Stress and deformation of offshore piles under structural and wave loading

Various offshore structures, especially large structures such as Tension Leg Platforms (TLP), are usually supported by concrete piles as the foundation elements. The stress distribution within such a large structure is a dominant factor in the design procedure of an offshore pile. To provide a more accurate and effective design for offshore foundation systems under axial and lateral wave loads, a finite element model is employed herein to determine the stresses and displacements in a concrete pile under similar loading conditions. A parametric study is also performed to examine the effects of the stress distribution due to the changing loading conditions.     

天然状态与饱和状态下的不稳定斜坡危险性对比分析

本文依据北京市房山区史家营乡大村涧村的调查成果,借助于有限元分析软件,建立居住区附近影响范围内的边坡三维工程地质力学模型和弹塑性模型,利用有限差分法分析,对单一矸石堆边坡和矸石-岩土混合型边坡,进行计算分析,对比分析了这两种边坡在天然状态下与饱和状态下的安全系数,最终进行了边坡危险性评价。     

电磁成像正演仿真与传感器参数优化设计

应用有限元方法对电磁层析成像测量进行正演模拟,计算不同测量组合下各种典型流型的电势实部和虚部分布,考察电极个数、电极张角以及电极厚度对模拟测量响应的影响,并采用神经网络方法对传感器各结构参数进行优化设计.研究结果表明,介质分布对电势影响起主要作用,其中虚部分布特征更加明显;传感器各参数中电极数目对测量值影响较大,电极张角和电极厚度有一定影响.     

基于卷积完全匹配层的非规则网格时域有限元探地雷达数值模拟

复频移完全匹配层（Complex Frequency-Shifted PML,CFS-PML）在长时间时域计算中对凋落波、倏失波具有好的吸收效果,并被广泛应用于时域有限差分模拟中.而本文采用卷积方法将CFS-PML应用于时域有限元求解GPR波动方程的数值模拟中.论文以TM波为例,推导了基于CPML（Convolutional PML）边界的时域有限元GPR波动方程求解公式,采用Newmark-β方法对时间导数进行离散,有效改善了时域有限元GPR数值计算程序的稳定性.并以狭长模型为例,开展了CPML边界中关键参数m、R和κ的选取实验,通过对比反射误差大小确定了综合最优参数组合.相同时刻UPML与CPML波场快照、3个检测点的反射误差比较,说明CPML较UPML具有更好的吸收效果.最后,采用非规则四边形网格对1个复杂GPR模型进行剖分,应用加载CPML边界条件的FETD程序对该模型进行了正演,得到了二维剖面法、宽角法正演GPR剖面图,说明非规则四边形对复杂模型的良好适应性,基于CPML边界条件的FETD可有效减少边界反射误差,能实现对任意复杂不规则模型的正演模拟.

     

人工神经网络和决策树模型在滑坡易发性分析中的性能对比

机器学习模型广泛应用于区域性滑坡易发性分析。模型的选择关系到评价结果的可信度、准确率和稳定性。现有滑坡易发性分析模型对比研究侧重模型的预测精度。模型的稳定性和数据量敏感性对机器学习模型的性能评估同样非常重要。本文以福建省南平市蔡源流域为研究区,以四川省绵阳市北川县为验证区,从预测精度、稳定性和数据量敏感性3个方面深入对比BP（Back Propagation）人工神经网络模型和CART（Classification and Regression Tree）决策树模型在滑坡易发性分析中的效果,主要结论如下:① 在逐渐增加一定数量训练样本的过程中,BP人工神经网络模型预测精度的增长率更高。在蔡源流域内,当训练样本数量增加10 000时,BP人工神经网络模型的预测精度上升5.22%,CART决策树模型的预测精度上升2.11%。② BP人工神经网络的预测精度高于CART决策树模型,且较为稳定。在100组数据集上,BP人工神经网络模型验证集预测精度的均值和验证集滑坡样本预测精度的均值分别为81.60%和84.86%,高于CART决策树模型的72.97%和76.59%。与此同时,BP人工神经网络模型对应预测精度的标准差分别是0.32%和0.37%,小于CART决策树模型的0.35%和0.67%。③ BP人工神经网络模型分析的滑坡易发区相比CART决策树模型,更接近实际滑坡的空间分布。最后,北川县的验证实验也出现了相同的现象。     

