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Two artificial neural network models for the prediction of elastic modulus of jointed rock mass from the elastic modulus of
corresponding intact rock and joint parameters have been demonstrated in this paper. The data collected from uniaxial and
triaxial compression tests on different rocks with different joint configurations and different confining pressure conditions,
reported in the literature are used as input for training the networks. Important joint properties like joint frequency, joint
inclination and roughness of joints are considered separately for making the network more versatile. Two different techniques
of artificial neural networks namely feed forward back propagation (FFBP) and radial basis function (RBF) are used to predict
the elastic modulus ratio. 相似文献
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Christie(1962)、Smitch(1972)和张翊钧(1985)都提出了钠质斜长石的Δ131-131(或σ)、An与温度T的关系图,后来又提出了钠质斜长石变形温度的计算公式。但由于图解的不便之处及目前应用的公式中推断分界点的不确定性和σ、An与T之间存在一种非线性关系的特点,本文基于具有高度非线性映射能力的人工神经网络,提出了求解岩石变形温度的新方法。文中先介绍了变形温度计的研究概况,然 相似文献
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Christie(1962)、Smith(1972)和张翊钧(1985)都提出了钠质斜长石的△131-(或σ)、An与温度T的关系图,后来又提出了钠质斜长石变形温度的计算公式。但由于图解的不便之处及目前应用的公式中推断分界点的不确定性和σ、An与T之间存在一种非线性关系的特点,本文基于具有高度非线性映射能力的人工神经网络,提出了求解岩石变形温度的新方法。文中先介绍了变形温度计的研究概况,然后阐述了ANN(ArtificialNeuralNetwor)模型,最后应用该模型估算岩石变形时的温度,经对比得出,利用人工神经网络在岩石变形温度估算中具有良好的应用效果。 相似文献
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V. Palchik 《Rock Mechanics and Rock Engineering》2011,44(1):121-128
The ratios M
R = E/σ
c for 11 heterogeneous carbonate (dolomites, limestones and chalks) rock formations collected from different regions of Israel
were examined. Sixty-eight uniaxial compressive tests were conducted on weak-to-strong (5 MPa < σ
c < 100 MPa) and very strong (σ
c > 100 MPa) rock samples exhibiting wide ranges of elastic modulus (E = 6100–82300 MPa), uniaxial compressive strength (σ
c = 14–273.9 MPa), Poisson's ratio (ν = 0.13–0.49), and dry bulk density (ρ = 1.7–2.7 g/cm3). The observed range of M
R = 60.9–1011.4 and mean value of M
R = 380.5 are compared with the results obtained by Deere (Rock mechanics in engineering practice, Wiley, London, pp 1–20,
1968) for limestones and dolomites, and the statistical analysis of M
R distribution is performed. Mutual relations between E, σ
c, ρ, M
R for all studied rocks, and separately for concrete rock formations are revealed. Linear multiple correlations between E on the one hand and σ
c and ρ on the other for Nekorot and Bina limestone and Aminadav dolomite are obtained. It is established that the elastic modulus
and M
R in very strong carbonate samples are more correlated with ρ−σ
c combination and ε
a max, respectively, than in weak to strong samples. The relation between M
R and maximum axial strain (ε
a max) for all studied rock samples (weak-to-strong and very strong) is discussed. 相似文献
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本文运用T.kohonen自组织人工神经网络模型,处理矿产资源统计预测问题,得出与数量化理论Ⅱ处理极为相似的结果。 相似文献
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我国的兖州、淮北、平顶山、焦作等矿区,普遍存在着煤层缺失、剥失、分叉、合并等现象,煤层厚度变化对煤炭开采产生很大影响.据资料统计,如果实际煤厚比设计煤厚变薄10%~20%时,煤炭产量就会下降35%~40%.煤田高分辨率三维地震勘探的开展,为解决地质问题提供了丰富的三维数据体,特别是为BP人工神经网络法研究煤层厚度提供了多种地震波属性. 相似文献
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分析了前人关于融沉系数经验方法的研究结果,结果显示,与融沉系数关系最为密切的物性参数为液塑限、粉黏粒含量、干密度和含水量(含冰量).为了能够综合描述诸因素与融沉系数的经验关系,以兰州黄土和青藏黏土为试验对象,得到了两种具有不同物性参数的土在不同含水量和干密度条件下的融沉系数.采用BP神经网络算法对试验数据进行学习训练,... 相似文献
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ANN在水文计算中的初步应用 总被引:2,自引:1,他引:2
人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理,具有自学习和自适应等一系列优点,因而用它来进行有关的水文计算是可行的.针对水文计算问题,初步建立了基于神经网络的计算分析系统,给出了应用实例. 相似文献
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The geochemical discriminate diagrams help to distinguish the volcanics recovered from different tectonic settings but these
diagrams tend to group the ocean floor basalts (OFB) under one class i.e., as mid-oceanic ridge basalts (MORB). Hence, a method
is specifically needed to identify the OFB as normal (N-MORB), enriched (E-MORB) and ocean island basalts (OIB). 相似文献
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盾构施工地面长期沉降的神经网络预测 总被引:1,自引:0,他引:1
基于逆传播人工神经网络方法,建立了盾构施工地面长期沉降的非线性预测模型,建立了沉降与诸多影响因素:所处位置、时间、上覆土性参数及盾构施工参数等的关系模型。通过在上海地铁2号线龙东路一中央公园站区间资料的验证,发现与实际比较吻合。 相似文献
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聚丙烯纤维低弹模混凝土以其优良的工作性能和力学性能成为近年来我国水利工程大坝防渗墙采用的主要材料之一.在室内试验的基础上,对聚丙烯低弹模混凝土的强度、塌落度特性进行了研究;开发了一套混凝土耐久性渗透水采集仪,对聚丙烯低弹模混凝土的渗透性和耐久性进行了研究,试验结果表明:与传统低弹模混凝土相比,聚丙烯低弹模混凝土的抗压、弹模变化不大,抗拉强度有提高,抗渗性能明显改善,渗透系数降低了一个数量级左右,耐久性年限可以进一步提高,研究结果为聚丙烯低弹模混凝土大坝防渗墙的工程应用提供了技术支撑. 相似文献
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M. Monjezi S. M. Hashemi Rizi V. Johari Majd Manoj Khandelwal 《Geotechnical and Geological Engineering》2014,32(1):21-30
Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R2 = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively. 相似文献
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