考虑竖向应力的土-水特征曲线在土石坝稳定渗流分析中的应用

本文首先基于砾石土料实测的不同竖向应力作用下的土-水特征曲线,预测对应的渗透系数曲线,并通过饱和-非饱和稳定渗流分析对不同各向异性程度的均质土坝采用有无竖向应力作用下的土-水特征曲线和渗透系数曲线计算所得的渗流场进行比较。结果表明:采用有竖向应力作用下的曲线计算的渗流场饱和区范围较大,不同部位的孔隙水压力变化的规律不同,但坝体各向异性程度越大,坝体中的孔隙水压力值受竖向应力的影响越小;无论有无竖向应力作用,坝体的各向异性程度均对坝料的水力特性无明显影响,但有无竖向应力作用时的体积含水率差异较大。     

土石坝砾质粘土的击实曲线研究

通过室内击实试验我们可以测定土的最大干密度和最优含水率,以此成为压实度检测的依据,以便控制好填筑土体的压实质量,更好地保障工程质量达到所规定的要求。不同土体,不同的操作方法,击实试验的结果是不同的,本文章结合苗尾水电站料场的土料的特性,通过对比颗粒含量与界限含水率的不同,分析击实曲线的变化规律。     

粗粒土渗透特性影响因素及渗透规律试验研究

影响粗颗粒土渗透特性的因素有很多。通过室内渗透试验,采用不同的试验条件,研究了粗颗粒土的干密度ρd、细料含量P5、孔隙比e和不均匀系数C u对渗透系数的影响,分析了粗颗粒土的干密度ρd、细料含量P5、孔隙比e和不均匀系数C u与渗透系数之间的关系,建立了粗颗粒土的干密度ρd、细料含量P5、孔隙比e和不均匀系数C u与渗透系数之间的关系式。     

不同压实度下膨胀土表面裂隙演化规律机理研究

膨胀土裂隙的发生发展对不少工程造成恶劣影响,为探究压实度在膨胀土裂隙演化过程中的作用机理,通过室内试验来研究不同压实度下膨胀土表面裂隙演化规律。利用称重拍照装置和Matlab编写图像处理程序[1],进行了1.5g/cm3、1.6g/cm3、1.7g/cm3三种干密度试样下的湿干循环试验,脱湿温度均为75℃。结果显示,不同干密度试样其对应的水分蒸发速度不同,蒸发速度的不同又影响着裂隙的发展[2];压实度越低,蒸发速度越快,裂隙发育就越快,面积就越大,同时试样被裂隙切割地也就越支离破碎;随着湿干循环次数的增加,裂隙率也会随着增加[3],但第二次增加的幅度最大。研究结果为膨胀土等相关问题的研究提供理论参考。     

纤维风积沙土归一化特性分析

基于纤维风积沙土的不固结不排水三轴剪切试验,为实现应变硬化型纤维风积沙土应力—应变关系的归一化,基于应变硬化型典型的双曲线特征,提出了以固结围压作为归一化因子。通过以固结围压为归一化因子,对纤维风积沙土应力—应变特性进行归一化分析和比较表明,纤维风积沙土的应力-应变特性归一化程度更高,效果更好。同时,建立了应变硬化型纤维风积沙土应力—应变特性的归一化方程。     

公路路基土最佳含水量和最大干密度的确定

由于筑路材料结构层次等因素的不同,确定室内标准密度的方法也多样化,有些方法需在实践中进一步完善。最大干密度是指标准击实曲线上最大的干密度值,该值对应的含水量即为最佳含水量。现主要对击实试验法进行分析。     

土的室内承载比(CBR)试验检测及应用

土是路基施工中重要原材料之一,室内承载比(CBR)是路基土和路面的强度指标,本文通过制作不同干密度的CBR试件,经过4昼夜饱水后检测膨胀量并做贯入试验,得到不同击实度时对应的土的CBR值关系。根据击实度与CBR关系图进一步判断所选择的土源能否满足设计要求,为建设工程选择土源提供依据。     

