人工神经网络在实时卡钻预测中的应用研究

采用人工神经网络模型中BP模型对现场的钻井工况进行卡钻预测 ,并开发了人工神经网络实时卡钻预测系统软件。     

人工神经网络方法在基坑变形预测中的应用研究

分析研究了人工神经网络方法在基坑变形预测中的建模方法,并通过实例应用,证明这种方法是切实可行的.同时将人工神经网络方法预测结果和灰色系统模型及时序模型预测进行比较,充分证明人工神经网络方法在变形预测中的优越性.     

Kohonen人工神经网络在新疆东天山地区斑岩型钼矿资源预测中的应用

采用多源信息找矿模型,结合Kohonen人工神经网络对新疆东天山地区斑岩型钼矿进行了成矿预测。通过该地区已有斑岩型钼矿的成矿、控矿规律,确定了5类预测变量。由于东天山钼矿床已知样本较少,使用非线性的Kohonen人工神经网络法进行少模型预测。此方法不依赖预测区域的样本数量,实行非监督分类。分类结果显示:东天山地区2个典型钼矿床皆落入A类成矿有利区域,证明分类效果较为可信。实验结果表明,该方法操作简便,是一种较为快捷、有效的预测方法。     

台阶压碴爆破效果遗传神经网络预测

应用遗传神经网络模型对台阶压碴爆破效果进行了预测,增强了预测结果的可靠性。遗传神经网络是人脑的模型,从爆破实例中获取经验知识,并应用专家知识对爆破效果进行预测,取得了符合实际的预测结果,从而,为选择爆破控制参数和取得良好的爆破效果提供了依据。     

灰色系统与神经网络组合模型在地下水水位预测中的应用

灰色GM(1,1)模型与人工BP神经网络对于预测非线性数列变化趋势都具有很好的适用性,但同其他预测方法一样也存在各自的局限性。本文采用灰色GM(1,1)模型与人工神经网络相结合的方法,对GM(1,1)模型预测结果进行了修正。以收集到的某地区1996～2006年的地下水水位埋深数据为算例,计算结果表明,经人工神经网络修正后的灰色系统的预测值比原预测值的预测精度有了很大提高。     

基于人工神经网络模型的岩石特性预测

近年来,软计算技术被用作替代的统计工具。如人工神经网络（ANN）被用于开发预测模型来估计所需的参数。在本研究中,通过利用冲击钻进过程中的一些钻进参数（气压、推力、钻头直径、穿透率）和所产生的声级,建立了预测岩石性质的神经网络模型。在实验室中所产生的数据,用于开发预测岩石特性（如单轴抗压强度、耐磨性、抗拉强度和施密特回弹数）的神经网络模型,并使用各种预测性能指标对所建模型进行检验,结果表明人工神经网络模型适用于岩石性质的预测。     

典型降水预报ANN多指标优选——以太湖湖西区为例

为提高多种典型人工神经网络应用于降水预报的精度与稳定性并做出优选,对太湖流域湖西区丹徒、丹阳、金坛、溧阳、宜兴5站的年降水量时间序列建立基于组成成分分析的人工神经网络模型,并通过平均相对误差、平均绝对误差、均方根误差及合格率4项评价指标对比分析预报效果。该模型采用Mann-Kendall法、秩和检验法、谱分析法进行组成成分分析;建立BP网络、小波神经网络、RBF网络、GRNN网络及Elman网络模拟并预测随机成分,与确定性成分叠加得年降水量预报结果。在湖西区的研究结果表明,基于组成成分分析的人工神经网络模型的拟合及预测精度高于原始人工神经网络和线性自回归模型,GRNN网络的预测精度与稳定性高于其他4类神经网络。     

遗传神经网络法在巷道围岩松动圈预测中的应用

针对BP人工神经网络具有易陷入局部极小等缺陷,提出了将遗传算法与神经网络结合,同时优化网络结构的权值与阈值的思想,建立了基于遗传算法的围岩松动圈预测的神经网络模型。用该模型对巷道围岩松动圈厚度进行了预测并与BP预测结果相比较。结果表明,该遗传神经网络模型可靠,预测精度高,用来对围岩松动圈厚度进行预测是有效的和可行的。     

用神经网络模型预测济宁市地下水水位变化规律

本文通过应用人工神经网络模型中的ＢＰ网络模型,对济宁市地下水水位变化规律了预测,并与线顺归模型的计算结果进行比较,证明ＢＰ网络模型的精度较高。     

基于神经网络的混沌时间序列预测

应用混沌方法对时间序列观测数据进行处理,计算出最大lyapunov指数,得到最大可预报时间尺度。在此基础上,建立人工神经网络预测预报混沌时间序列的模型。结合实例,对该预测方法进行了计算验证。     

人工神经网络在海浪数值预报中的应用

探讨将人工神经网络技术和传统的数值模式相结合,以期得到一个更有效的海浪预报方法.以第3代海浪模式的模拟结果作为输入,浮标观测资料作为输出,采用人工神经网络进行训练,训练的初步结果显示,人工神经网络可以改进海浪数值模式的预报精度,但在波高比较大时,改进的效果并不令人满意.为此,对观测值大于1.5m时的有效波高进行再训练,从而结果有了进一步的改善.研究结果证明人工神经网络技术可以提高海浪数值预报的精度.     

