Modeling of soil penetration resistance using multiple linear regression (MLR)

In agricultural areas, the use of machinery leads to improved yields. Nevertheless, its inadequate implementation and excessive utilization can seriously affect the soil efficiency. In fact, latter can be generated by increasing the penetration resistance and subsequently, it results in the compaction phenomenon. This problem becomes considerable with the increasing report wheel/soil. The aim of this work was to evaluate the efficiency through the prediction of soil penetration resistance (Rp) using a statistical model based on moisture content, density, tractor weight, number of passes, and the wheel inflation pressure. Experimental works (211 measurements) were analyzed and the penetration resistance was modeled using multiple linear regressions (MLR). Besides, the developed model elucidates the variables affecting the accentuation of soil Rp and allows the investigation of equations for novel sampled soils. Our results showed that the parameters related to soil and tractors were significant to explain Rp. The adopted model in the MLR analysis emphasizes that the mechanical parameters of ground measurements are statistically significant in estimating and evaluating Rp. The statistical calculation of the R ² expresses 83% of the variance in Rp generated by the various parameters related to soil and tractor. In view of the importance of estimating the penetration resistance (Rp), the regression equation shows that the weight of the tractor and the number of passages contributed the most to the proposed model for the soil.     

Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)

Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model’s validity.     

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.     

Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN)

In this study, the preprocessing of the gamma test was used to select the appropriate input combination into two models including the support vector regression (SVR) model and artificial neural networks (ANNs) to predict the stream flow drought index (SDI) of different timescales (i.e., 3, 6, 9, 12, and 24 months) in Latian watershed, Iran, which is one of the most important sources of water for the large metropolitan Tehran. The variables used included SDI _t , SDI _t ? 1, SDI _t ? 2, SDI _t ? 3, and SDI _t ? 4 monthly delays. Two variables including SDI _t and SDI _t ? 1 with lower gamma values were identified as the most optimal combination of variables in all drought timescales. The results showed that the gamma test was able to correctly identify the right combination for the forecasting of 6, 9, and 12 months SDI using the ANN model. Also, the gamma test was considered in selecting the appropriate inputs for identifying the values of 9, 12, and 24 months SDI in SVR. The support vector machine approach showed a better efficiency in the forecast of long-term droughts compared to the artificial neural network. In total, among forecasts made for 30 scenarios, the support vector machine model only in scenario 3 of SDI₃, scenario 1 of SDI₆, and scenarios 2 and 3 of SDI₂₄ represented poorer efficiency compared to the artificial neural network (MLP layer), but in other scenarios, the results of SVR had better efficiency.     

岩土边坡稳定性预报的人工神经网络方法

阐述了经典边坡稳定分析方法的局限性,综合考虑了影响边坡稳定性的因素,建立基于人工神经网络的边坡稳定性预报方法。采用遗传算法优化神经网络的结构,以提高其非线性映射能力和泛化能力,从而,提高预报准确度。基于已有的工程实例训练所建立的神经网络,并对新的边坡稳定性问题进行了预报,预报结果表明,所建立的边坡稳定性预报方法具有较高的预报准确度。     

Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques

Compound broad-crested weir is a typical hydraulic structure that provides flow control and measurements at different flow depths. Compound broad-crested weir mainly consists of two sections; first, relatively small inner rectangular section for measuring low flows, and a wide rectangular section at higher flow depths. In this paper, series of laboratory experiments was performed to investigate the potential effects of length of crest in flow direction, and step height of broad-crested weir of rectangular compound cross-section on the discharge coefficient. For this purpose, 15 different physical models of broad-crested weirs with rectangular compound cross-sections were tested for a wide range of discharge values. The results of examination for computing discharge coefficient were yielded by using multiple regression equations based on the dimensional analysis. Then, the results obtained were also compared with genetic programming (GP) and artificial neural network (ANN) techniques to investigate the applicability, ability, and accuracy of these procedures. Comparison of results from the GP and ANN procedures clearly indicates that the ANN technique is less efficient in comparison with the GP algorithm, for the determination of discharge coefficient. To examine the accuracy of the results yielded from the GP and ANN procedures, two performance indicators (determination coefficient (R ²) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of GP technique sound satisfactory regarding the performance indicators (R ²?=?0.952 and RMSE?=?0.065) with less deviation from the numerical values.     

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.     

Using artificial neural network (ANN) in prediction of collapse settlements of sandy gravels

Collapse settlement is one of the main geotechnical hazards, which should be controlled during first impoundment stage in embankment dams. Imposing large deformations and significant damages to dams makes it an important phenomenon, which should be checked during design phases. Also, existence of a variety of contributing parameters in this phenomenon makes it difficult and complicated to well predict the potential of collapse settlement. Thus, artificial neural networks, which are commonly applied by majority of geotechnical engineers in predicting various perplexing problems, can be efficiently used to calculate the value of collapse settlement. In this paper, feedforward backpropagation neural networks are considered. And three-layered FFBPNNs with the architectures of 4–6–2 and 4–9–2 accurately predicted the coefficient of stress release and collapse settlement value, respectively. These networks were trained using 180 datasets gained from large-scale direct shear test, which were carried out on gravel materials. High correlation between measured and predicted values for both collapse settlement and coefficient of stress release can be easily understood from the coefficient of determination and root mean square error. It is shown that sand content and normal stress applied to the specimens, respectively, are most effective parameters on the collapse settlement value and coefficient of stress release.     

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.     

