饱和砂土地震液化判别的分形插值模型

借鉴分形基本理论,提出了基于分形插值模型的饱和砂土地震液化判别方法.该方法首先选取影响饱和砂土地震液化判别的7个主要因素,根据分类标准,采用在每级标准中随机内插的方法,得到40个标准样本,用于构建饱和砂土地震液化判别的分形插值模型;其次根据最大似然分类原则确定每个饱和砂土地震液化判别指标的评价分维数;然后利用加权求和法计算样本的综合评价值,并根据样本综合评价值与经验等级之间的关系建立分形插值评价模型;最后,进行了实例分析结果表明:该模型的评价结果合理、客观,计算得到的每个样本具体得分值,即使对属于同一级的样本也可以给出其地震液化程度的顺序,为饱和砂土地震液化评价工作提供了一种新的研究方法与思路.     

基于遗传神经网络的地震砂土液化判别研究

针对BP人工神经网络具有易陷入局部极小等缺陷,本文提出了将遗传算法与神经网络结合,同时优化网络结构的权值与阈值的思想,建立了砂土液化判别的遗传神经网络模型。根据地震液化的实测资料,分别对BP〗神经网络判别结果和遗传神经网络判别结果进行了比较,结果表明后者比前者判别能力要好些。     

基于遗传神经网络的砂土液化判别模型

针对BP人工神经网络具有易陷入局部极小等缺陷，本文提出了将遗传算法与神经网络相结合，同时优化网络结构与权值、阈值的思想。根据地震液化的实测资料，建立了砂土液化判别的遗传神经网络模型，比较计算结果证明了该模型的科学性、高效性。文中并进行主成分分析，提出液化影响的主要因素。     

基于支持向量机的砂土液化预测分析

将支持向量机方法应用于砂土地震液化预测问题.考虑影响砂土液化的因素,选用震级、标贯击数、相对密实度、土层埋深、地震历时、地面运动峰值加速度和震中距7个影响因子作为液化判别指标,建立了砂土液化预测的支持向量机模型.以砂土液化实测数据作为学习样本进行训练,建立相应函数对待判样本进行分类.研究结果表明:支持向量机模型分类性能良好,是砂土地震液化预测的一种有效方法,可以在实际工程中进行推广.     

基于Finn本构模型的饱和砂土地震液化分析

天然地震作用下的饱和砂土液化问题是岩土地震工程研究的重要课题之一。目前,国内推荐使用的规范法是基于实际地震液化调查而建立的判别方法,方法本身缺乏理论基础。采用Finn液化本构关系建立了砂土液化数值分析模型,运用有限差分法的动力时程分析模块,分析了饱和砂土地基的地震液化问题。结果表明,将Finn本构模型应用于砂土液化分析,可以较好地给出地震作用过程中孔隙水压力和有效应力变化的规律。     

基于RS-PCA-GA-SVM的砂土液化预测方法研究

砂土液化是一种危害性比较大的自然灾害,对砂土液化进行判定预测在地质灾害防治领域中有重要的研究意义。通过粗糙集理论(Rough Set,RS)对影响砂土液化的6个初始评价指标(包括震级、土深、震中距、地下水位、标贯击数和地震持续时间)进行属性约简,去掉冗余或干扰信息,得到基于4个核心预测指标的数据集。通过主成分分析法(Principal Component Analysis,PCA)从核心评价指标中提取出主成分,采用支持向量机(Support Vector Machine,SVM)对数据集进行训练,用遗传算法(Genetic Algorithm,GA)优化参数,建立砂土液化的RS-PCA-GA-SVM预测模型。并结合砂土液化实际数据将预测结果与基于Levenberg-Marquardt算法改进的BP神经网络模型(LM-BP)的预测结果做比较。实例计算表明:基于RS-PCA-GA-SVM模型得到的砂土液化预测结果精度较LM-BP神经网络有很大的提高,判别结果与实际情况比较吻合,可在实际工程中应用。     

