基于非局部Biot理论的平面波作用下饱和地基动力响应

为探究孔隙尺寸效应对饱和土地基动力响应的影响,基于非局部-Biot理论,构建了P波及SV波作用下饱和土地基表面动力响应的计算模型.采用波函数展开法,求解了地表位移及应力的解析解.分析了孔隙尺寸(以非局部参数描述)、入射波频率及入射角对地表位移及应力响应的影响.结果表明:低频下,非局部Biot理论计算结果与经典Biot理论计算结果基本一致;高频时,地表位移及应力随非局部参数变化较为明显,即高频率下,孔隙尺寸效应对地表响应影响不可忽略.入射波频率对地表响应的影响与孔隙尺寸有关,孔隙越大,频率影响越为显著.相同幅值的P波及SV波作用下,SV波引起的地表动力响应大于P波,且SV波在入射角为45°时发生全反射.研究成果可为半空间饱和土中波动问题的研究提供借鉴.

P波作用下土体孔隙尺寸及结构非均匀效应对圆形衬砌动力特性的影响

基于应变梯度非局部Biot理论,以深埋圆形衬砌为研究对象,利用波函数展开法和饱和土与衬砌间的边界条件,得到隧道衬砌在P波作用下的动力响应解析解。研究了不同入射P波频率下,非局部参数与尺寸因子对动应力集中系数(DSCF)的影响规律。结果表明,入射波频率较低时,非局部参数和尺寸因子对DSCF几乎没有影响,随着入射波频率的增大,非局部参数和尺寸因子对DSCF的影响会越来越明显,且非局部参数与DSCF呈负相关,尺寸因子与DSCF呈正相关;衬砌内动应力最大处均出现在衬砌右侧;随着频率的增加,衬砌内DSCF沿径向呈现明显的波动,非局部参数和尺寸因子对衬砌内环向应力的分布模式影响不大。

一种有效的机载双频干涉地形高程重建方法

双频联合解缠不需要满足Iton假设,因此双频干涉可以有效地提取地形起伏较大区域的高程信息。针对目前双频干涉处理中高程重建精度低的问题,该文提出了一种有效的双频干涉SAR地形高程重建方法。该方法对常规处理流程中的关键步骤进行了改进,首先在不同波段配准之前采用非局部参数估计(NL-InSAR)技术对幅度图、相干系数、干涉相位进行精确估计,利用各个波段滤波后的幅度信息来实现不同波段的干涉相位的配准。然后采用聚类分析技术对联合解缠相位标记有效点和噪点,并利用这些有效点对联合解缠相位进行均值滤波。用于实验的机载实测数据包括同一场景的C波段和X波段主、辅SAR图像复数据,在针对实测数据处理中,该方法取得了较好的高程重建结果。

Parametric modeling of carbon nanotubes and estimating nonlocal constant using simulated vibration signals-ARMA and ANN based approach

Nonlocal continuum mechanics is a popular growing theory for investigating the dynamic behavior of Carbon nanotubes(CNTs).Estimating the nonlocal constant is a crucial step in mathematical modeling of CNTs vibration behavior based on this theory.Accordingly,in this study a vibration-based nonlocal parameter estimation technique,which can be competitive because of its lower instrumentation and data analysis costs,is proposed.To this end,the nonlocal models of the CNT by using the linear and nonlinear theories are established.Then,time response of the CNT to impulsive force is derived by solving the governing equations numerically.By using these time responses the parametric model of the CNT is constructed via the autoregressive moving average(ARMA)method.The appropriate ARMA parameters,which are chosen by an introduced feature reduction technique,are considered features to identify the value of the nonlocal constant.In this regard,a multi-layer perceptron(MLP)network has been trained to construct the complex relation between the ARMA parameters and the nonlocal constant.After training the MLP,based on the assumed linear and nonlinear models,the ability of the proposed method is evaluated and it is shown that the nonlocal parameter can be estimated with high accuracy in the presence/absence of nonlinearity.

Prediction of nonlocal scale parameter for carbon nanotubes

Based on the theory of nonlocal elasticity,a nonlocal shell model accounting for the small scale effect is developed for the bending characteristics of CNTs subjected to the concentrated load.With this nonlocal shell model,explicit expressions are derived for the bending solutions.To extract the proper values of nonlocal scale parameter,we have made molecular dynamics(MD) simulations for various radii and lengths of armchair and zigzag CNTs,the results of which are matched with those of nonlocal continuum model.It is found that the present nonlocal elastic shell model with its appropriate values of nonlocal scale parameter has the capability to predict the bending behavior of CNTs,which is comparable with the results of MD simulations.Moreover,exact closed form solutions for the nonlocal scale parameter for zigzag and armchair CNTs are obtained.The results show that nonlocal scale parameter is independent of the length of CNTs,and dependent on the radius of CNTs.

Local Polynomial-Brunk Estimation in Semi-Parametric Monotone Errors-in-Variables Model with Right-Censored Data

This paper introduces a semi-parametric model with right-censored data and a monotone constraint on the nonparametrie part. The authors study the local linear estimators of the parametric coefficients and apply B-spline method to approximate the nonparametric part based on grouped data. The authors obtain the rates of convergence for parametric and nonparametric estimators. Moreover, the authors also prove that the nonparametric estimator is consistent at the boundary. At last, the authors investigate the finite sample performance of the estimation.

Weighted Profile Least Squares Estimation for a Panel Data Varying-Coefficient Partially Linear Model

This paper is concerned with inference of panel data varying-coefficient partially linear models with a one-way error structure. The model is a natural extension of the well-known panel data linear model （due to Baltagi 1995） to the setting of semiparametric regressions. The authors propose a weighted profile least squares estimator （WPLSE） and a weighted local polynomial estimator （WLPE） for the parametric and nonparametric components, respectively. It is shown that the WPLSE is asymptotically more efficient than the usual profile least squares estimator （PLSE）, and that the WLPE is also asymptotically more efficient than the usual local polynomial estimator （LPE）. The latter is an interesting result. According to Ruckstuhl, Welsh and Carroll （2000） and Lin and Carroll （2000）, ignoring the correlation structure entirely and ＂pretending＂ that the data are really independent will result in more efficient estimators when estimating nonparametric regression with longitudinal or panel data. The result in this paper shows that this is not true when the design points of the nonparametric component have a closeness property within groups. The asymptotic properties of the proposed weighted estimators are derived. In addition, a block bootstrap test is proposed for the goodness of fit of models, which can accommodate the correlations within groups illustrate the finite sample performances of the Some simulation studies are conducted to proposed procedures.

Efficient Quantile Estimation for Functional-Coefficient Partially Linear Regression Models

The quantile estimation methods are proposed for functional-coefficient partially linear regression （FCPLR） model by combining nonparametric and functional-coefficient regression （FCR） model. The local linear scheme and the integrated method are used to obtain Focal quantile estimators of all unknown functions in the FCPLR model. These resulting estimators are asymptotically normal, but each of them has big variance. To reduce variances of these quantile estimators, the one-step backfitting technique is used to obtain the efficient quantile estimators of all unknown functions, and their asymptotic normalities are derived. Two simulated examples are carried out to illustrate the proposed estimation methodology.