振动力作用下饱和砂土液化的模糊神经网络预测
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摘要
根据饱和砂土在振动力作用下会发生液化,而液化的程度又具有模糊性的特点,将模糊数学与神经网络算法结合,建立饱和砂土液化预测的模糊神经网络系统。该系统是1种前向多层网络,它将传统的模糊逻辑控制器基本元件和功能与具有分布学习能力的神经网络相联系,通过实际工程样本数据训练获得系统模糊推理中饱和砂土液化的基本参数,从而优化推理系统。利用模糊神经网络系统建立饱和砂土液化的模糊规则,剔除对饱和砂土液化影响不大的因素,突出对饱和砂土液化影响较大的因素。系统以饱和砂土的相对密度、标准贯入击数、上覆有效应力、振动力幅值等参数作为预测饱和砂土液化的判别指标,预测砂土在振动力作用下发生液化的可能性。应用该系统对实际工程中35个饱和砂土样本进行液化预测,其结果与工程实际有很高的符合度。
Based on the fact that the saturated sand may become liquefaction under the vibration force and the liquefaction potential has the fuzzy characteristics,the fuzzy mathematics and neural network algorithm were combined to construct a fuzzy neural network system for forecasting the liquefaction of the saturated sand.The system is a forwards-multilayer network,integrating the basic element and function of the traditional fuzzy logistic controller with the neural network that has the ability of distributed-study.It can obtain some basic parameters of the saturated sand liquefaction in the fuzzy inference through training with the practical engineering data.Thus,the inference system can be optimized.The fuzzy rules for the lique-faction of the saturated sand were set up with the fuzzy neural network system.The factors having little effect on the liquefaction of the saturated sand were eliminated and some factors having more effect on the liquefaction of saturated sand were given prominence to.Some parameters of the saturated sand,such as the relative density,the blow count of SPT,the overlaying effectiveness stress,the vibration force value,were used as the indicators to judge the liquefaction of the saturated sand and to forecast the possibility of the liquefaction of the saturated sand under the action of vibration force.The system was used in 35saturated sand samples of the practical engineering to forecast the liquefaction,and the results were of high consistency with the practical engineering.
Based on the fact that the saturated sand may become liquefaction under the vibration force and the liquefaction potential has the fuzzy characteristics,the fuzzy mathematics and neural network algorithm were combined to construct a fuzzy neural network system for forecasting the liquefaction of the saturated sand.The system is a forwards-multilayer network,integrating the basic element and function of the traditional fuzzy logistic controller with the neural network that has the ability of distributed-study.It can obtain some basic parameters of the saturated sand liquefaction in the fuzzy inference through training with the practical engineering data.Thus,the inference system can be optimized.The fuzzy rules for the lique-faction of the saturated sand were set up with the fuzzy neural network system.The factors having little effect on the liquefaction of the saturated sand were eliminated and some factors having more effect on the liquefaction of saturated sand were given prominence to.Some parameters of the saturated sand,such as the relative density,the blow count of SPT,the overlaying effectiveness stress,the vibration force value,were used as the indicators to judge the liquefaction of the saturated sand and to forecast the possibility of the liquefaction of the saturated sand under the action of vibration force.The system was used in 35saturated sand samples of the practical engineering to forecast the liquefaction,and the results were of high consistency with the practical engineering.
引文
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[12]陈守煜.模糊最优归类理论模型及其在围岩稳定性分类与场地土类别评定中的应用[J].水利学报,1993(12):26-36.(CHEN Shouyu.Fuzzy Optimal Model and Its Applications to Stability Classification of Surrounding Rocks as Well as Site Classification[J].Journal of Hydraulic Engineering,1993(12):26-36.in Chinese)
[2]陈新民,罗国煜.地震砂土液化可能性的非确定性灰色预测方法[J].桂林工学院学报,1997(2):106-109.(CHEN Xinmin,LUO Guoyu.Indeterminate Gray Prediction Method of Possibility of Sand Liquefaction during Earthquake[J].Journal of Guilin Institute of Technology,1997(2):106-109.in Chinese)
[3]叶吉芳,张固宇,陈俶季,等.模糊讯息分析法评估砂性土液化潜能[J].岩土工程学报,1996,18(1):17-20.(YEH Chifang,CHANG Kuyo,CHERN Shigi,et al.Evaluation of Liquefaction Potential of Sand by Fuzzy Informa-tion Analysis[J].Chinese Journal of Geotechnical Engineering,1996,18(1):17-20.in Chinese)
[4]赵振宇,徐用懋.模糊理论和神经网络的基础与应用[M].北京:清华大学出版社,1997.
[5]陈守煜.系统模糊决策理论与应用[M].大连:大连理工大学出版社,1994.
[6]李人厚.智能控制理论和方法[M].西安:西安电子科技大学出版社,1999.
[7]陈奎,刘兴昌.砂土地震液化的神经网络预测[J].中国地质灾害与防治学报,2000,11(1):15-18.(CHEN Kui,LIU Xinchang.The Prediction of BP Neural Network to Sand Liquefaction during Earthquake[J].The Chinese Journal of Geological Hazard and Control,2000,11(1):15-18.in Chinese)
[8]金富,祁冰,贺可强.轻亚粘土液化机制的动三轴特征与模糊综合评判[J].水文地质工程地质,1992(5):66-70.(JIN Fu,QI Bing,HE Keqiang.The Kinetic-Triaxial Character of the Fluidization Mechanism of Light Loam and Its Fuzzy-Comprehensive Evolution[J].Hydrogeology and Engineering Geology,1992(5):66-70.in Chinese)
[9]翁焕学.砂土地震液化模糊综合评判实用方法[J].岩土工程学报,1993,15(2):74-79.(WENG Huanxue,Practical Methods of Fuzzy-Comprehensive Evolution of Sand Liquefaction during Earthquake[J].Chinese Journal of Geotechnical Engineering,1993,15(2):74-79.in Chinese)
[10]陈守煜.论相对隶属度[J].大自然探索,1993(2):20-23.(CHEN Shouyu.On Relative Grade of Membership[J].Discovery of Nature,1993(2):20-23.in Chinese)
[11]陈守煜.论状态特征值(数学及其应用文集)[M].北京:气象出版社,1995.
[12]陈守煜.模糊最优归类理论模型及其在围岩稳定性分类与场地土类别评定中的应用[J].水利学报,1993(12):26-36.(CHEN Shouyu.Fuzzy Optimal Model and Its Applications to Stability Classification of Surrounding Rocks as Well as Site Classification[J].Journal of Hydraulic Engineering,1993(12):26-36.in Chinese)
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