动载作用下沥青混凝土路面反射裂缝粘弹性响应分析

为研究沥青混凝土路面内部反射裂缝在动态荷载作用下的力学响应,采用ABAQUS有限元软件建立分析模型.通过对沥青混凝土蠕变试验曲线进行非线性拟合,得到Prony级数以赋予模型中面层粘弹性材料属性.基于粘弹性理论、时温等效原理、断裂力学及动力学有限元方法,利用半正弦荷载模拟动荷载作用,以断裂参数J积分以及复合应力强度因子作为裂缝扩展评价指标,对路面反射裂缝在不同车速、不同环境温度以及不同材料阻尼系数等条件下的力学响应进行分析.计算结果表明:反射裂缝断裂参数值随车辆行驶速度的增加而减小;不同结构温度下的断裂参数差异不明显;断裂参数曲线随阻尼比的增大而逐渐后移,且最大值逐渐减小.因此,通过改善车辆行驶条件以提高车辆行驶速度,以及通过改进材料组成来增大沥青混凝土阻尼系数,都能够有效延缓反射裂缝扩展速度,延长路面的正常使用寿命.     

A Mixture IRTree Model for Extreme Response Style: Accounting for Response Process Uncertainty

This paper presents a mixture item response tree (IRTree) model for extreme response style. Unlike traditional applications of single IRTree models, a mixture approach provides a way of representing the mixture of respondents following different underlying response processes (between individuals), as well as the uncertainty present at the individual level (within an individual). Simulation analyses reveal the potential of the mixture approach in identifying subgroups of respondents exhibiting response behavior reflective of different underlying response processes. Application to real data from the Students Like Learning Mathematics (SLM) scale of Trends in International Mathematics and Science Study (TIMSS) 2015 demonstrates the superior comparative fit of the mixture representation, as well as the consequences of applying the mixture on the estimation of content and response style traits. We argue that methodology applied to investigate response styles should attend to the inherent uncertainty of response style influence due to the likely influence of both response styles and the content trait on the selection of extreme response categories.     

A New Perspective on the Effects of Covariates in Mixture Models

In this simulation study, we explored the effect of introducing covariates to a growth mixture model when covariates were also generated by a mixture model. We varied the association between the latent classes underlying the growth trajectories and the covariates, the degree of separation between the latent classes underlying the covariates, the number of covariates included, and amount of missing data in the growth data. We found that adding covariates to the growth mixture model generally hurt class recovery except where the latent classes underlying the growth trajectories and the covariates were the same or very strongly associated, and there was a large degree of separation between the classes underlying the covariates. We found that when covariates were introduced, entropy might no longer be an accurate indicator of the distinctiveness of the growth trajectory classes.     

Bayesian Analysis of Mixture Structural Equation Models With an Unknown Number of Components

Multivariate heterogenous data with latent variables are common in many fields such as biological, medical, behavioral, and social-psychological sciences. Mixture structural equation models are multivariate techniques used to examine heterogeneous interrelationships among latent variables. In the analysis of mixture models, determination of the number of mixture components is always an important and challenging issue. This article aims to develop a full Bayesian approach with the use of reversible jump Markov chain Monte Carlo method to analyze mixture structural equation models with an unknown number of components. The proposed procedure can simultaneously and efficiently select the number of mixture components and conduct parameter estimation. Simulation studies show the satisfactory empirical performance of the method. The proposed method is applied to study risk factors of osteoporotic fractures in older people.     

基于改进SVM的新疆北部地区积雪面积反演研究——以天山山区中段为例

积雪面积是融雪径流模型中变量数据输入之一,准确的获取雪盖范围是进行流域尺度融雪水文过程研究的关键,在水资源管理及洪水预报中具有重要意义.本文以天山山区中段为例,利用MODIS数据,提出了结合混合光谱分解的积雪分量及灰度共生矩阵提取的纹理特征的SVM分类方法,对研究区积雪面积信息提取进行了研究.结果表明:通过利用混合光谱分解的积雪分量作为SVM的特征输入,总体分类精度比传统SVM分类结果有了一些提高.同时考虑结合基于灰度共生矩阵提取的纹理特征用于分类中,总体精度比传统SVM方法提高了1.081％,制图精度达到了99.01％.本文提出的分类方法能够适应特征组合之间的非线性关系,从而能提供更多的区域地物空间分布信息,能够调整无样本地表类型地区的积雪面积反演,对今后的融雪水文过程研究有重要意义.     

同构思维在标志设计中的应用与反思

论述了同构思维在标志设计中的应用,并对其进行总结,从概念同构、图形同构、意向同构三个方面系统地阐述了同构思维的概念与分类,并反思了在标志设计中运用同构思维的意义与需要注意的问题.     

Software review

STATISTICA 5.0. StatSoft, 2325 East 13th Street, Tulsa, OK 74104, (918) 583–4149. $995 retail, academic site license—$2,000 for 10 copies. Requirements: 386 or better, 4 Meg RAM, Microsoft Windows 3.1 or 95.     

The Ability for Posterior Predictive Checking to Identify Model Misspecification in Bayesian Growth Mixture Modeling

Proper model specification is an issue for researchers, regardless of the estimation framework being utilized. Typically, indexes are used to compare the fit of one model to the fit of an alternate model. These indexes only provide an indication of relative fit and do not necessarily point toward proper model specification. There is a procedure in the Bayesian framework called posterior predictive checking that is designed theoretically to detect model misspecification for observed data. However, the performance of the posterior predictive check procedure has thus far not been directly examined under different conditions of mixture model misspecification. This article addresses this task and aims to provide additional insight into whether or not posterior predictive checks can detect model misspecification within the context of Bayesian growth mixture modeling. Results indicate that this procedure can only identify mixture model misspecification under very extreme cases of misspecification.     

沥青砼路面常见病害的分析与防治

针对目前沥青砼路面常常出现裂缝、泛油、松散、坑槽等常见病害,从原材料,施工过程、施工工艺、材料组成设计等不同方面提出了改进意见和措施。     

基于光谱混合分析的城市植被覆盖度与地表温度关系的研究

分别选择NDVI和光谱混合分析植被组分(VF)作为城市植被覆盖度指示因子,研究植被覆盖度与地表温度(LST)的相关关系.选择了一定数量的样本点,通过线性拟合的方法研究了NDVI及VF与LST的相关关系.研究结果表明,VF与LST之间的相关性比NDVI与LST之间相关性更好.研究表明,在城市LST研究中VF可以作为表征植被性质的参数.