基于交替迭代算法神经网络评价岩石边坡稳定性

目前边坡工程中常用的稳定性分析方法主要分为极限平衡法和数值分析法2大类，文章对它们各自的主要愿理、特点及其优缺点等进行了阐述。首先，根据经典边坡稳定分析方法存在的局限性，提出有必要建立基于人工神经网络的边坡稳定性预报方法。其次，针对经典算法BP网络存在的某些缺陷，提出了一种交替迭代算法神经网络，以提高其非线性映射能力和泛化能力。交替迭代神经网络算法通过解2个阶数比较低的线性代数方程组，逐步求得连接权值的。以此提高收敛速度，且有利于寻求最优解。作者用FORTRAN语言编制了程序。分析了建立边坡岩体稳定性预测网络模型的建立中应该注意的几个方面。最后，基于已有的40个岩石边坡工程实例进行所建立的神经网络的训练和边坡稳定的预报，结果表明文中所建立的边坡稳定性预报方法具有较高的预报准确度。     

Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone

Present paper endeavors to develop predictive artificial neural network model for forecasting the mean monthly total ozone concentration over Arosa, Switzerland. Single hidden layer neural network models with variable number of nodes have been developed and their performances have been evaluated using the method of least squares and error estimation. Their performances have been compared with multiple linear regression model. Ultimately, single-hidden-layer model with 8 hidden nodes have been identified as the best predictive model.     

Prediction and controlling of flyrock in blasting operation using artificial neural network

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.     

Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis

Accurate laboratory measurement of geo-engineering properties of intact rock including uniaxial compressive strength (UCS) and modulus of elasticity (E) involves high costs and a substantial amount of time. For this reason, it is of great necessity to develop some relationships and models for estimating these parameters in rock engineering. The present study was conducted to forecast UCS and E in the sedimentary rocks using artificial neural networks (ANNs) and multivariable regression analysis (MLR). For this purpose, a total of 196 rock samples from four rock types (i.e., sandstone, conglomerate, limestone, and marl) were cored and subjected to comprehensive laboratory tests. To develop the predictive models, physical properties of studied rocks such as P wave velocity (V_p), dry density (γ_d), porosity, and water absorption (A_b) were considered as model inputs, while UCS and E were the output parameters. We evaluated the performance of MLR and ANN models by calculating correlation coefficient (R), mean absolute error (MAE), and root-mean-square error (RMSE) indices. The comparison of the obtained results revealed that ANN outperforms MLR when predicting the UCS and E.     

Performance of artificial neural network and convolutional neural network on slope failure prediction using data from the random finite element method

Acta Geotechnica - The random finite element method has been widely used to evaluate slope uncertainty and reliability. To determine the probability of failure, the safety factor sampling often...     

人工神经网络在边坡滑移预测中的应用

利用一项实际边坡工程的多年实测滑移资料，基于人工神经网络方法，建立了非线性人工神经网络分析模型，从而能够较精确地对该边坡的滑移进行预测，通过本工程实际的应用实践，发现它可避开复杂的本构模型而成为解决岩土工程问题的新途径。     

基于SOFM神经网络的边坡稳定性评价

针对边坡工程稳定性分析中参数的不确定性,在分析自组织特征映射神经网络（SOFM）基本学习算法的基础上,从提高算法收敛速度和性能出发,将自组织特征映射神经网络基本学习算法加以改进,据此建立了评价边坡稳定状态的SOFM神经网络模型。然后用收集到的边坡稳定工程实例作为样本,对该模型进行训练和检验,并与BP神经网络判别结果对比。结果表明,SOFM神经网络性能良好、预测精度高,是边坡稳定性评价的一种有效方法。     

An enhanced approach to predict permeability in reservoir sandstones using artificial neural networks (ANN)

A key challenge in the oil and gas industry is the ability to predict key petrophysical properties such as porosity and permeability. The predictability of such properties is often complicated by the complex nature of geologic materials. This study is aimed at developing models that can estimate permeability in different reservoir sandstone facies types. This has been achieved by integrating geological characterization, regression models and artificial neural network models with porosity as the input data and permeability as the output. The models have been developed, validated and tested using samples from three wells and their predictive accuracy tested by using them to predict the permeability in a fourth well which was excluded from the model development. The results indicate that developing the models on a facies basis provides a better predictive capability and simpler models compared to developing a single model for all the facies combined. The model for the combined facies predicted permeability with a correlation coefficient of 0.41 which is significantly lower than the correlation coefficient of 0.97, 0.93, 0.99, 0.96, 0.96 and 0.85 for the massive coarse-grained sandstones, massive fine-grained sandstones-moderately sorted, massive fine-grained sandstones-poorly sorted, massive very fine-grained sandstones, parallel-laminated sandstones and bioturbated sandstones, respectively. The models proposed in this paper can predict permeability at up to 99% accuracy. The lower correlation coefficient of the bioturbated sandstone facies compared to other facies is attributed to the complex and variable nature of bioturbation activities which controls the petrophysical properties of highly bioturbated rocks.     

用神经网络评价边坡稳定性

影响边坡稳定性因素是复杂且具有随机和模糊特性。神经网络的性能特征使适用于解决非性的边坡稳定性评价问题，本文建立了边坡稳定性评价的复合网络模型，并利用边坡工程的失稳及稳定实例对网络进行了训练和测试，计算分析表明，网络模型对于评价边坡的稳定性有较好的适用性。     

基于广义回归神经网络的边坡稳定性评价

边坡失稳是比较常见的地质灾害,判定其稳定性的方法很多,在使用过程中也暴露出了这些方法的缺陷。针对这些问题,构建了适合于边坡稳定性评价的广义回归神经网络模型,并运用Matlab的神经网络工具箱进行了分析和计算,使用了相关数据来训练和测试该模型的可靠性和可行性。结果表明,广义回归神经网络模型在使用过程中需选择合适的光滑因子,而所得出的数据与实际结果较为相符,解决了之前使用的BP神经网络模型的缺点,具有很好的工程运用前景。