Energy-based probabilistic evaluation of soil liquefaction   总被引：3，自引：0，他引：3  

Y.R. Chen  S.C. Hsieh  J.W. Chen  C.C. Shih 《Soil Dynamics and Earthquake Engineering》2005,25(1):55-68

This paper presents a seismic wave energy-based method with back-propagation neural networks to assess the liquefaction probability. An empirical equation and Fourier spectrum of acceleration are employed, respectively, to calculate the seismic wave energy. Discriminant analysis is used to determine the equation of the boundary curve separating the data points with and without liquefaction. The proposed method shows capability in evaluating the probability of soil liquefaction based on the boundary curve and a logarithm normal distribution.  相似文献   

A comparative study of ANN and Neuro-fuzzy for the prediction of dynamic constant of rockmass   总被引：1，自引：0，他引：1  

T. N. Singh  R. Kanchan  A. K. Verma  K. Saigal 《Journal of Earth System Science》2005,114(1):75-86

Physico-mechanical properties of rocks have great significance in all operational parts in mining activities, from exploration to final dispatch of material. Compressional wave velocity (p-wave velocity) and anisotropic behaviour of rocks are two such properties which help to understand the rock response under varying stress conditions. They also influence the breakage mechanism of rock. There are different methods to determine thep-wave velocity and anisotropyin situ and in the laboratory. These methods are cumbersome and time consuming. Fuzzy set theory, Fuzzy logic and Neural Networks techniques seem very well suited for typical geotechnical problems. In conjunction with statistics and conventional mathematical methods, hybrid methods can be developed that may prove to be a step forward in modeling geotechnical problems. Here, we have developed and compared two different models, Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and Artificial neural network systems, for the prediction of compressional wave velocity.  相似文献   

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  

冯劲  高广运 《岩土工程技术》2005,19(1):5-8

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   

Prediction of surface blast patterns in limestone quarries using artificial neural networks     

A. S. Tawadrou  P. D. Katsabani 《Fragblast: International Journal for Blasting and Fragmentation》2005,9(4):233-242

This paper is an application of artificial neural networks (ANNs) in the prediction of the geometry of surface blast patterns in limestone quarries. The built model uses 11 input parameters which affect the design of the pattern. These parameters are: formation dip, blasthole diameter, blasthole inclination, bench height, initiation system, specific gravity of the rock, compressive and tensile strength, Young's modulus, specific energy of the explosive and the average resulting fragmentation size. Detailed data from a previous investigation were used to train and verify the network and predict burden and spacing of a blast. The built model was used to conduct parametric studies to show the effect of blasthole diameter and bench height on pattern geometry.  相似文献   

Landslide hazard and risk mapping at catchment scale in the Arno River basin   总被引：6，自引：2，他引：6  

F. Catani  N. Casagli  L. Ermini  G. Righini  G. Menduni 《Landslides》2005,2(4):329-342

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   

关于BP神经网络转换GPS高程的若干问题   总被引：6，自引：0，他引：6  

高宁  高彩云  吴良才 《测绘工程》2006,15(3):67-69

BP神经网络用于GPS高程转换有着较高的精度,但也存在不少问题,如网络的隐含层和隐含层单元个数选取、参加学习的样本的质量如何影响仿真精度等。结合实例分析上述问题,得出一些结论。  相似文献   

BP神经网络在长期天气过程预报中的应用试验   总被引：3，自引：2，他引：3  

刘德李晶  李永华向波 李梗 《气象科技》2006,34(3):250-253

采用误差反传前向网络(简称BP网络)方法,以日、月相概率作为输入因子,建立长期天气预报模型。结果表明,模型的业务预报试验效果比较理想,对较大降水和升(降)温过程均有一定预报能力,相对于传统的单纯运用日、月相概率预报长期天气过程的方法,BP神经网络方法具有预报较客观、准确率较高等特点,在目前长期天气预报理论和数值预报模式尚不能用于实际业务的情况下具有较大的应用价值。  相似文献   

Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks     

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   

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   

New Indices for Characterizing Spatial Models of Ore Deposits by the Use of a Sensitivity Vector and an Influence Factor     

Katsuaki Koike  Setsuro Matsuda 《Mathematical Geology》2006,38(5):541-564

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