Prediction of residual friction angle of clays using artificial neural network

The residual strength of clay is very important to evaluate long term stability of proposed and existing slopes and for remedial measure for failure slopes. Various attempts have been made to correlate the residual friction angle (_r) with index properties of soil. This paper presents a neural network model to predict the residual friction angle based on clay fraction and Atterberg's limits. Different sensitivity analysis was made to find out the important parameters affecting the residual friction angle. Emphasis is placed on the construction of neural interpretation diagram, based on the weights of the developed neural network model, to find out direct or inverse effect of soil properties on the residual shear angle. A prediction model equation is established with the weights of the neural network as the model parameters.     

Modelling the mechanical behaviour of unsaturated soils using a genetic algorithm-based neural network

Modelling the mechanical behaviour of unsaturated soils has been the subject of many research works in the past few decades. A number of constitutive models have been developed to describe the complex behaviour of unsaturated soils. Despite the significant advances in the constitutive theories for unsaturated soils, none of the existing models can completely describe the various aspects of the real behaviour of unsaturated soils. In this paper, a new unified approach is presented, based on the integration of a neural network and a genetic algorithm, for the modelling of unsaturated soils. In the proposed approach, a genetic algorithm was used to optimise the weights of the neural network. A three-layer sequential architecture was chosen for the neural network. The network had eight input neurons, five neurons in the hidden layer and three neurons in the output layer. The eight input neurons represented the initial gravimetric water content, initial dry density, degree of saturation, net mean stress with respect to pore-air pressure, axial strain, deviatoric stress, soil suction and volumetric strain, and the three neurons in the output layer represented the deviatoric stress, suction and volumetric strain at the end of each increment. The network was trained and tested using a database that included results from a comprehensive set of triaxial tests on unsaturated soils from the literature. The predictions of the proposed model were compared with the experimental results. The comparison of the results indicates that the proposed approach was accurate and robust in representing the mechanical behaviour of unsaturated soils.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts

The use of microfine cements in permeation grouting has been growing as a strategy in geotechnical engineering because it usually provides improved groutability (N). One of the major challenges of using microfine cement grouts is the ability to estimate the N within a reasonable level of error. The suitability of traditional groutability prediction formulas, which are mostly based on the grain-size of the soil and the grout, is questionable for semi-nanometer scale grout. This study first investigated the accuracy of the current formulas; we found that the accuracy ranges from 45% to 68%, a level that is not adequate for practical engineering. An alternative approach, based on a Radial Basis Function Neural Network (RBFNN), was developed. RBFNN provides a prediction with a 95.8% accuracy within a short time frame. Several parameters were considered in our proposed network; besides the grain-size of the soil (D₁₀/D₁₅), other important parameters included the void ratio (e), the fines content (FC), the uniformity coefficient (C_u), the coefficient of gradation (C_z) and the water-to-cement ratio (w/c). A total of 240 in situ data samples were collected to support the training and testing of the network. After finding a good correlation between the field observation and the RBFNN output, it was concluded that RBFNN is a suitable and reliable tool to predict the outcome of permeation grouting when microfine cement grout is used.     

Estimation of compaction parameters of fine‐grained soils in terms of compaction energy using artificial neural networks

The determination of the compaction parameters such as optimum water content (w_opt) and maximum dry unit weight (γ_dmax) requires great efforts by applying the compaction testing procedure which is also time consuming and needs significant amount of work. Therefore, it seems more reasonable to use the indirect methods for estimating the compaction parameters. In recent years, the artificial neural network (ANN) modelling has gained an increasing interest and is also acquiring more popularity in geotechnical engineering applications. This study deals with the estimation of the compaction parameters for fine‐grained soils based on compaction energy using ANN with the feed‐forward back‐propagation algorithm. In this study, the data including the results of the consistency tests, standard and modified Proctor tests, are collected from the literature and used in the analyses. The optimum structure of a network is determined for each ANN models. The analyses showed that the ANN models give quite reliable estimations in comparison with regression methods, thus they can be used as a reliable tool for the prediction of w_opt and γ_dmax. Copyright © 2010 John Wiley & Sons, Ltd.     

用神经网络方法预测复合射孔岩层裂缝扩展深度

以神经网络为基本工具,利用其强大的非线性映射能力,并结合有限元法动力分析成果,为预测在复合射孔条件下岩层裂缝扩展深度提供了一条新的途径。结果表明,用神经网络方法能够充分体现爆燃参数与岩层裂缝扩展深度之间的非线性隐含关系,具有较高的拟合精度,其结果与有限元方法的计算值接近,简单实用。     

基于人工神经网络的砂土液化势评价

本文利用静力触探（ＣＰＴ）场地液化数据,建立了液化势判定的反向传播神经网络模型,研究表明,同传统方法相比,人工神经网络方法在判别砂土液化势方面是可行的。     

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。     

Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City,Bangladesh

Natural Hazards - Soil liquefaction resistance evaluation is an important site investigation for seismically active areas. To minimize the loss of life and property, liquefaction hazard analysis is...     

