Vibration‐based damage assessment of steel structure using global and local response measurements

Damage assessment of a structure involves acquiring and identifying dynamic characteristics of the structure and using these characteristics to evaluate behavior and performance. In this study, an unsymmetrical three‐story steel structure (fabricated with one weak column in the first floor) was tested on shaking table and subjected to a series of earthquake excitations with increasing level of excitation back to back. Besides, white noise excitation was also applied in between the earthquake excitation to serve as the reference state. Both the traditional sensing system (accelerometer and linear variable differential transformer) and the local optical tracker system were implemented in the structure to collect the vibration‐based responses. For operational modal analysis, structural response from white noise excitation will be used in this study. First, the traditional system identification using global response data is used (multivariate autoregressive (AR)‐model) to extract system natural frequencies and mode shapes from all different set of white noise responses after earthquake excitation. The migration of AR‐coefficient ellipse error from each sensor response was used to identify the damage location. Second, blind source separation technique was used to identify the modal contribution of the structure from each test, which provide information to detect the damage severity. Finally, from the local optical tracker array data, the principal component analysis was applied to quantify the earthquake‐induce local stress of the structural member. Combine the result from damage detection using global measurement and the identified local element stress, one can locate and quantify the damage. Copyright © 2015 John Wiley & Sons, Ltd.     

基于GIS和证据权重法的那仁宝力格地区铜多金属矿成矿预测

根据那仁宝力格地区主要铜多金属矿床成矿规律,利用GIS平台提取地、物、化、遥等多元地质找矿信息,在MRAS系统中开展了基于18个成、控矿要素的铜多金属矿成矿预测与评价。根据后验概率值大小编制了成矿后验概率等值线图,划分了4个等级的找矿条件和9个找矿远景区。结果表明,预测模型有效,近95%的已知矿床（点）位于D级及以上找矿有利地区。在众多成、控矿要素中,增隆昌组与阿古鲁沟组、构造信息、遥感组合异常、铁族元素地球化学组合异常等具有较大权重。新圈定的那仁宝力格找矿远景区和霍各乞南部找矿远景区是下一步研究区寻找新的铜多金属矿床的重点地区。     

Accounting for Parameter Uncertainty in Reservoir Uncertainty Assessment: The Conditional Finite-Domain Approach

An important aim of modern geostatistical modeling is to quantify uncertainty in geological systems. Geostatistical modeling requires many input parameters. The input univariate distribution or histogram is perhaps the most important. A new method for assessing uncertainty in the histogram, particularly uncertainty in the mean, is presented. This method, referred to as the conditional finite-domain (CFD) approach, accounts for the size of the domain and the local conditioning data. It is a stochastic approach based on a multivariate Gaussian distribution. The CFD approach is shown to be convergent, design independent, and parameterization invariant. The performance of the CFD approach is illustrated in a case study focusing on the impact of the number of data and the range of correlation on the limiting uncertainty in the parameters. The spatial bootstrap method and CFD approach are compared. As the number of data increases, uncertainty in the sample mean decreases in both the spatial bootstrap and the CFD. Contrary to spatial bootstrap, uncertainty in the sample mean in the CFD approach decreases as the range of correlation increases. This is a direct result of the conditioning data being more correlated to unsampled locations in the finite domain. The sensitivity of the limiting uncertainty relative to the variogram and the variable limits are also discussed.     

Statistical Multivariate Analysis in the Assessment of River Water Quality in the Vicinity of KRS Dam, Karnataka, India

Surface water quality assessment is a basic and critical tool for informing sustainable management of water resources. The aim of this study is to evaluate the reservoir and river water quality of the Cauvery River in the vicinity of the Krishna Raja Sagar Dam, Karnataka, India. The results will assist water management in the study area for varied future demands including, irrigation, industries, and river conservation. Ecologically important parameters such as dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, and physico-chemical parameters were analyzed. The results were compared with standard permissible limits. The differences in various parameters were statistically significant (P < 0.01) when comparing the upstream and downstream stretches of the river, particularly in summer. To identify a good technique for water quality assessment, all of the water quality parameters have been calculated by correlating various parameters and comparing them over two critical dry seasons. The dendrogram of the water-quality parameters clearly indicate that river water is safe for drinking, fishing, irrigation, and industrial purposes.     

