隧道围岩弹塑性随机有限元分析及可靠度计算

在弹塑性有限元分析的初应力法基础上,推导了弹塑性随机有限元增量初应力法的计算迭代格式,结合可靠度理论,给出了围岩稳定的可靠度分析方法,在计算出可靠度的同时还给出了围岩和锚喷支护结构的应力特征值。并对一工程算例进行了详细的分析和讨论,得出了一些有益的结论。     

我国螺旋度的研究及应用

对自20世纪90年代以来至今国内关于螺旋度理论及应用研究工作进行了较系统地总结,将螺旋度归类为垂直螺旋度、水平螺旋度及完全螺旋度,同时又将垂直螺旋度划分为局地垂直螺旋度和积分垂直螺旋度,详细介绍并给出了每一类螺旋度的计算表达式以及其在天气诊断分析中的应用状况,对螺旋度的应用研究现状进行了简单地分析与讨论,最后展望了未来螺旋度研究与应用的发展方向。     

浅析大数据对测绘保障的影响

通过对大数据时代的特点进行分析,结合实际从测绘保障的方式、职能、方法等方面进行剖析,总结了大数据时代测绘保障的重点,目的是探索大数据时代测绘保障的新方向,为提高测绘保障服务奠定基础。     

运筹学重点课程建设的构建

为了进行运筹学重点课程建设,运筹学课程团队提出了课程建设的基本构想,首先对运筹学课程予以定位,其次从运筹学课程的教学内容、课程建设的内容、课程建设的特色三方面确定了运筹学课程建设的目标,最后提出了课程建设主要措施和步骤。     

一次暴雪过程地形方案的敏感性试验研究

利用WRF模式中提供的包络地形、轮廓地形和平均地形方案对2005年12月20-21日的山东暴雪过程进行数值模拟.结果表明:地形方案与降雪的时空分布有很大的相关性,地形越接近实际地形,降雪的时空分布越接近实况;降雪过程中有明显的中尺度重力波活动,降雪的峰值滞后于重力波的波峰;重力波受地形影响很大,地形越不平滑,重力波的强度越强、移速越慢;当重力波的强度较强时,降雪峰值滞后于波峰的时间差也较长.     

基于支持向量机的岩石薄片图像分割

支持向量机是对传统学习分类方法的一个良好替代,特别是小样本、高维的情况下,有着良好的泛化能力,利用支持向量机良好的分类特性,将图像分割的问题转化为分类问题,对岩石薄片图像进行分割实验.通过实验表明：支持向量机对于分割颜色分布不均匀、边缘模糊的岩石薄片图像,有很好的应用前景.     

盘锦湿地生态系统野外观测站概况

重点介绍了盘锦湿地生态系统野外观测站建立的背景、研究定位与研究方向,主要观测场及观测仪器设备,已有基础数据积累和科研任务等。在此基础上,提出了未来盘锦站应在建设目标、人才队伍建设及规范管理方面的重点工作,为将研究站建设成为生态气象监测示范与技术支持基地、研制生态气象业务评估方法与模型的平台、生态气象业务人才培养基地以及作为地球系统科学前沿研究的孵化器,最终成为生态学和资源、环境科学等相关学科的发展提供野外试验和研究平台提供保障。     

地震预警现地PGV连续预测的最小二乘支持向量机模型

为提升现地仪器地震烈度预测的准确性与连续性,研究面向地震预警的PGV连续预测模型.以中国仪器地震烈度标准的计算参数：0.1~10 Hz带通滤波三分向矢量合成速度峰值PGV为预测目标,利用日本K-net与KiK-net台网P波触发后1~10 s强震数据,基于人工智能中的机器学习方法-最小二乘支持向量机,选取7种特征参数作为输入构建最小二乘支持向量机PGV预测模型LSSVM-PGV.结果表明,本文建立的LSSVM-PGV模型在训练数据集与测试数据集上的预测误差标准差变化趋于一致,具备泛化性能;P波触发后3 s预测PGV与实测PGV即可整体符合1：1关系,随着时间窗的增长,PGV预测的误差标准差显著减小、并在P波触发后6 s趋向收敛,具备准确连续预测能力;对比同为P波触发后3 s的常用Pd-PGV模型,LSSVM-PGV模型的PGV预测误差标准差明显减小,"小值高估"与"大值低估"现象明显改善,预测准确性得到提升.熊本地震序列的震例分析表明,对于6.5级以下地震,LSSVM-PGV模型最多在P波触发后3 s即可预测出与实测PGV整体符合1：1关系的PGV;对于7.3级主震,由于其破裂过程的复杂性,P波触发后3 s的预测结果出现一定程度的低估,但随着时间窗增长至6 s时,预测PGV与实测PGV符合1：1关系、并直到10 s整体趋势保持一致.本文构建的LSSVM-PGV模型可用于现地地震预警仪器地震烈度的预测.     

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.