共查询到19条相似文献，搜索用时 62 毫秒
1.
二维场地液化势预测的神经网络方法 总被引：4，自引：1，他引：3
基于人工神经网络,提出了场地液化势预测模型。场地液化势的空间数据结构特征可由不同参数的自回归神经网络（GRNN）来模拟。该预测模型的一个重要参数spread可用地质统计学（Kriging）方法中的交叉验证技术来确定。研究表明,在最优spread参数条件下GRNN能够较好地映射场地液化势数据结构特征。用GRNN模型预测结果与经典的Kriging估计方法所得到的结果十分吻合。GRNN模型可以用于二维空间数据的预测及基于GIS决策系统。 相似文献
2.
基于MATLAB神经网络工具箱的岩爆预测模型 总被引：6，自引：0，他引：6
文章介绍了BP人工神经网络的基本原理，针对其收敛差的缺点，发挥MATLAB神经网络工具箱的优势，分别采用VLBP和LMBP算法建立了改进后的BP神经网络。对于影响岩爆发生的关键因素，总结了专家经验，选取地下硐室围岩最大切向应力与岩石单轴抗压强度比值、岩石单轴抗压强度和抗拉强度比值和岩石冲击性倾向指数作为岩爆预测的评判指标，建立了岩爆预测的神经网络模型，并利用国内外一些岩石地下工程实例进行分析计算校验，计算结果表明，用该模型进行岩爆预测是可行有效的。 相似文献
3.
巷道围岩参数的人工神经网络预测 总被引：7，自引：0，他引：7
应用人工智能方法解决地下工程问题,提出了预测巷道围岩参数的人工神经网络预测法,构造了预测围岩参数的神经网络模型。预测结果证明,该模型具有很高的预测精度。提出的方法有一定的实用价值和参考价值。 相似文献
4.
基于人工神经网络基本原理和方法,构建多隐层BP神经网络径流预测模型,以新疆伊犁河雅马渡站径流预测为例进行分析,并构建常规单隐层BP以及RBF、GRNN神经网络模型作为对比分析模型,将各模型预测结果与文献[1]中的预测结果进行比较,结果表明:(1)多隐层BP神经网络径流预测模型泛化能力强,预测精度高,算法稳定,模型精度优于IEA-BP网络模型,表明研究建立的多隐层BP神经网络模型用于径流预测是合理可行的,是一种可以应用于水文径流预测预报的新方法.(2)RBF、GRNN神经网络径流预测模型预测精度高于常规单隐层BP网络模型,且RBF与GRNN神经网络模型具有收敛速度快、预测精度高、调整参数少,不易陷入局部极小值等优点,可以更快地预测网络,具有较大的计算优势. 相似文献
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判别岩石所处的变形破坏阶段是分析岩石变化过程的重要基础。由于室内试验视频数据具有很好的等时距分布特征,可以使用基于长短期记忆的神经网络（LSTM-NN）模型判别外荷作用下岩石的变形破坏阶段。本文根据花岗岩室内单轴压缩试验所得应力-应变曲线和试验视频图像中裂隙的分布情况,将岩石变形破坏过程分成岩石压密阶段、弹性变形阶段、裂隙扩展阶段、整体破坏阶段,在提取不同阶段不同组分主要数字特征参数（面积）基础上,建立了基于LSTM-NN模型的岩石变形破坏阶段分类网络,分析了模型主要参数（学习率和最大周期等）对分类准确性的影响,使用所建模型对岩石所处变形破坏阶段进行了判别。结果表明,在LSTM-NN模型参数中,学习率和最大周期对变形破坏阶段判别准确率的影响较大,二者分别为0.005和200时的判别准确率达到最高;对于整个变形破坏阶段来说,LSTM-NN模型对裂隙扩展阶段预测的判别效果最好、对整体破坏阶段预测的判别效果最差;对于花岗岩中不同组分来说,LSTM-NN模型对变形破坏阶段预测准确性高低的顺序是裂隙、黑云母、长石、石英。 相似文献
7.
通过对广州市南沙地区大量软土物理力学试验和微结构分析,获取了40组软土试样的物理力学性质指标和微观结构参数。综合运用灰色关联分析的数据分析能力和人工神经网络的非线性映射功能,建立了软土物理力学性质指标与微结构参数的灰色关联-径向基神经网络模型。该模型利用灰色关联分析方法对数据进行预处理,提取重要因子作为网络的输入,而径向基神经网络充分利用样本数据信息,自适应确定隐含层节点个数、径向基函数中心、宽度以及网络的权系数。克服了传统RBF网络隐层节点数为样本个数,当数据较多时导致网络结构庞大、学习速度慢的缺点。通过模型A和模型B的实例研究表明,该方法简化了网络结构,提高了训练速度和预测精度,为软土物理力学性质与微结构参数关系的定量研究提供了一条有效途径。 相似文献
8.
