基于减法聚类模糊神经网络的砂土液化势判别
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摘要
砂土地震液化问题是岩土地震工程学的重要研究课题之一。在分析模糊神经网络原理的基础上,利用减法聚类算法对自适应模糊推理系统进行优化,并建立了砂土地震液化的模糊神经网络模型。然后,将该模型用于实际工程的砂土液化判别中,并与传统砂土液化判别方法结果进行对比。判别结果表明:文中建立的模糊神经网络模型具有较强的学习功能,用于砂土地震液化判别中是可行的和有效的。
Sand liquefaction is one of important issues in the research field of geotechnical earthquake engineering.The adapted fuzzy reasoning system is optimized by adopting the algorithm of subtraction clustering based on the analysis of fuzzy neural network,and an adaptive fuzzy neural network model of sand liquefaction is proposed and applied to an actual engineering.The forecasted results show that the proposed model in this article is feasible and effective.
Sand liquefaction is one of important issues in the research field of geotechnical earthquake engineering.The adapted fuzzy reasoning system is optimized by adopting the algorithm of subtraction clustering based on the analysis of fuzzy neural network,and an adaptive fuzzy neural network model of sand liquefaction is proposed and applied to an actual engineering.The forecasted results show that the proposed model in this article is feasible and effective.
引文
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[2]薛新华,陈群.基于GRNN的砂土液化危害等级评价模型研究[J].四川大学学报:工程科学版,2010,42(1):42-47.XUE Xinhua,CHEN Qun.Study on hazard degree evaluation of sand liquefaction based on the generalized regression neural network[J].Journalof Sichuan University(Engineering Science Edition),2010,42(1):42-47.(in Chinese)
[3]石兆吉,张荣祥,顾宝和.砂土液化判别和评价综合方法研究[J].地震工程与工程振动,1997,17(1):82-88.SHI Zhaoji,ZHANG Rongxiang,GU Baohe.Study on synthetic methods for criteria and appraisal of sand liquefaction[J].Journal of EearthquakeEngineering and Engineering Vibration,1997,17(1):82-88.(in Chinese)
[4]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.XUE Xinhua,ZHANG Wohua,LIU Hongjun.Comprehensive fuzzy evaluation method for sand liquefaction[J].Journal of Chongqi Jianzhu Univer-sity,2006,28(1):55-58.(in Chinese)
[5]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 Guoxing,LI Fangming.Probabilistic estimation of sand liquefaction based on neural network model of radial basis function[J].ChineseJournal of Geotechnical Enigneering,2006,28(3):301-305.(in Chinese)
[6]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 Hongjun,XUE Xinhua.Artifical neural network model for prediction of seismic liquefaction of sand soil[J].Rock and Soil Mechanics,2004,25(12):1942-1946,1950.(in Chinese)
[7]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 Fangming,CHEN Guoxing.Saturated sand liquefaction potential estimation method based on BP neural network[J].Journal of Natural Disas-ters,2005,14(2):108-114.(in Chinese)
[8]Takagit,Sugeno M.Fuzzy identification of systems and its applications to modeling and control[J].IEEEE Transactions on Systems,Managen-ment and Cybernetics,1985,15(1):116-132.
[9]Chiu S L.Fuzzy model identification based on duster estimation[J].Journal of Intelligent and Fuzzy Systems,1994,2(3):267-278.
[10]Seed H B,Idriss I M.Simplified procedure for evaluating soil liquefaction potential[J].Journal of the Soil Mechancis and Foundations Division,ASCE,1971,97(9):1249-1273.
[11]Seed H B,Tokimatsu K,Harder L F,et al.Influence of SPT procedures in soil liquefaction resistence evaluations[J].Journal of GeotechnicalEnigeering Division,ASCE,1985,111(12):1425-1445.
[12]Liao S C C,Veneziano D,Whitman R V.Regression models for evaluating liquefaction probability[J].Journal of the Geotechnical Engineering,ASCE,1988,114(4):389-411.
[13]GB50011-2010建筑抗震设计规范[S].GB50011-2010 Code for Seismic Design of Building[S].(in Chinese)
[14]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.REN Wenjie.Application of artificial neural network in estimation and grade evaluation of foundation soil liquefaction[D].Tianjin:Hebei Uni-versity of Technology,2002.(in Chinese)
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