人工神经网络在岩体质量分级中的应用

结合四川省金沙江某水电站工程实例，应用BP人工神经网络方法建立3层BP网络模型，选取岩石单轴抗压强度等6个影响因素为输入变量，对坝基复杂岩体进行质量分级。通过机算机Visual C 语言编程实现神经网络模型，进行网络的学习和运算。以神经网络合理结构分析方法选取合理结构，确定合理隐层单元的数量，提高网络测试的精度。对测试结果的分析发现，经过优化的BP网络模型经多次学习后，测试精度提高，结果可靠，取得较好的实际应用效果。     

Forecasting of groundwater level in hard rock region using artificial neural network

In hardrock terrain where seasonal streams are not perennial source of freshwater, increase in ground water exploitation has already resulted here in declining ground water levels and deteriorating its’ quality. The aquifer system has shown signs of depletion and quality contamination. Thus, to secure water for the future, water resource estimation and management has urgently become the need of the hour. In order to manage groundwater resources, it is vital to have a tool to predict the aquifer response for a given stress (abstraction and recharge). Artificial neural network (ANN) has surfaced as a proven and potential methodology to forecast the groundwater levels. In this paper, Feed-Forward Network based ANN model is used as a method to predict the groundwater levels. The models are trained with the inputs collected from field and then used as prediction tool for various scenarios of stress on aquifer. Such predictions help in developing better strategies for sustainable development of groundwater resources.     

Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model

A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, E_V).

The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding.     

Prediction of the sawing quality of Marmarit stones using the capability of artificial neural network

The sawing rate is one of the most significant and effective parameters in extracting building stones via diamond wire sawing. This parameter designates the capability of diamond wire sawing for sawing different stones; in addition, the parameter gives rise to economical considerations for quarry designers. In this study, the existent relations between stone geotechnical parameters and the sawing rate of stones via diamond wire sawing were analyzed using regression and correlation coefficient as well as the collected data from Marmarit stone quarries. Moreover, we estimated the sawing rate of Marmarit using the dimensional stone rock mass rating (DSRMR); upon comparison of the data obtained from DSRMR our pre‐collected data on quarries, we did not gain satisfactory results from DSRMR, hence we used artificial neural network (ANN). The results showed that the percentage of Silica, the coefficient of water absorption, the uniaxial compressive strength (UCS), and abrasive hardness are the proper parameters for creating the ANN. Discontinuities have the least effects possible on diamond wire sawing. Having given the training possibility of the ANN, and its ability to evaluate relations among input parameters, the ANN, which was being trained with Marmarit's traits, was an accurate network for estimating diamond wire sawing in Marmarit quarries, although it could not generalize this network for other stones such as Chini and Crystal. Copyright © 2011 John Wiley & Sons, Ltd.     

Undrained lateral load capacity of piles in clay using artificial neural network

This paper describes the application of the artificial neural network model to predict the lateral load capacity of piles in clay. Three criteria were selected to compare the ANN model with the available empirical models: the best fit line for predicted lateral load capacity (Q_p) and measured lateral load capacity (Q_m), the mean and standard deviation of the ratio Q_p/Q_m and the cumulative probability for Q_p/Q_m. Different sensitivity analysis to identify the most important input parameters is discussed. A neural interpretation diagram is presented showing the effects of input parameters. A model equation is presented based on neural network parameters.     

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.     

Storm surge prediction using an artificial neural network model and cluster analysis

In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can be used to predict regional storm surges and may be used to develop a forecast system.     

应用一种改进BP神经网络算法预测密度曲线

利用测井数据与地震数据二者相结合进行综合分析,是地震勘探工作者的重要工作。可以通过分析井位处的地震数据与测井数据,提取地震的多个属性,建立一个与测井属性的统计关系。选取已改进的三层网络结构BP神经网络算法,在应用一个实际例子后表明,该算法的主要特点是收敛速度快、计算简单,同时还具有跳出局部最小的能力。应用此神经网络算法对某油田的二维地震数据进行了处理,提取了多种地震属性,并在井位置建立了地震属性与密度曲线的非线性关系,成功预测了剖面密度曲线,为了解储层状况提供了有益的资料。     

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。     

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。     

人工神经网络在矿井构造定量评价中的应用

探讨了矿井构造定量评价的人工神经网络方法, 结合东坡井田实际, 重点讨论了 BP模型的输入层、隐含层和输出层的构置和优选等问题, 并使用有序地质量最优分割方法和插值法得到学习样本, 经过学习样本的训练, 对未知单元进行评价取得了良好的效果。