Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

Assessing Impacts of Typhoons and the Chi-Chi Earthquake on Chenyulan Watershed Landscape Pattern in Central Taiwan Using Landscape Metrics

The Chi-Chi earthquake (M_L = 7.3) occurred in the central part of Taiwan on September 21, 1999. After the earthquake, typhoons Xangsane and Toraji produced heavy rainfall that fell across the eastern and central parts of Taiwan on November 2000 and July 2001. This study uses remote sensing data, landscape metrics, multivariate statistical analysis, and spatial autocorrelation to assess how earthquake and typhoons affect landscape patterns. It addresses variations of the Chenyulan watershed in Nantou County, near the earthquake’s epicenter and crossed by Typhoon Toraji. The subsequent disturbances have gradually changed landscape of the Chenyulan watershed. Disturbances of various types, sizes, and intensities, following various tracks, have various effects on the landscape patterns and variations of the Chenyulan watershed. The landscape metrics that are obtained by multivariate statistical analyses showed that the disturbances produced variously fragmented patches, interspersed with other patches and isolated from patches of the same type across the entire Chenyulan watershed. The disturbances also affected the isolation, size, and shape-complexity of patches at the landscape and class levels. The disturbances at the class level more strongly affected spatial variations in the landscape as well as patterns of grasslands and bare land, than variations in the watershed farmland and forest. Moreover, the earthquake with high magnitude was a starter to create these landscape variations in space in the Chenyulan watershed. The cumulative impacts of the disturbances on the watershed landscape pattern had existed, especially landslides and grassland in the study area, but were not always evident in space and time in landscape and other class levels.     

松嫩平原西部碱尘气溶胶的时段变化及其气候影响因素分析

利用耦合等离子体发射光谱测试技术(ICP-AES)对松嫩平原西部2003年7月到2004年5月间获得的大气总悬浮颗粒物(TSP)样品进行了元素分析,并结合气象资料和TSP质量浓度,初步确定了碱尘活动年际内的演变规律及其主要控制因素.结果表明,TSP质量浓度的高峰时段是早春,其元素总体浓度亦高于其它时段,其中Na元素浓度具有明显的高值,表明碱尘在该时段为TSP的主要成分;晚春时段区域沙尘影响加强,很难监测本地碱尘活动;夏季,碱尘的3种重要元素(Na、Ca、K)含量增加,表明夏季有碱尘活动,与TSP浓度偏高吻合;秋季,碱尘和区域沙尘的活动均减弱.影响碱尘活动的气候因子主要是风力、降水量、温度、日照和湿度,但不同时段其主导作用不同.     

长江河口硅和磷生源要素质量浓度的变化特征

根据近年来长江河口区域内大通水文站的记录和近期在长江入海河口区上海段多个采样点的生源要素质量浓度实测数据,比较水中可溶性硅酸盐和磷酸盐的质量浓度,分析在长江入海河口区水中硅和磷元素含量的主要变化特征.结果表明,长江水中的硅酸盐和磷酸盐从长江入海河口进入大海的过程中,其质量浓度有降低的趋势,它们在长江入海河口的多年的质量浓度变化趋势是相反的.长江入海河口区域上游的磷酸盐质量浓度升高幅度较大,而硅酸盐变化不大,硅酸盐、磷酸盐质量浓度在长江河口区进入海洋的过程中迅速降低.      

N、P、Fe、Mn对亚历山大藻LC3生长的影响

利用正交试验研究了N、P、Fe、Mn四种物质对亚历山大藻LC3生长的影响,比较无机营养盐及微量金属元素对亚历山大藻LC3增殖影响的大小顺序,结果显示无机N对藻的生长有促进作用,882—2646μmol／L浓度时LC3生长较好,2646μmol／L下生长速率略快。无机P对亚历山大藻LC3生长的影响差异显著,一定浓度的P对LC3的生长有促进作用。Fe及Mn对藻生长均有促进作用,分别在3．5μg／L的Fe浓度下及18—36gg／L的Mn浓度下藻可获得较快的生长速率。四种元素对亚历山大藻LC3生长均有促进作用,各元素对生长影响的大小顺序依次为：P〉Mn〉N〉Fe。     

