Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

A Practical Procedure for the Measurement of Fragmentation by Blasting by Image Analysis

Summary. The operation of a digital image analysis system in a limestone quarry is described. The calibration of the system, required in order to obtain moderately reliable fragmentation values, is done from muckpile sieving data by tuning the image analysis software settings so that the fragmentation curve obtained matches as close as possible the sieving. The sieving data have also been used to extend the fragment size distribution curves measured to sizes below the system’s optical resolution and to process the results in terms of fragmented rock, discounting the material coming from a loose overburden (natural fines) that is cast together with the fragmented rock. Automatic and manual operation modes of the image analysis are compared. The total fragmentation of a blast is obtained from the analysis of twenty photographs; a criterion for the elimination of outlier photographs has been adopted using a robust statistic. The limitations of the measurement system due to sampling, image processing and fines corrections are discussed and the errors estimated whenever possible. An analysis of consistency of the results based on the known amount of natural fines is made. Blasts with large differences in the amount of fines require a differentiated treatment, as the fine sizes tend to be the more underestimated in the image analysis as they become more abundant; this has been accomplished by means of a variable fines adjustment factor. Despite of the unavoidable errors and the large dispersion always associated with large-scale rock blasting data, the system is sensitive to relative changes in fragmentation.     

基于GIS与ANN耦合技术的地裂缝灾情非线性模拟预测系统——以山西榆次地裂缝灾害为例

现代地裂缝在世界许多国家普遍存在 ,已成为当今世界范围内的主要地质灾害之一。本文在详尽分析了山西榆次地裂缝的各个致灾因子的基础上 ,利用GIS技术建立了地质学意义上的专题层 ;然后采用人工神经网络技术构建出了地裂缝灾害活动性的评价模型 ,并建立了地裂缝活动性的评价系统 ,对榆次地裂缝进行了灾害活动性评价 ,为榆次市城建和国土规划等部门的正确决策提供了重要的科学依据     

喀什"奇异震群"与构造地震的对比研究

20 0 1年 11月 14、 15日新疆喀什台记录到 86次振动事件 (“奇异震群”事件 )。为识别其性质 ,应用波谱分析、偏振分析等方法 ,对该事件和同范围构造地震的地震波特征进行了对比研究 ,发现 :(1)喀什“震群”的全波列衰减比构造地震的全波列衰减快很多 ;(2 )与构造地震的速度谱相比 ,“震群”的频谱相对较为“集中”,而构造地震的则较为“发散”;(3)“震群”位移谱的拐角频率和卓越频率均较构造地震的相应量高出一倍多 ,且离散较小。通过对喀什“奇异震群”这些特征进行分析研究 ,认为这一事件极有可能是人工事件     

Classification of artificial (man-made) ground

The legacy inherited from anthropogenic processes needs to be addressed in order to provide reliable and up-to-date ground information relevant to development and regeneration in the urban environment. The legacy includes voids as well as anthropogenic deposits (artificial ground). Their characteristics derive from former quarrying and mining activities industrial processes creating derelict ground, variably consolidated made ground, and contaminated groundwater and soils. All need to be systematically assessed to inform the planning process and provide the basis for engineering solutions. Site-specific investigation needs to be conducted on the back of good quality geoscientific data. This comes from ‘field’ survey, remotely sensed data interpretation, historical maps, soil geochemical sampling, and geotechnical investigation. Three-dimensional and, in the future, four dimensional, characterization of superficial deposits is required to reach an understanding of the potential spatial lithological variability of artificial ground and the geometry of important surfaces, i.e. the boundary conditions. The classification scheme for artificial ground outlined in this paper and adopted by the British Geological Survey, will help in achieving this understanding.     

Application of visual,statistical and artificial neural network methods in the differentiation of water from the exploited aquifers in Kuwait

The ability of artificial neural network to differentiate water samples from the two aquifers of Kuwait on the basis of their major ion chemistry has been demonstrated. The major ion concentration distribution in the groundwater of the Kuwait Group and the Dammam Formation aquifers of Kuwait appears very similar. Cross-plots, supported by the discriminant function analysis of the data, however, suggest that there are some subtle differences in the overall composition of the water from the two aquifers that make it possible to differentiate the water from the two aquifers in almost 80% of the cases. An artificial neural network improved the differentiation capability to 90% of the cases. It is also possible to estimate the fraction of Kuwait Group water in the flow stream of dually completed wells with the help of an artificial neural network developed for this purpose. Electronic Publication     

福建省干旱概况及夏旱期间人工增雨条件分析

对福建省干旱的成因和干旱的环流形势进行了探讨,着重分析了夏旱期间人工增雨作业的天气形势、云状、回波特征及云顶温度等条件,结果表明福建省干旱的形成与大型环流形势、地形、地貌及土壤植被条件有关;平均而言,闽东南沿海干旱多于内陆地区;西太平洋副热带高压是夏季致旱的主要天气系统;夏季,西风槽前型、台风外围影响型、副热带辐合带型是进行人工增雨作业的有利天气形势,Sc、Cb、Cu云都有一定的人工增雨作业条件,其中降水时长1～5 h,过程雨量1.0～5.0 mm的云是旱季作业比较适合的作业云.这些结果为开展人工增雨作业提供理论依据,达到解除或缓解干旱的目的.     

月降水量的神经网络混合预报模型研究

以均生函数表征预报量自身周期变化，结合500hPa月平均高度场和月平均海温场预报因子，采用神经网络方法建立了一种新的短期气候预报模型。分别以广西桂北、桂中和桂南6月降水量作为预报对象进行预报试验，结果表明，这种新的预报方法比均生函数回归预报模型及高度场、海温场预报因子的回归预报模型，具有更好的物理基础和预报能力。     

震后震型早期判定的人工神经网络方法

应用人工神经网络的方法，利用３０次强震震后１天和２天内的地震资料作为学习样本，对广西及其邻区发生的４次地震的震型作了早期预测判定，结果表明应用效果较好，正确率达７５％。该方法值得进一步研究。     

乌兰布和沙漠东北边缘植物群落物种多样性及其生态环境

应用群落多样性指数（Ｄ）、均匀度（Ｊ）、生态优势度（Ｃ）等指标，对乌兰布和沙漠东北边缘不同生境（荒漠区、绿洲外围、绿洲内）的天然植物群落进行了研究，结果表明这三种指标值能有效地表征群落的组成结构特征及生境的优劣；荒漠植物群落多样性指数（Ｄ＝０．６０３～２．９８４）和均匀度（Ｊ＝０．３３７～０．６９１）都较低，而生态优势度（Ｃ＝０．７２４～０．１９５）较高，说明荒漠植被生态环境的脆弱性，群落结构越复杂，多样性指数越高；多样性与生态环境关系密切，Ｄ和Ｊ随生境渐好而递增，Ｃ随生境渐好而递减，可作为环境评价指标；多样性在一定程度上体现了群落的发展阶段和稳定程度，越接近顶级阶段，多样性越高，且环境的改善有助于群落演替进程的加速；干旱区弃耕撩荒地次生演替植被，在弃耕初期多样性随弃耕年限增大而减小，后期处于一个较稳定的水平，且各演替阶段植物组成变化较大。