基坑施工变形的时间序列预报问题 总被引：1，自引：0，他引：1
本文在分析灰色系统与人工神经网络基本原理的基础上,结合前人研究成果和实例分析,指出灰色系统用于预测基坑变形存在的若干问题,认为灰色系统不宜用于地下连续墙的水平位移预测,在其他变形预测中也要慎用。建立了基坑变形时间序列预测的神经网络模型,并用实例进行了论证。实例研究结果表明：神经网络是解决基坑变形时间序列预报的有效方法,在地下工程中具有非常好的应用前景。 相似文献
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岩石细观统计渗流模型研究(Ⅰ): 理论模型 总被引：5，自引：2，他引：5
渗流模型是岩石流固耦合研究的一个关键问题。岩石在裂纹开始产生至裂纹充分贯通前，其渗透性质受内部孔隙和不断演化的微裂纹的共同控制。目前，岩石的渗流模型一般只是针对单纯的裂隙（或裂隙网络）或孔隙建立的，因此，不能很好地描述岩石裂纹扩展演化过程中的渗透性质和现象。针对这一问题，提出了岩石细观统计渗流模型，该模型将岩石裂纹演化过程中的渗透性视为一个非线性动态过程，同时，考虑了裂纹扩展演化和孔隙渗流的共同作用。因此，该模型可用于分析岩石裂纹扩展过程中的渗透性演化。 相似文献
11.
《Geomechanics and Geoengineering》2013,8(1):53-61
In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill bit diameter, drill bit speed, penetration rate of the drill bit and equivalent sound level (Leq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) of intact rock for output, was established. The constructed models were checked using various prediction performance indices. Goodness of the fit measures revealed that recommended ANN model fitted the data as accurately as experimental results, indicating the usefulness of artificial neural networks in predicting rock properties. 相似文献
12.
The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN. 相似文献
13.
Masoud Monjezi Hasan Ali Mohamadi Bahare Barati Manoj Khandelwal 《Arabian Journal of Geosciences》2014,7(2):505-511
In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20–30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height. 相似文献
14.
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
15.
Mohammad Rezaei Masoud Monjezi Saeed Ghorbani Moghaddam Farhad Farzaneh 《Arabian Journal of Geosciences》2011,5(5):1031-1037
Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E a), mean relative error (E r), and determination coefficient (R 2) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden). 相似文献
16.
This paper demonstrates the applicability of cognitive systems or neural networks in predicting the drillibality of rocks
and wear factor using engineering properties of rocks. Drillability of rocks is a useful guide for evaluating the suitability
of drills for different ground operations. The wear factor of different materials subsequently helps in the selection of proper
drills for different drilling operations. Different rocks were tested for Protodyakonov index, impact strength index, shore
hardness number, Schmidt hammer number, drillability and micro bit chisels for wear factor. The data obtained from the tests
were used to train and test the neural network. Results from the analysis demonstrate that cognitive systems are an effective
tool in the prediction and suitability of drilling operations. Application of these predictive models can be a useful tool
to obtain the value of these important parameters, they can save time and help to avoid the tedious process of instrumentation. 相似文献
17.
Sajad Haghir Chehreghani Aref Alipour Mehdi Eskandarzade 《Journal of the Geological Society of India》2011,78(3):271-277
One important decision in design of surface mine is the selection of mine equipment and plant. Demand for mechanical excavation
is growing in mining industry because of its high productivity and excavation in large scale with lower costs. Several models
have been developed over the years to evaluate the ease of excavation and machine performance against rock mass properties.
Due to complexity of excavation process and large number of effective parameters, approaches made for this purpose are essentially
empirical. There are many uncertainties in results of these models. An attempt is made in this paper to revise the exisiting
models. Neural network models for estimation of rock mass excavatability and production rate of VASM-2D excavating machine
at Limestone quarry in Retznei, Austria, is presented. Input parameters of this model are Uniaxial compressive strength, tensile
strength and discontinuities spacing of rocks. Output is the specific excavation rate per power consumption (bcm/Kwh) as the
productivity indicator. Average of deviation between actual data and results estimated by neural network model was only 15%
which is in an acceptable range. 相似文献
18.
The process of drilling, in general, always produces sound. Though sound is used as a diagnostic tool in mechanical industry, its application in predicting rock property is not much explored. In this study, an attempt has been made to estimate rock properties such as uniaxial compressive strength, Schmidt rebound number and Young's modulus using sound level produced during rotary drilling. For this purpose, a computer numerical controlled vertical milling centre was used for drilling holes with drill bit diameters ranging from 6 to 20 mm with a shank length of 40 mm. Fourteen different rock types were tested. The study was carried out to develop the empirical relations using multiple regression analysis between sound level produced during drilling and rock properties considering the effects of drill bit diameter, drill bit speed and drill bit penetration rate. The F-test was used to check the validity of the developed models. The measured rock property values and the values calculated from the developed regression model are fairly close, indicating that the developed models could be efficiently used with acceptable accuracy in prediction of rock properties. 相似文献
19.
Prediction and controlling of flyrock in blasting operation using artificial neural network 总被引：4，自引：1，他引：3
M. Monjezi Amir Bahrami Ali Yazdian Varjani Ahmad Reza Sayadi 《Arabian Journal of Geosciences》2011,4(3-4):421-425
Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine. 相似文献