多元层状边坡土体风蚀速率与微结构参数关系

吐鲁番交河故城台地多元层状土质边坡由于风蚀而形成空腔的现象表明,不同类型的土具有不同的抗风蚀能力,抗风蚀能力的强弱是多因素影响的共同结果,微观结构是其中一个重要内因。风蚀速率是表征土抗风蚀能力强弱的重要物理量,研究运用风洞试验、微结构分析等手段揭示了风蚀速率与颗粒面积比、颗粒圆度、孔隙等效直径、孔隙充填比等微结构特征参数之间的良好对应关系;进而采用相关分析法分析并证实风蚀速率与各特征参数间的良好相关关系,应用回归分析方法建立3个试验风速下风蚀速率与单个参数和多个参数之间的回归方程,验证了微结构特征对土抗风蚀能力有显著影响,进而为土抗风蚀能力的评价提供了微观依据。     

河北省任丘市浅层地下水化学特征及形成机制

浅层地下水是任丘市重要的水资源之一,为研究其水化学特征及其形成机制,本文在实地调查取样分析化验的基础上,以水文地球化学理论为指导,借助多元数理统计的方法,从多个角度揭示了研究区浅层地下水的形成演化过程:①通过离散分析,认为研究区各主要离子变异系数较大,说明研究区浅层地下水受人为影响明显,水化学特征复杂.②通过相关分析,...     

我国滨海核电站防护工程设计标准研究

面对全球极端气候频发的趋势,国内外核电站海岸防护工程普遍采用"可能最大台风暴潮"、"设计基准洪水"等具有含混性的规定作为确定防灾设计标准的依据,其内涵的各种不确定性因素必然会对防灾决策带来重大的致灾隐患。按照国务院核电站安全规划(2011-2020)提出的"必须按照全球最高安全要求",全面开展滨海核电站防护工程设计标准研究己势在必行。使用多维复合极值分布理论及双层嵌套多目标联合概率模式,将核电站水文与气象的研究标准合二为一,对IAEA及我国滨海核电站防护工程设计标准进行了风险评估,供有关部门参考。     

多元逐步回归分析应用于固化土强度与微结构参数相关性评价

用石灰固化不同含盐量的滨海盐渍土,固化后土微结构参数发生了变化,力学强度大幅度提高。多元逐步回归分析计算结果表明二者之间存在着良好的相关关系,颗粒粒度分维、颗粒定向分维、等效直径、扁圆度和面积比是对固化土力学强度影响较为显著的5个微结构参数。借助土微结构参数与强度间的相关性计算分析,从另外一个角度验证石灰对滨海盐渍土的固土作用。     

Compositional Data Analysis of Some Alkaline Glasses

The approach to the analysis of compositional data involving log-ratio transformation of the data has not been generally adopted by researchers wishing to analyse such data. In the context of exploratory methods of multivariate analysis, such as principal components analysis, where the hope is to identify (cluster) structure in the data, this may be because traditional methods can produce more interpretable results than the log-ratio approach. After illustrating this with an example, circumstances under which the log-ratio approach performs poorly when traditional approaches work well are identified. Log-ratio analysis can be dominated by variables having low absolute presence and high relative variation that do not contribute to, and can obscure, structure in the data. Traditional methods can detect certain kinds of structure in the data that correspond to structure on a ratio scale, after a suitable redefinition of the composition. Since traditional methods often detect such structure more directly than log-ratio analysis it can be concluded that claims that the traditional analysis is inappropriate or meaningless are exaggerated. This conclusion is based on empirical experience rather than theoretical concerns. The arguments are illustrated using compositional data for alkaline glasses, but have more general application.     

Significance tests for multivariate normality of clusters from branching patterns in dendrograms

A significance test is presented for whether, based on levels of branches in a dendrogram, a cluster is from a multivariate normal distribution. The method compares the observed cumulative graph of number of branches with a graph derived from a simple logistic function. Provided the number of objects or variables is not small, the difference between graphs can be tested by the Kolmogorov-Smirnov, Cramér-von Mises, and Lilliefors statistics.Logistic functions were obtained by simulation and are available for three similarity measures: (1) Euclidean distances, (2) squared Euclidean distances, and (3) simple matching coefficients, and for five cluster methods: (1) WPGMA, (2) UPGMA, (3) single linkage (or minimum spanning trees), (4) complete linkage, and (5) Ward's increase in sums of squares. For simple matching coefficient, the mean intracluster similarity also is required.The method allows a test of whether the dendrogram could be from a cluster of smaller dimensionality due to character correlations. Good fit of the data to abnormally large or small dimensionality provides an important warning to interpretation of the dendrogram. Quantiles of test statistics were found by simulation to be well-approximated by logistic functions. The Lilliefors test is recommended for general use; if a conservative test is required, the two-tailed Kolmogorov-Smirnov test is most suitable. The method is suitable for use with a hand calculator, and a computer program for it is available from the author.