液化判别的可靠性研究

本文根据收集到的30次地震800余例砂土和粉土液化调查资料,采用优化方法建立和改进了砂土和粉土的液化判别式,其中包括应力比比值法、能量法、规范法和简化应力比比值法的液化判别式。文中首次提出液化判别可信度的概念,指出可信度与回判成功率的区别,说明可信度能更恰当地度量液化判别结果的可靠性。文中应用信息熵理论,提出综合评价液化判别式好坏程度的定量指标。应用上述方法研究优化后的液化判别式可靠性发现,它们对砂土液化判别的适用性是好的,对粉土液化判别的适用性是理想的。但应指出,当以烈度表示地震作用强度时,规范法的适用性是好的,并对10—15m深的砂土或粉土的液化判别也是适用的;当以加速度表示地震作用强度时,用规范法判别液化则遇到了困难。本文引进模糊烈度概念,修改了规范法。修改后的规范法能强好地适用于已知地面加速度情况下的液化判别。 一个场址所受到的地震作用强度可用烈度、地面加速度、震级和震中距等指标表示。本文给出的几种液化判别方法分别适用于上述不同情况。     

河流沉积相土层结构对砂土液化的影响研究

目前相关规范主要依据工程场地单点的测试数据进行砂土液化判别,而实际的三维土层结构可能非常复杂。研究土层结构对砂土液化的影响机制,有利于提高砂土液化判别结果准确度。分析2008 年5 月28 日发生的松原M_S地震和2010—2011 年新西兰坎特伯雷地震序列中砂土液化点的分布,结果显示:砂土液化点主要位于高弯度河流的沉积相地层,凹岸侧蚀、凸岸沉积形成的边滩具有典型的二元结构,其顶部分布的黏土类不透水层有利于下伏饱和粉细砂等易液化土层的超孔隙水压累积;而辫状河流沉积相中,上覆黏土类不透水层间断分布特征明显。针对河流不同沉积相的土层结构建立简化场地模型,使用FLAC3D 进行砂土液化数值模拟,揭示出不同土层结构中超孔隙水压力的累积、消散和渗流过程机制,结果表明,河流沉积相土层结构对砂土液化场点的分布和地表变形具有显著影响。在合理的工程地质分区基础上,现有的液化判别方法有必要考虑场地的土层结构的影响。      

黄土场地地震液化研究

通过往返加荷动三轴试验，对兰州某民用机场扩建工程场地的饱和黄土和砂土进行了液化试验。在试验结果的基础上，运用抗液化剪应力判别方法、地震危险性分析计算结果以及根据有限元一维模型计算求得的该场地的地震剪应力，对该场地饱和黄土和砂土的地震液化进行了综合判断。结果表明，在未来遭受到50年10％超越概率的地震作用时，该场地的饱和黄土比饱和砂土更容易发生液化。     

基于液化SPT基准值的液化势估计

基于神经网络BP模型和可靠度理论,并沿用抗震规范中液化标准贯入锤击数基准值概念,建立了简化的液化判别概率方法。文中以液化标准贯入锤击数作为估计液化势的基本依据。该基准值是给定地面加速度、土层埋深、地下水位的液化临界锤数,也与震级大小和液化概率有关。为了对不同震级和土层中任一点进行液化判别,引入土层埋深水位以及震级大小对基准值的修正系数。为了方便工程应用,也给出了按地震分组的液化判别方法。     

Fuzzy neural network models for liquefaction prediction

Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined.     

Unsupervised fuzzy neural network structural active pulse controller

Applying active control systems to civil engineering structures subjected to dynamic loading has received increasing interest. This study proposes an active pulse control model, termed unsupervised fuzzy neural network structural active pulse controller (UFN‐SAP controller), for controlling civil engineering structures under dynamic loading. The proposed controller combines an unsupervised neural network classification (UNC) model, an unsupervised fuzzy neural network (UFN) reasoning model, and an active pulse control strategy. The UFN‐SAP controller minimizes structural cumulative responses during earthquakes by applying active pulse control forces determined via the UFN model based on the clusters, classified through the UNC model, with their corresponding control forces. Herein, we assume that the effect of the pulses on structure is delayed until just before the next sampling time so that the control force can be calculated in time, and applied. The UFN‐SAP controller also averts the difficulty of obtaining system parameters for a real structure for the algorithm to allow active structural control. Illustrative examples reveal significant reductions in cumulative structural responses, proving the feasibility of applying the adaptive unsupervised neural network with the fuzzy classification approach to control civil engineering structures under dynamic loading. Copyright © 2001 John Wiley & Sons, Ltd.     

