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

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.     

Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks

Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and utilize the methane for energy production.Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and operational factors. This study proposes a principle component analysis (PCA) and artificial neural network (ANN)-based approach to predict the ventilation methane emission rates of U.S. longwall mines.Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the data set and network structures were used for network training and testing to achieve minimum mean square errors and high correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall ventilation emission rates.     

Support capacity and roof behaviour at longwall faces with shield supports

Summary Field investigations on seven U.S. longwall coal faces were carried out to examine the behaviour and interaction of roof, floor and support on longwall faces equipped with hydraulic powered supports. The most important factor in determining face stability was found to be periodic weighting and an empirical design equation was developed for support capacity. The interaction of various elements in the face system is examined and discussed.     

自适应BP算法及其在河道洪水预报上的应用

提出一种改进的BP算法,即自适应BP算法。该方法采用两种策略:一是在权重修改公式中加动量项;二是学习率随总误差的变化作自适应调整,亦即总误差增加时,学习率将减小,反之学习率增大。以上两种策略能有效的抑制网络陷于局部极小并缩短了学习时间。实例研究表明,该算法用于河道洪水的预报,能取得令人满意的结果。     

Forecasting vertical ground surface movement from shrinking/swelling soils with artificial neural networks

Artificial neural networks (ANNs) are used to estimate vertical ground surface movement when soils expand and contract due to changes in soil moisture content caused by changing climate conditions. Several counterpropagation ANN test cases were investigated to map climate data (i.e. temperature and rainfall) to vertical ground surface movement at field sites in Texas and Australia. Three of the four ANN test cases use a historical time series of climate data to forecast ground surface elevation relative to a specified datum. The fourth ANN test case predicts the rate of ground surface movement, and requires post‐processing of the predicted rates to calculate ground surface elevation relative to a specified datum. The counterpropagation network has demonstrated a successful mapping of temperature and rainfall data to vertical ground surface movement at a field site when it is trained with a subset of data from the same field site (test cases 1 and 2). The results of training an ANN on one field site and testing it on another field site (test cases 3 and 4) demonstrate the ability of the ANN to capture trends in vertical ground surface movement. When compared with the predictions from a physics‐based method (shrink test‐water content method) that requires measurements/estimates of changes in soil water content, the ANN‐based predictions (based on climatic changes) captured the trends in the field measurements of shrinking–swelling soil surface movements equally well. These findings are promising and merit further investigation with data from additional field sites. Copyright © 2007 John Wiley & Sons, Ltd.     

基于人工神经网络的岩性识别方法

从神经网络的机理、特点出发,探讨了采用神经网络技术进行测井岩性识别的可行性及优越性,并以十红滩地区的找矿目的层为对象,进行了岩性分析与对比,为该方法的进一步应用开拓了前景。     

人工神经网络在爆破块度预测中的应用研究

利用人工神经网络模型对爆破块度进行预测,实验结果表明,该方法是完全可行的。通过对实验样本数据进行归一化处理后再对人工神经网络模型进行训练和预测,其预测精度会得到大大提高。     

Estimation of effective stress parameter of unsaturated soils by using artificial neural networks

Great efforts are required for determination of the effective stress parameter χ, applying the unsaturated testing procedure, since unsaturated soils that have the three‐phase system exhibit complex mechanical behavior. Therefore, it seems more reasonable to use the empirical methods for estimation of χ. The objective of this study is to investigate the practicability of using artificial neural networks (ANNs) to model the complex relationship between basic soil parameters, matric suction and the parameter χ. Five ANN models with different input parameters were developed. Feed‐forward back propagation was applied in the analyses as a learning algorithm. The data collected from the available literature were used for training and testing the ANN models. Furthermore, unsaturated triaxial tests were carried out under drained condition on compacted specimens. ANN models were validated by a part of data sets collected from the literature and data obtained from the current study, which were not included in the training phase. The analyses showed that the results obtained from ANN models are in satisfactory agreement with the experimental results and ANNs can be used as reliable tool for prediction of χ. Copyright © 2007 John Wiley & Sons, Ltd.     

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.     

