the collected sand liquefaction examples and compared with results obtained by the BP neural network.The results show that the generalized regression neural network model presents excellent network performance,high prediction accuracy,and is an effective way to evaluate the sand liquefaction hazard degree.
The factors which control and affect sand liquefaction,are random and uncertain.Because artificial neural network can consider both quantitative and qualitative factors,it is suitable for solving uncertain problem of the evaluation of sand soil liquefaction.A generalized regression neural network model for evaluating sand liquefaction hazard degree was established based on the basic principles and algorithms,and was trained and checked with the collected sand liquefaction examples and compared with results obtained by the BP neural network.The results show that the generalized regression neural network model presents excellent network performance,high prediction accuracy,and is an effective way to evaluate the sand liquefaction hazard degree.
引文
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[2]Dobry R,Ladd R S,Yokel F Y,et al.Prediction of pore water pressure buildup and liquefaction of sands during earthquakes by the cyclic strain method[R].NBS Building Science Series138,US Department of Commerce,National Bureau of Standards,1982.
[3]Davis R O,Berrill J B.Energy dissipation and seismic liq-uefaction in sands[J].Earthquake Engineering and Struc-ture Dynamics,1982,10(1):68-70.
[4]Chen Guoxing,Li Fangming.Probabilistic estimation of sand liquefaction based on neural network model of radial basisfunction[J].Chinese Journal of Geotechnical Engineering,2006,28(3):301-305.[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.]
[5]Xue Xinhua,Zhang Wohua,Liu Hongjun.Comprehensive fuzzy evaluation method for sandliquefaction[J].Journal of Chongqing Jianzhu University,2006,28(1):55-58.[薛新华,张我华,刘红军.砂土地震液化的模糊综合评判法[J].重庆建筑大学学报,2006,28(1):55-58.]
[6]Yan Weiming,Qiao Yaling,He Haoxiang.Application of rough set theory to prediction of seismic damage[J].Journal of Natural Disasters,2006,15(3):147-151.[阎维明,乔亚玲,何浩祥.粗糙集理论在震害预测中的应用[J].自然灾害学报,2006,15(3):147-151.]
[7]Liu Zhangjun,Ye Liaoyuan,Peng Gang.Fuzzy probability comprehensive evaluation method for sand liquefaction dur-ing earthquake[J].Rock and Mechanics,2008,29(4):876-880.[刘章军,叶燎原,彭刚.砂土地震液化的模糊概率评判方法[J].岩土力学,2008,29(4):876-880.]
[8]Liu Hongjun,Xue Xinhua.Artificial neural network model for prediction of seismic liquefaction of sandsoil[J].Rock and Mechanics,2004,25(12):1942-1946.[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.]
[9]Xia Jianzhong,Luo Zhanyou,Gong Xiaonan,et al.Support vector machine model for prediction sandliquefaction[J].Chinese Journal of Rock and Engineering,2005,24(22):4139-4144.[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.]
[10]The Committee on Soil Dynamics of the Geotechnical Engi-neering Division.Definition of terms related to liquefaction[J].Journal of the Geotechnical Engineering Division,ASCE,1978,104(GT9):1179-1200.
[11]钱家欢,殷宗泽.土工原理与计算[M].第2版.北京:中国水利水电出版社,1996.
[12]Ren Jingang,Wang Yufang.The research method summary of sand behavior under earthquakeexcitation[J].HaiHe Water Resources,2006(3):51-53.[任金刚,王玉芳.饱和砂土地震液化研究方法概述[J].海河水利,2006(3):51-53.]
[13]Specht D F.A general regression neuralnetwork[J].IEEE Transactions on Neural Network,1991,2(6):568-576.
[14]中华人民共和国建设部.建筑抗震设计规范(GB50011-2001)[S].北京:中国建筑工业出版社,2001.
[15]Ren Wenjie.Application of artificial neural network in es-timation and grade evaluation of foundation soil liquefaction[D].Tianjin:Hebei University of Technology,2002.[任文杰.人工神经网络在地基土液化判别及等级评价中的应用[D].天津:河北工业大学,2002.]
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