New bounds for the height limit of a vertical slope

Using the finite element method, the static and kinematic methods of limit analysis provide tools to solve many stability problems in mechanics of continuous media. The classic problem of the height limit of a Tresca or Mises vertical slope subjected to the action of gravity stems naturally from this theory in plane strain. Although the exact solution to this problem remains unknown, the present work has produced precise bounds using the static and kinematic approaches conjointly: the height limit is now between 3·760 and 3·786 C/ γ, γ being the weight per unit volume and C the soil cohesion. These tests also show that both methods, used on current workstations with industrial optimization codes such as XPRESS or OSL, are capable of solving any plane problem of limit loads in geotechnics or in structural calculus. Copyright © 2000 John Wiley & Sons, Ltd.     

移动式三维激光扫描技术在隧道扫描中的应用研究

移动式三维激光扫描技术通过发射激光并以螺旋线形式对隧道进行全断面高密度扫描,初步获取隧道表面特征信息,再通过内业分析发射和接收激光信号的强度,可以获得隧道衬砌内表面的影像信息.使用这些数据信息,可以对隧道状态、病害、限界、变形、收敛情况等进行分析和评价.本文采用移动式三维激光扫描技术选取杭州地铁5号线一个车站和区间进行...     

山东莱州西岭勘查区ZK88-19深孔同径造斜绕障工艺

山东莱州西岭勘查区ZK88-19号钻孔设计孔深2500 m,2016年12月开孔,于孔深2034 m处发生烧钻事故,处理孔底钻具时再次发生烧钻事故,导致2套钻具及2套内管总成滞留孔底;2017年8月,受划定环境保护区的影响,中断施工,直至2020年3月恢复施工,继续处理孔内事故难度很大;经过多次论证,应用了深孔同径造斜绕障技术,包括设计了造斜偏心楔、造斜钻头以及研发了偏心楔固定技术等,成功绕过事故段,恢复钻进。     

基于Google Earth遥感影像的城市建筑物高度反演

基于光学遥感影像中建筑物的阴影分布,推导了建筑物高度与阴影分布的关系函数,在此基础上构建了建筑物高度反演方法,并在北京市典型区进行了实例研究。结果表明：利用建筑物在夏季的阴影全长或在冬季可视部分的阴影长提取高度,分别有90.9%和84.8%的建筑物高度误差控制在实测值的5%以内;在提取居民区或高层建筑物的高度时应尽量采用夏季的遥感影像,减少阴影遮挡的情况;低矮建筑物的高度提取,宜采用阴影更为显著的冬季影像可视阴影部分进行反演计算;在缺乏卫星轨道等影像参数时,利用典型已知建筑的高度反推出相关参数,并推求同景影像中其他建筑物高度的方法是可行的,为利用城市代表性建筑物推求建筑群高度提供了可能。     

High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data

Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (H_canopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 H_canopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 H_canopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 H_canopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted H_canopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in H_canopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools.     

Woody vegetation cover,height and biomass at 25-m resolution across Australia derived from multiple site,airborne and satellite observations

Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting and land management. Here, we investigated whether machine learning can be used to combine multiple spatial observations and training data to estimate woody vegetation canopy cover fraction (‘cover’), vegetation height (‘height’) and woody above-ground biomass dry matter (‘biomass’) at 25-m resolution across the Australian continent, where possible on an annual basis. We trained a Random Forest algorithm on cover and height estimates derived from airborne LiDAR over 11 regions and inventory-based biomass estimates for many thousands of plots across Australia. As predictors, we used annual geomedian Landsat surface reflectance, ALOS/PALSAR L-band radar backscatter mosaics, spatial vegetation structure data derived primarily from ICESat/GLAS satellite altimetry, and spatial climate data. Cross-validation experiments were undertaken to optimize the selection of predictors and the configuration of the algorithm. The resulting estimation errors were 0.07 for cover, 3.4 m for height, and 80 t dry matter ha^-1 for biomass. A large fraction (89–94 %) of the observed variance was explained in each case. Priorities for future research include validation of the LiDAR-derived cover training data and the use of new satellite vegetation height data from the GEDI mission. Annual cover mapping for 2000–2018 provided detailed insight in woody vegetation dynamics. Continentally, woody vegetation change was primarily driven by water availability and its effect on bushfire and mortality, particularly in the drier interior. Changes in woody vegetation made a substantial contribution to Australia’s total carbon emissions since 2000. Whether these ecosystems will recover biomass in future remains to be seen, given the persistent pressures of climate change and land use.     