Prediction of surface crown pillar stability using artificial neural networks

A relatively novel technique, artificial neural networks (ANN), is used in predicting the stability of crown pillars left over large excavations. Data for the training and verification of the networks were obtained from the literature. Four artificial networks, based on two different architectures, were used. The networks used different numbers of input parameters to predict the stability or failure of crown pillars. Multi‐layer perceptron networks using mine type, dip of orebody, overburden thickness, pillar thickness, pillar length, stope height, backfill height, Rock Mass Rating (RMR) of the host rock and RMR of the orebody showed excellent performance in training and verification. Adding three more variables, namely pillar width, rock density and pillar thickness to width ratio, showed symptoms of over‐learning without degrading performance significantly. Radial basis function networks were capable of predicting crown pillar behaviour on the basis of few input functions. It was shown that mine type, dip and pillar thickness to width ratio can be used for a preliminary estimation of stability. Copyright © 2006 John Wiley & Sons, Ltd.     

基于神经网络的沉陷区水深遥感研究

为获取煤矿积水沉陷区遥感影像数据与沉陷区水深的定量关系,建立了BP神经网络水深反演模型,并对淮南潘一矿积水沉陷区水深进行了反演。首先对Landsat卫星影像数据(TM影像)进行几何校正、大气校正和沉陷区范围提取等,然后输出像元反射率值,并与水深实测控制点坐标匹配,使水深值与反射率值对应。实验结果表明:以水深值2 m为阈值,水深值小于2 m的区域,模型反演水深值与实测水深值的平均绝对误差为0.166 3 m,平均相对误差为13.29%;水深值为2~6 m的区域,模型反演水深值与实测水深值平均绝对误差为0.578 6 m,平均相对误差为15.20%。     

Modeling soil collapse by artificial neural networks

The feasibility of using neural networks to model the complex relationship between soil parameters, loading conditions, and the collapse potential is investigated in this paper. A back propagation neural network process was used in this study. The neural network was trained using experimental data. The experimental program involved the assessment of the collapse potential using the one-dimensional oedometer apparatus. To cover the broadest possible scope of data, a total of eight types of soils were selected covering a wide range of gradation. Various conditions of water content, unit weights and applied pressures were imposed on the soils. For each placement condition, three samples were prepared and tested with the measured collapse potential values averaged to obtain a representative data point. This resulted in 414 collapse tests with 138 average test values, which were divided into two groups. Group I, consisting of 82 data points, was used to train the neural networks for a specific paradigm. Training was carried out until the mean sum squared error (MSSE) was minimized. The model consisting of eight hidden nodes and six variables was the most successful. These variables were: soil coefficient of uniformity, initial water content, compaction unit weight, applied pressure at wetting, percent sand and percent clay. Once the neural networks have been deemed fully trained its accuracy in predicting collapse potential was tested using group II of the experimental data. The model was further validated using information available in the literature. The data used in both the testing and validation phases were not included in the training phase. The results proved that neural networks are very efficient in assessing the complex behavior of collapsible soils using minimal processing of data. This revised version was published online in July 2006 with corrections to the Cover Date.     

基于人工神经网络的数字识别技术

章介绍了一种利用BP神经网络进行数字识别的方法。首先,对数字进行特征提取,获得采样数据．再对样本数据进行学习和训练,形成良好的网络,然后对与训练数字有所区别的数字进行检测,达到了一定的准确度,表明了该方法在实际应用中具有可行性。本共分为五部分,第一部分对神经网络的基本原理进行了简单介绍,第二部分讲述了反向传播算法的基本原理,第三部分讲述了数字识别的基本原理,第四部分讲述了基于人工神经网络的数字识别的实例,第五部分对上述内容作了简要小结。     

BP神经网络洪水预报模型在洪水预报系统中的应用

采用相关分析法,在区域降水、观测断面流量(或水位)因子中识别出影响预报断面径流过程的主要变量,在多个观测断面的数据均为流量情况下,采用基于时延组合的合成流量为影响预报断面径流过程的变量,采用自相关分析法,识别出影响预报断面径流过程的前期流量(或水位),以这些变量为BP神经网络模型的输入,以预报断面的流量(或水位)为模型的输出,在BP神经网络隐层节点数自动优选的基础上,构建了基于BP神经网络的洪水预报模型。将模型载入中国洪水预报系统中,应用结果表明:模型在历史洪水训练样本具有一定代表性的情况下,可获得较高的预报精度。     