冻融循环作用下细粒土抗剪强度特性参数的研究

新疆地质条件复杂以及春秋季温度变差较大,使得细粒土冻融循环前、后土体的物理、力学性质发生变化,现通过室内冻融循环试验和剪切试验,对细粒土冻融循环前、后抗剪强度特性参数变化进行研究,对不同冻融循环温度变差范围及不同冻融循环次数条件下,细粒土强度特性参数粘聚力c、内摩擦角Ф值的变化规律进行揭示。     

潜油电机测试装置性能研究

本文对潜油电机的测试系统进行设计,通过所设计的测试系统全面检测潜油电机的各项性能指标进行多次试验。所做的测试通过潜油电机型式试验数据分析系统进行数据的采集,通过系统对试验数据进行分析后,将分析结果导出通过多项式拟合方式、拉格朗日方程和牛顿方程拟合方式绘制拟合曲线。通过对这些数据以及曲线的分析,可以判断被检产品是否符合设计要求、品质的优劣以及改进的目标和方向。     

浅议低液限弱膨胀土在二灰土的处理应用

针对底液限弱膨胀土在二灰土基层施工潮湿状态下具有很强的塑性,晾晒含水量降低缓慢,膨胀土含有砂浆石粉碎困难,导致二灰土灰剂量拌合后不均匀,成型后强度、压实度离散性大,表面不易打碎的土颗粒遇水膨胀影响外观质量,后期养护不及时容易干缩裂缝,经过工地现场试验、施工工艺对比总结,总结对该种土的处理方法,有效地保证了二灰土的内在、外在质量,消除了干缩裂缝的产生。     

Numerical issues in threshold autoregressive modeling of time series

This paper analyses the contribution of various numerical approaches to making the estimation of threshold autoregressive time series more efficient. It relies on the computational advantages of QR factorizations and proposes Givens transformations to update these factors for sequential LS problems. By showing that the residual sum of squares is a continuous rational function over threshold intervals it develops a new fitting method based on rational interpolation and the standard necessary optimality condition. Taking as benchmark a simple grid search, the paper illustrates via Monte Carlo simulations the efficiency gains of the proposed tools.     

Nonlinearity measures: a case study

Summary An important problem in applied statistics is fitting a given model function f (β) with unknown parameters β to a data vector y. Minimizing the residual sum of squares provides the least squares estimates of β. If f (β) is linear in β the precision of these estimates is well-known. In a nonlinear case approximate (though asymptotically exact) confidence statements can be made. B eale [1] introduced measures of nonlinearity which can be used to indicate when approximate confidence statements are appropriate. G uttman and M eeter [2] showed that in some, severely nonlinear, cases Beale's measures do not give the right indication. In this paper two new nonlinearity measures are introduced and their use is illustrated on a practical problem described by W itt [3]. A more detailed discussion of the theoretical background can be found in references [1] and [2].     

Fit performance of several programs used for non-linear least squares problems

Seven computerprograms for non-linear regression or curve fitting problems are compared. The comparison of the programs, running in different computing centra, is restricted to the fit performance. Six model functions are fitted according to the least squares criterion to data series, arising from practical work. The special least squares minimization programs turned out to be better suited for these problems than general optimizing programs.     

On Model Selection Curves

Many popular methods of model selection involve minimizing a penalized function of the data (such as the maximized log-likelihood or the residual sum of squares) over a set of models. The penalty in the criterion function is controlled by a penalty multiplier λ which determines the properties of the procedure. In this paper, we first review model selection criteria of the simple form “Loss + Penalty” and then propose studying such model selection criteria as functions of the penalty multiplier. This approach can be interpreted as exploring the stability of model selection criteria through what we call model selection curves. It leads to new insights into model selection and new proposals on how to select models. We use the bootstrap to enhance the basic model selection curve and develop convenient numerical and graphical summaries of the results. The methodology is illustrated on two data sets and supported by a small simulation. We show that the new methodology can outperform methods such as AIC and BIC which correspond to single points on a model selection curve.     