三维激光扫描技术在矿山表面积计算中的应用

基于三维激光扫描技术进行表面积计算的原理为:获取的目标表面海量点云数据,采用一定的数学计算方法,便可求得场区的表面积。该方法弥补了传统方法计算物体表面积只能获取表面离散点坐标,对于因堆积物表面形状复杂而无法测量到的离散点,只能通过插值计算得到的缺陷;解决了随着计算范围增大或地面起伏变化工作量不断增大,计算误差会随着地面起伏的增大的问题。结合项目实例,本文提出了一种采用三维激光扫描技术进行废弃矿山修复治理区的三维信息采集和表面积核算方法,并对该方法表面积计算的技术原理、表面积计算的技术路线及项目实施过程中遇到的难题及解决方案问题进行了详细阐述,为后续相关项目实施提供了一定的参考依据。     

孤立波作用下植物带消浪效果影响因素分析

近岸植物带能有效削弱波浪作用,植物带消浪影响因素分析对布置护岸工程、维护岸线稳定有重要意义。在模型实验基础上,探究相对波高、模型相对宽度、模型相对高度和体积分数对透射系数的影响。设置波浪水槽试验,研究孤立波在不同水深、入射波高及植物排布方式等组合条件下对植物消浪效果的影响。采用遗传编程法得出各复杂度下的透射系数计算公式,并分析各参数在方程中的表现情况,发现遗传编程能定性给出参数的敏感度。相比于非线性回归拟合结果,遗传编程方法得到的函数关系更加精确。采用人工神经网络方法进一步分析与验证各影响因素对植物消浪效果的影响程度,结果表明人工神经网络得出的结果与遗传编程方法相近,体积分数对消浪效果起主导作用。     

基于人工神经网络的台风浪高快速计算方法

采用2010—2017年南海5个浮标波高观测资料和中国气象局热带气旋最佳路径集中的热带气旋参数, 基于前馈型误差反向传播(Forward Feedback Back Propagation, FFBP)神经网络(Artificial Neural Network, ANN)方法, 分别建立了各浮标站的台风浪高快速计算模型。研究显示, 基于热带气旋中心坐标、中心最低气压、近中心最大风速、热带气旋中心与浮标之间的距离和方位4个参数建立的神经网络模型经反复训练后, 模型输出结果可以很好地拟合观测数据, 各浮标有效波高计算值与观测值的均方根误差小于0.3m, 平均相对误差为5.78%~7.23%, 相关系数大于0.9, 属高度相关。独立测试结果显示, “山竹”( 国际编号: 1822)影响期间有效波高最大值的神经网络模型预报结果与观测值基本吻合, 相对误差为-31.06%~0.98%, 但计算的最大值出现时间和观测情况不完全一致。该计算方法可应用于热带气旋影响期间的有效波高最大值计算, 因而在海洋工程领域和海洋预报领域具有应用前景。     

极值波高与历时联合分布的重现水平分析

使用美国北卡罗来纳州的 FRF 1985—2016 年的极值波高及其持续时间数据,采用最优的 Gumbel-Hougaard copula函数和 Kendall 分布函数构建极值波高和相应历时不同组合的联合概率分布模式,分析各个组合的遭遇概率、“或”重现期、“且”重现期和 Kendall 重现期,以出现最大可能概率的方法推算各组合联合设计值。结果表明：Kendall 重现期所对应的累积频率更准确地代表了特定设计频率下的风险率;重现期分别为 5 年、10 年、20 年、50 年、100 年、200 年推算的 Kendall重现期设计值介于“或”重现期和“且”重现期设计值之间,小于相应的边缘分布设计值;基于 Kendall 重现期的极值波高及其持续时间不同重现期组合推算的结果可为海洋工程构筑物设计与风险管理提供新的选择与参考。