Artificial Neural Network Prediction Models for Soil Compaction and Permeability

This paper presents Artificial Neural Network (ANN) prediction models which relate permeability, maximum dry density (MDD) and optimum moisture content with classification properties of the soils. The ANN prediction models were developed from the results of classification, compaction and permeability tests, and statistical analyses. The test soils were prepared from four soil components, namely, bentonite, limestone dust, sand and gravel. These four components were blended in different proportions to form 55 different mixes. The standard Proctor compaction tests were adopted, and both the falling and constant head test methods were used in the permeability tests. The permeability, MDD and optimum moisture content (OMC) data were trained with the soil’s classification properties by using an available ANN software package. Three sets of ANN prediction models are developed, one each for the MDD, OMC and permeability (PMC). A combined ANN model is also developed to predict the values of MDD, OMC, and PMC. A comparison with the test data indicates that predictions within 95% confidence interval can be obtained from the ANN models developed. Practical applications of these prediction models and the necessary precautions for using these models are discussed in detail in this paper.     

Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks

This paper presents artificial neural network (ANN) prediction models for estimating the compaction parameters of both coarse- and fine-grained soils. A total number of 200 soil mixtures were prepared and compacted at standard Proctor energy. The compaction parameters were predicted by means of ANN models using different input data sets. The ANN prediction models were developed to find out which of the index properties correlate well with compaction parameters. In this respect, the transition fine content ratio (TFR) was defined as a new input parameter in addition to traditional soil index parameters (i.e. liquid limit, plastic limit, passing No. 4 sieve and passing No. 200 sieve). Highly nonlinear nature of the compaction data dictated development of two separate ANN models for maximum dry unit weight (γ_dmax) and optimum water content (ω_opt). It was found that generalization capability and prediction accuracy of ANN models could be further enhanced by sub-clustered data division techniques.     

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.     

基于BP神经网络方法的矿井涌水量预测

鉴于矿井涌水威胁煤矿安全生产及其影响因素的复杂性,提出基于BP神经网络的矿井涌水量预测方法.在充分分析新安煤矿＋25m开采水平的涌水影响因素的基础上,选取大气降水、采空区面积和底板构造断裂和采动裂隙三个影响因子,建立了非线性人工神经网络预测模型,对＋25m开采水平的正常涌水量进行了预计.其结果和实际观测数据能够较好地相吻合,表明采用人工神经网络预计矿井涌水量是可行的.     

Machine learning techniques applied to prediction of residual strength of clay

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.     

地下水动态并行预测方法

本文首次提出了地下水动态并行预测的概念，采用人工神经网络技术使并行预测得以实现，两个算例演示结果表明，人工神经网络不但实现了并行预测而且还比传统的不确定性主人有较高的预测精度。     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.     

人工神经网络(ANN)模型在地下水资源预测中的应用研究

分析了地下水系统影响因素的复杂性，提出对于研究程度不能满足分布参数模型计算要求的研究区域，更适于从系统的观点出发、建立适宜的集中参数模型，从整体上分析研究，以解决相关问题。结合沈阳市地下水资源评价与管理实例，尝试应用人工神经网络(ANN)技术在水资源系统模型研究中的新模式。构建了基于BP算法的ANN降水量和蒸发量的预测、地下水水位动态模拟、预测及开采量优化方面的应用模型，结果表明模型精度满足要求。     

Integration of Information Technology into Geotechnical Engineering Practice: Concept and Development

Information Technology (IT) has been extensively used to predict, visualize, and analyze physical parameters in order to expedite routine geotechnical design procedures. This paper presents an example of the combined technique of IT and numerical analysis for routine geotechnical design projects. The proposed approach involves the development of ANN(s) using a calibrated finite element model(s) for use as a prediction tool and implementation of the developed ANN(s) into a GIS platform for visualization and analysis of spatial distribution of predicted results. A novel feature of the proposed approach is an ability to expedite a routine geotechnical design process that otherwise requires significant time and effort in performing numerical analyses for different design scenarios. A knowledge-based underground excavation design system that utilizes artificial neural networks (ANNs) as prediction tools is also introduced. Practical implications of the use of IT in geotechnical design are discussed in great detail.