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981.
人工神经网络在基桩低应变完整性检测中的应用 总被引:2,自引:0,他引:2
目前基桩低应变完整性检测数据的后期处理有很多方法 ,但分析中人为干预较多。利用人工神经网络强大的非线性映射能力和学习训练功能 ,提出了基于BP网络的基桩完整性检测模型。该模型基于现场实测资料 ,避免了数据处理过程中各种人为干预。应用该模型对工程实例进行了分析 ,训练和测试网络结果说明该方法能够快速、方便地对基桩质量进行模式识别 相似文献
982.
We present the methodologies adopted and the outcomes obtained in the analysis of landslide risk in the basin of the Arno
River (Central Italy) in the framework of a project sponsored by the Basin Authority of the Arno River, started in the year
2002 and completed at the beginning of 2005. In particular, a complete set of methods and applications for the assessment
of landslide susceptibility and risk are described and discussed.
A new landslide inventory of the whole area was realized, using conventional (aerial-photo interpretation and field surveys)
and non-conventional methods (e.g. remote sensing techniques such as DInSAR and PS-InSAR).
The great majority of the mapped mass movements are rotational slides (75%), solifluctions and other shallow slow movements
(17%) and flows (5%), while soil slips, and other rapid landslides, seem less frequent everywhere within the basin. The relationships
between landslide characteristics and environmental factors have been assessed through statistical analysis. As expected,
the results show a strong control of land cover, lithology and morphology on landslide occurrence. The landslide frequency-size
distribution shows a typical scaling behaviour already underlined in other landslide inventories worldwide. The assessment
of landslide hazard in terms of probability of occurrence in a given time, based for mapped landslides on direct and indirect
observations of the state of activity and recurrence time, has been extended to landslide-free areas through the application
of statistical methods implemented in an artificial neural network (ANN). Unique conditions units (UCU) were defined by the
map overlay of landslide preparatory factors (lithology, land cover, slope gradient, slope curvature and upslope contributing
area) and afterwards used to construct a series of model vectors for the training and test of the ANN. Various different ANNs
were selected throughout the basin, until each UCU was assigned a degree of membership to a susceptibility and a hazard class.
Model validation confirms that prediction results are very good, with an average percentage of correctly recognized mass movements
of about 85%. The analysis also revealed the existence of a large number of unmapped mass movements, thus contributing to
the completeness of the final inventory. Temporal hazard was estimated via the translation of state of activity in recurrence
time and hence probability of occurrence. The following intersection of hazard values with vulnerability and exposure figures,
obtained by reclassification of digital vector mapping at 1:10,000 scale, lead to the definition of risk values for each terrain
unit for different periods of time into the future. The final results of the research are now undergoing a process of integration
and implementation within land planning and risk prevention policies and practices at local and national level. 相似文献
983.
本文以岫岩县作为研究区域,以自然流域作为评价单元格,采用人工神经网络对该区泥石流的区域危险性进行评价。结果显示,三级以上危险区囊括了99%的泥石流灾害点,评价结果符合实际情况。以流域作为评价单元充分体现了泥石流发生的空间特征和物理机制,危险性区划图直观明了。可为防灾减灾规划提供科学依据。 相似文献
984.
Geometry,kinematics and evolution of the Tongbai orogenic belt 总被引:2,自引:0,他引:2
1 Introduction spectively[2,3]. Several tectonic units such as the Bei- The Qinling-Dabie orogenic belt has attracted huaiyang, north Dabie, south Dabie and Susong belts worldwide attention by its very complex and abundant have been recognized in eastern Dabie[4]. Nine tec- geological characters, and has been a “hot point” of tonic units have been recognized in western Dabie and international geological research[1]. A vast amount of a more detailed division has been suggested especially … 相似文献
985.
Józef Kabiesz 《Geotechnical and Geological Engineering》2006,24(5):1131-1147
During hard coal mining operations conducted under conditions of rockburst hazard, one of the most important preventive measures
can be the prediction of occurrence time and location of the strong seismic mine tremors of energy E
s ⩾ 104 J. This is a very difficult task and the way it is being currently performed appears to be unsatisfactory. Therefore, attempts
have been made to use neural networks, specifically trained for this application. The paper presents an approach for determining
an influence of the type and shape of the input data on the efficiency of such a prediction. The considerations are based
on a selected example of the seismic activity recorded during longwall mining operations conducted in one of the Polish mines. 相似文献
986.