The Creation of a National Agricultural Land Use Dataset: Combining Pycnophylactic Interpolation with Dasymetric Mapping Techniques

Agricultural Census data is summarised over spatially coarse reporting units for reasons of farm confidentiality. This is problematic for research at a local level. This article describes an approach combining dasymetric and volume preserving techniques to create a national land use dataset at 1 km² resolution. The results for an English county are compared with contemporaneous aggregated habitat data. The results show that the accurate estimates of local agricultural land use (Arable and Grass) patterns can be estimated when individual 1 km squares are combined into blocks of > 9 squares, thereby providing local estimates of agricultural land use. This in turn allows more detailed modelling of land uses related to specific livestock and cropping activities. The dataset created by this work has been subject to extensive external validation through its incorporation into a number of other national models: nitrate leaching (e.g. MAGPIE, NEAP‐N), waste, and pathogen modelling related to agricultural activity.     

地学信息体视化中3维交互技术的研究与实现

地学信息的3维可视化是当前地球信息科学研究的热点之一.体视化技术主要研究包含物体内部信息的体数据的表示、变换、操作和显示等,是实现地学信息3维可视化的理想技术与手段.主要研究地学信息体视化中3维交互技术的理论、技术与方法,并采用面向对象的程序开发语言VC 6.0和3维可视化开发工具包VTK(The Visualization Toolkit)来实现这些算法,开发体视化3维交互功能控件.     

3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar

We developed a method to produce a 3-D voxel-based solid model of a tree based on portable scanning lidar data for accurate estimation of the volume of the woody material. First, we obtained lidar measurements with a high laser pulse density from several measurement positions around the target, a Japanese zelkova tree. Next, we converted lidar-derived point-cloud data for the target into voxels. The voxel size was 0.5 cm × 0.5 cm × 0.5 cm. Then, we used differences in the spatial distribution of voxels to separate the stem and large branches (diameter > 1 cm) from small branches (diameter ⩽ 1 cm). We classified the voxels into sets corresponding to the stem and to each large branch and then interpolated voxels to fill out their surfaces and their interiors. We then merged the stem and large branches with the small branches. The resultant solid model of the entire tree was composed of consecutive voxels that filled the outer surface and the interior of the stem and large branches, and a cloud of voxels equivalent to small branches that were discretely scattered in mainly the upper part of the target. Using this model, we estimated the woody material volume by counting the number of voxels in each part and multiplying the number of voxels by the unit voxel volume (0.13 cm³). The percentage error of the volume of the stem and part of a large branch was 0.5%. The estimation error of a certain part of the small branches was 34.0%.     

利用多时相DEM数据的冰雪覆盖体量变化计算方法研究

提出了利用同一研究区不同时相且无地质灾害发生情况下数字高程模型(DEM)的差异来计算冰雪覆盖体量变化的方法。设计实现了两种DEM主要表达形式(规则格网DEM和TIN)的冰雪覆盖体量变化计算方法,提出基于三维道格拉斯-普克算法的DEM数据综合方法,减少离散点数量,从而提高建立TIN网的速度以及体积变化计算速度。最后以天山乌鲁木齐河流域数据为例对两种计算方法进行了实验验证。     

体素模型的战场环境建模研究

针对基于面模型的战场环境建模难以展现战场环境要素的内部属性和规律等的不足,该文提出了利用体素模型实现战场环境建模的基本方法。该方法能够记录战场环境各要素的不同属性,反映其内部属性和规律,准确地模拟战场地理环境的变换过程,可实现战场环境各要素的统一建模。文章利用体素模型实现了对战场环境的认知与抽象,对战场环境体素数据模型进行了分类。最后以战场地形环境要素为例,利用战场环境基本体元实现了对战场地形环境的体素建模。