用模拟退火算法寻找Heston期权定价模型参数

随机波动率模型由于放松了Black-Sholes模型的假定而更符合市场情况,因此成为研究金融衍生品定价的热点。Heston随机波动率不同于其他随机波动率模型之处在于其存在闭形式解。Heston期权定价模型在应用中需要确定五个待估参数,此问题通常比较困难。本文采用模拟退火算法并利用最小化残差平方和来估算,该算法以一定概率跳出局部极小值,从而以概率1收敛到全局极小值,最终得到Heston模型的待估参数。在实证研究中,本文利用香港恒生股票指数期权在2010年10月15日交易的数据,得到待估参数,并用该参数对2010年10月18日期权进行了模拟定价。     

Recursions for the two-stage least-squares estimators

This paper presents recursion formulae for the two-stage least-squares estimators of the structural coefficients in a simultaneous equation model and for the residual sum of squares used in estimating the asymptotic covariance matrix. Included are formulae for updating estimates when a new set of observations is obtained and for revising estimates when a set of observations is discarded. The recursion formulae should prove to be of both practical and theoretical interest to econometricians.     

On multiple regression analysis

The sums of squares associated with the independent variables in a multiple regression equation depend on the order in which these variables are introduced. Two methods have been proposed in the literature to avoid this inconvenience: "forward selection" or "backward elimination".
With forward selection the independent variables are introduced in successive stages. The order is not predetermined but at each stage that variable is taken as the next one which produces the highest reduction in the residual sum of squares of the dependent variable.
With backward elimination on the other hand, we start with the complete regression equation and eliminate the independent variables from it in the order in which they produce the smallest increases in the residual sum of squares.
This paper describes a simple and convenient computational lay-out which can be used for both procedures. In forward selection we start with the matrix of product sums, and in bacward elimination we work from the inverse matrix.
In addition these techniques are applied to a variety of practical examples in order to see what results they lead to and what pitfalls may be encountered.     

Instrumental variable estimation of nonseparable models

There are many environments where knowledge of a structural relationship is required to answer questions of interest. Also, nonseparability of a structural disturbance is a key feature of many models. Here, we consider nonparametric identification and estimation of a model that is monotonic in a nonseparable scalar disturbance, which disturbance is independent of instruments. This model leads to conditional quantile restrictions. We give local identification conditions for the structural equations from those quantile restrictions. We find that a modified completeness condition is sufficient for local identification. We also consider estimation via a nonparametric minimum distance estimator. The estimator minimizes the sum of squares of predicted values from a nonparametric regression of the quantile residual on the instruments. We show consistency of this estimator.     

一种寻找Heston期权定价模型参数的新方法

Heston随机波动率模型的期权定价比Black-Sholes模型更符合市场情况,是金融衍生品定价研究的热点。但应用时需要确定五个待估参数,参数的确定属于组合优化问题,此问题的求解通常比较困难。本文利用遗传算法解决该优化问题,从而得到Heston模型的待估参数。该算法避免丢失最优解,具有群体搜索的特点,有着很好的概率跳出局部极小值,从而以概率1收敛到全局极小值。在实证研究中,利用香港恒生股票指数期权在2014年6月10日和2014年6月25日交易的数据为样本,得到待估参数,并用该参数对2014年6月12日的买入期权和2014年6月27日的卖出期权进行了模拟定价。数值结果与进化过程表明本文方法的有效性和可行性。     

指数曲线模型在公路路基沉降预测中的应用研究

在软基上修建高速公路时,为保证公路质量一般需要对地基进行加固,为检验加固效果和优化施工,可根据实测数据对沉降发展进行预测,因此选用经验公式法的指数曲线模型,提出利用Origin软件的拟合功能对实测数据进行拟合,计算得到最终沉降、剩余沉降和沉降发展曲线,评价地基处理效果以及指导施工。