BP神经网络在BOULTON法确定潜水含水层参数中的应用

本文在分析具有迟后排水的N.S.Boulton第二潜水井流模型解析解的基础上,利用复合高斯求积法和学习速率、动量因子自适应的BP神经网络相结合对该模型数值求解,提出了确定潜水含水层系统参数的Boulton-BP法.实例计算结果表明,用Boulton-BP法求得参数正演出的降深-时间过程与抽水试验所得降深-时间过程拟合很好.该方法简单,快速,不需要将抽水试验所得降深-时间过程分为前、后两段分别求参,不仅简化潜水含水层的求参过程,而且有良好的求参效果.     

基于进化神经网络混凝土大坝变形预测

根据丰满大坝多年变形观测数据,建立了基于进化神经网络混凝土大坝变形预测方法。经典的BP神经网络的缺陷在于收敛速度慢和泛化能力弱等特性。与普通的多元回归方法和传统的BP神经网络相比,采用遗传算法训练的人工神经网络预测模型预报大坝的变形具有精度高和全局收敛的特点。在丰满大坝工程实际应用表明,所建立的基于进化神经网络混凝土大坝变形预报方法与广泛采用的统计方法相比,可以显著提高大坝变形预报精度。     

Estimation of soil compaction parameters by using statistical analyses and artificial neural networks

This study presents the application of different methods (simple–multiple analysis and artificial neural networks) for the estimation of the compaction parameters (maximum dry unit weight and optimum moisture content) from classification properties of the soils. Compaction parameters can only be defined experimentally by Proctor tests. The data collected from the dams in some areas of Nigde (Turkey) were used for the estimation of soil compaction parameters. Regression analysis and artificial neural network estimation indicated strong correlations (r ² = 0.70–0.95) between the compaction parameters and soil classification properties. It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.     

巷道围岩参数的人工神经网络预测

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。     

A direct inversion scheme for deep resistivity sounding data using artificial neural networks

Initialization of model parameters is crucial in the conventional 1D inversion of DC electrical data, since a poor guess may result in undesired parameter estimations. In the present work, we investigate the performance of neural networks in the direct inversion of DC sounding data, without the need ofa priori information. We introduce a two-step network approach where the first network identifies the curve type, followed by the model parameter estimation using the second network. This approach provides the flexibility to accommodate all the characteristic sounding curve types with a wide range of resistivity and thickness. Here we realize a three layer feed-forward neural network with fast back propagation learning algorithms performing well. The basic data sets for training and testing were simulated on the basis of available deep resistivity sounding (DRS) data from the crystalline terrains of south India. The optimum network parameters and performance were decided as a function of the testing error convergence with respect to the network training error. On adequate training, the final weights simulate faithfully to recover resistivity and thickness on new data. The small discrepancies noticed, however, are well within the resolvability of resistivity sounding curve interpretations.     

Forecasting gob gas venthole production performances using intelligent computing methods for optimum methane control in longwall coal mines

Gob gas ventholes (GGV) are used to control methane inflows into a longwall operation by capturing it within the overlying fractured strata before it enters the work environment. Thus, it is important to understand the effects of various factors, such as drilling parameters, location of borehole, applied vacuum by exhausters and mining/panel parameters in order to be able to evaluate the performance of GGVs and to predict their effectiveness in controlling methane emissions. However, a practical model for this purpose currently does not exist.In this paper, we analyzed the total gas flow rates and methane percentages from 10 GGVs located on three adjacent panels operated in Pittsburgh coalbed in Southwestern Pennsylvania section of Northern Appalachian basin. The ventholes were drilled from different surface elevations and were located at varying distances from the start-up ends of the panels and from the tailgate entries. Exhauster pressures, casing diameters, location of longwall face and mining rates and production data were also recorded. These data were incorporated into a multilayer-perceptron (MLP) type artificial neural network (ANN) to model venthole production. The results showed that the two-hidden layer model predicted total production and the methane content of the GGVs with more than 90% accuracy. The ANN model was further used to conduct sensitivity analyses about the mean of the input variables to determine the effect of each input variable on the predicted production performance of GGVs.