某水电站进水口边坡变形特征及稳定性评价

某水电站是澜沧江中、下游梯级开发的关键工程,其进水口高边坡地质条件复杂。通过对该电站进水口边坡的物质组成、结构特征、以及边坡开挖等因素的综合分析研究,阐述了其变形机理,说明此高边坡存在如下变形破坏形式:①楔形体滑动破坏;②扩展式的平面型塌滑和滑移型崩塌破坏;③卸荷松弛变形破坏。采用三维块体分析和有限元数值模拟,分析了这类边坡在开挖过程中的应力变形特征及其变化规律。     

基于谐波沉降的管道力学分析*

为避免地面沉降引发的油气管道事故,研究沉降管道的力学特性,提出基于谐波沉降的管道力学评估方法。以中国石化某沉降管道为研究对象,通过现场测量的方法获得沉降区管道高程,利用傅里叶级数展开对不均匀沉降数据进行处理分析,获得管道谐波沉降的拟合函数。建立ANSYS有限元模型,采用土弹簧模型模拟非沉降区管道与土体相互作用关系,将谐波沉降作为位移载荷施加到沉降区管道,对含内压管道的沉降进行数值模拟,分析其应力、应变分布规律。结果表明:沉降与非沉降交界处管道应变及应力最大,基于应变的评估准则,管道运行状态为安全,为应急响应提供支撑。     

基于可拓物元模型的安全经济开采评价指标体系的构建及应用

首先根据安全经济开采的内涵,建立以人员安全、设施设备安全、开采环境安全、安全投资和安全管理为主要制约因素的安全经济开采评价模型,并运用改进的层次分析法确定人员可靠度对安全经济开采水平影响最大,开采环境可靠度影响最小。其次,采用函数拟合法、专家打分法、标准化打分法量化安全经济开采水平指标,并将安全经济开采水平划分为3个等级。最后,运用可拓物元模型方法,得出某铁矿安全经济开采水平为二级,无重大经济损失,与实际吻合。     

Validation of a booted finite element model of the WIAMan ATD lower limb in component and whole-body vertical loading impacts with an assessment of the boot influence model on response

Objective: A novel anthropomorphic test device (ATD) representative of the 50th percentile male soldier is being developed to predict injuries to a vehicle occupant during an underbody blast (UBB). The main objective of this study was to develop and validate a finite element (FE) model of the ATD lower limb outfitted with a military combat boot and to insert the validated lower limb into a model of the full ATD and simulate vertical loading experiments.

Methods: A Belleville desert combat boot model was assigned contacts and material properties based on previous experiments. The boot model was fit to a previously developed model of the barefoot ATD. Validation was performed through 6 matched pair component tests conducted on the Vertically Accelerated Loads Transfer System (VALTS). The load transfer capabilities of the FE model were assessed along with the force-mitigating properties of the boot. The booted lower limb subassembly was then incorporated into a whole-body model of the ATD. Two whole-body VALTS experiments were simulated to evaluate lower limb performance in the whole body.

Results: The lower limb model accurately predicted axial loads measured at heel, tibia, and knee load cells during matched pair component tests. Forces in booted simulations were compared to unbooted simulations and an amount of mitigation similar to that of experiments was observed. In a whole-body loading environment, the model kinematics match those recorded in experiments. The shape and magnitude of experimental force–time curves were accurately predicted by the model. Correlation between the experiments and simulations was backed up by high objective rating scores for all experiments.

Conclusion: The booted lower limb model is accurate in its ability to articulate and transfer loads similar to the physical dummy in simulated underbody loading experiments. The performance of the model leads to the recommendation to use it appropriately as an alternative to costly ATD experiments.     

走滑断层作用下X80管道设计应变规律和神经网络预测

走滑断层是埋地管道常见的地质灾害威胁,断层作用下管道会发生较大的拉压应变而失效。为得到X80管道的设计应变,基于有限元方法建立了走滑断层作用下管道的应变响应数值计算模型,模型使用壳单元模拟管道,非线性弹簧单元模拟土壤约束,采用西二线实际工程的管道应变影响参数范围,计算了管道的设计应变;为预测管道的设计应变值,基于以上参数化分析得到的4 817组设计应变结果,采用人工神经网络建立了管道设计应变预测模型。结果表明:该神经网络模型预测结果的最大相对误差小于10%,预测准确性良好,且该方法具有较高的计算效率,可以为断层作用下埋地管道的应变设计与评估提供参考。