Visualization of Volcanic Rock Geochemical Data and Classification with Artificial Neural Networks 总被引:1,自引:0,他引:1
Juan Pablo Lacassie Javier Ruiz del Solar Barry Roser Francisco Hervé 《Mathematical Geology》2006,38(6):697-710
An unsupervised neural network technique, Growing Cell Structures (GCS) was used to visualize geochemical differences between
four different island arc volcanic rock types: basalts, andesites, dacites and rhyolites. The output of the method shows the
cluster structure of the dataset clearly, and the relevant geochemical patterns and relationships between its variables. The
data can be separated into four clusters, each associated with a specific volcanic rock type (basalt, andesite, dacite and
rhyolite), according to a unique combination of major element concentrations. Following clustering, performance of the trained
GCS network as a classifier of volcanic rock type was tested using two test datasets with major element concentration data
for 312 and 496 island arc volcanic rock samples of known volcanic type. Preliminary classification results are promising.
In the first test dataset 94% of basalts, 76% of andesites, 83% of dacites and 100% of the rhyolites were classified correctly.
Successful classification rates in the second dataset were 100%, 80%, 77%, and 98% respectively. The success of the analysis
suggests that neural networks analysis constitutes a useful analytical tool for identification of natural clusters and examination
of the relationships between numeric variables in large datasets, and that can be used for automatic classification of new
data. 相似文献
987.
Precise spatial estimation of ore grades and impurity contents from sample data limited in amount and location is indispensable
to metallic and nonmetallic resource exploration. One of the advantages of using geostatistics for this purpose is that it
can incorporate multivariate data into spatial estimation of one variable. However, there are two weak points concerning technical
and post-processing problems. First is the difficulty in application to geologic data in which spatial correlations are not
clear because of intrinsic nonlinear behavior. Second is the absence of indices to interpret the mechanisms and factors which
govern the spatial distribution. To address these problems, a spatial method of modeling based on a feedforward neural network,
SLANS, which recognizes the relationship between the data value and location by considering supplementary attributes such
as lithology and biostratigraphy, and a sensitivity analysis using this network were developed. These methods were applied
to two case studies, genetic mechanisms of kuroko deposits and quality assessment of a limestone mine. The first case study
is a spatial analysis of principal metals of kuroko deposits (volcanogenic massive sulfide deposits) in the Hokuroku district,
northern Japan. It was clarified that upward and downward sensitivity vectors were distinguished near the deposits inside
and outside the tectonic basin, respectively. Sensitivity analysis for the second case study showed a strong effect of crystalline
limestone on the important impurity, P2O5 contents. Hydrothermal alteration, which could cause leaching and secondary concentration of phosphorus, is considered to
have produced this effect. 相似文献
988.
Recent emergency flood situations at European rivers have revealed the demand for better and in-time information for citizens
in flood prone areas about flood development, as well as better coordination of resources and actions during pre-flood phases
and its critical stage. Information and Communication Technology (ICT) has a large potential to improve the situation. Decisions
may be supported by information about resources available at the region and national level, by information about means and
access to critical locations at the prevention as well as the evacuation phases, and by including citizens as well as managers
into one common information and communication process. The paper outlines the potential of ICT for these aspects. 相似文献
989.
基于RBF神经网络的遥感影像分类器设计 总被引:2,自引:0,他引:2
设计了一种运用径向基函数神经网络进行遥感影像分类的监督分类器,以实际的遥感光谱影像分类为例,将分类效果同传统的最小欧氏距离法分类进行比较,探讨了RBF分类的优越性,结果表明RBF神经网络是一种更为有效的图像分类器。 相似文献
990.
基于模糊神经网络的土地合理储备量预测研究 总被引:8,自引:0,他引:8
将神经网络和模糊理论相结合建立模糊神经网络模型,从模糊神经网络角度并运用灰色系统理论对建设用地量进行预测,并应用于重庆市2005~2010年的建设用地量预测.计算分析结果表明,该模型具有良好的可行性和合理性,可以为确定土地合理储备量提供依据. 相似文献