基于组合权重的模糊物元模型在砂土液化势评价中的应用

目前,砂土液化问题仍是岩土工程界重要的研究方向.鉴于砂土液化评价的复杂性及其影响因素的不确定性和模糊性,将熵值理论与模糊集和贴近度相结合,建立了基于熵权与专家打分法所获权重的组合权重的模糊物元模型,有效地避免了权重分配困难的问题.该模型以一种新的方法进行砂土液化势的评价,并且结合实例应用,通过与现场标贯试验对比,进一步验证了该模型的合理、简便和实用.     

Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data

Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. Derived from several field and laboratory tests, various simplified procedures such as stress-based, strain-based, Chinese criteria, etc. have been developed by utilizing case studies and undisturbed soil specimens. In order to address the collective knowledge built up in conventional liquefaction engineering, an alternative general regression neural network model is proposed in this paper.To meet this objective, a total of 620 sets of data including 12 soil and seismic parameters are introduced into the model. The data includes the results of field tests from the two major earthquakes that took place in Turkey and Taiwan in 1999 and some of the desired input parameters are obtained from correlations existing in the literature.The proposed GRNN model was developed in four phases, mainly: identification phase, collection phase, implementation phase, and verification phase. An iterative procedure was followed to maximize the accuracy of the proposed model. The case records were divided randomly into testing, training, and validation datasets.Generating a model that takes into account of 12 soil and seismic parameters is not feasible by using simplified techniques; however, the proposed GRNN model effectively explored the complex relationship between the introduced soil and seismic input parameters and validated the liquefaction decision obtained by simplified methods. The proposed GRNN model predicted well the occurrence/nonoccurrence of soil liquefaction in these sites. The model provides a viable tool to geotechnical engineers in assessing seismic condition in sites susceptible to liquefaction.     

A neurofuzzy system to analyze liquefaction-induced lateral spread

Lateral spreads of liquefied granular soil masses have caused severe damages to many engineered structures. Accordingly, many empirical procedures have been developed from field-direct observations and from multiple regression analyses carried out on the database gathered from many case histories. The intricacy and nonlinearity of the underlying phenomena makes the above approaches somewhat unreliable for estimating liquefaction-induced lateral spreads. The database has inconsistencies and contradictions because of inevitable subjective interpretations and neural network approaches have been proposed for dealing with these.To overcome these difficulties in this paper a hybrid system named neurofuzzy, which profits from fuzzy and neural paradigms, is advanced. The resulting model called NEFLAS (NEuroFuzzy estimation of liquefaction induced LAteral Spread) is shown to yield a much improved forecasting than both multiple regression and neural network procedures. The corresponding software can be obtained from the first author.     

Seismic liquefaction potential assessment by using relevance vector machine

Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine (RVM) to determine the liquefaction potential of soil by using actual cone penetration test (CPT) data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artifi cial neural network (ANN) model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.     

Assessment of liquefaction triggering using strain energy concept and ANN model: Capacity Energy

In the present study, an artificial neural network (ANN) model was developed to establish a correlation between soils initial parameters and the strain energy required to trigger liquefaction in sands and silty sands. A relatively large set of data including 284 previously published cyclic triaxial, torsional shear and simple shear test results were employed to develop the model. A subsequent parametric study was carried out and the trends of the results have been confirmed via some previous laboratory studies. In addition, the data recorded during some real earthquakes at Wildlife, Lotung and Port Island Kobe sites plus some available centrifuge tests data have been utilized in order to validate the proposed ANN-based liquefaction energy model. The results clearly demonstrate the capability of the proposed model and the strain energy concept to assess liquefaction resistance (capacity energy) of soils.     

Classified real-time flood forecasting by coupling fuzzy clustering and neural network

This paper presented a new classified real-time flood forecasting framework by integrating a fuzzy clustering model and neural network with a conceptual hydrological model. A fuzzy clustering model was used to classify historical floods in terms of flood peak and runoff depth, and the conceptual hydrological model was calibrated for each class of floods. A back-propagation (BP) neural network was trained by using real-time rainfall data and outputs from the fuzzy clustering model. BP neural network provided a rapid on-line classification for real-time flood events. Based on the on-line classification, an appropriate parameter set of hydrological model was automatically chosen to produce real-time flood forecasting. Different parameter sets was continuously used in the flood forecasting process because of the changes of real-time rainfall data and on-line classification results. The proposed methodology was applied to a large catchment in Liaoning province, China. Results show that the classified framework provided a more accurate prediction than the traditional non-classified method. Furthermore, the effects of different index weights in fuzzy clustering were also discussed.