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.     

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.     

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.     

粗粒土压实特性及颗粒破碎分形特征试验研究

对多个级配不同含水率的粗粒土进行击实试验,研究粗粒土的压实特性和颗粒破碎分形特征。结果表明,粗粒土最大干密度随级配中粗粒含量的增大而增大,当粗粒含量P5=70%时,最大干密度出现最大值;当P5＞70%时,最大干密度又随粗粒含量的增大而减小,粗粒土击实破碎后的粒径分布具有良好的分形特性,破碎分形维数为2.279 0～2.892 2,均大于击实前粗粒土粒度分形维数;相同级配条件下,粗粒土破碎分形维数随含水率的增大而增大,且粗粒含量P5＞50%时,增幅显著;粗粒土破碎分形维数D与破碎率Bg存在良好的线性关系,且击实前后粗粒土粒度分形维数差值能客观表征颗粒破碎的程度;粗粒含量和含水率是影响颗粒破碎率的两个重要因素,但相对于含水率而言,粗粒含量对破碎率的影响更加显著。     

Predicting pipeline corrosion in heterogeneous soils using numerical modelling and artificial neural networks

Acta Geotechnica - The influence of soil heterogeneity on the corrosion of underground metallic pipelines and the resulting evolution of localised corrosion patches were examined. A field-validated...     

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.     

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.     

Evaluation of water quality parameters for the Mamasin dam in Aksaray City in the central Anatolian part of Turkey by means of artificial neural networks

Sustaining the human ecological benefits of surface water requires carefully planned strategies for reducing the cumulative risks posed by diverse human activities. The municipality of Aksaray city plays a key role in developing solutions to surface water management and protection in the central Anatolian part of Turkey. The responsibility to provide drinking water and sewage works, regulate the use of private land and protect public health provides the mandate and authority to take action. The present approach discusses the main sources of contamination and the result of direct wastewater discharges into the Melendiz and Karasu rivers, which recharge the Mamasın dam sites by the use of artificial neural network (ANN) modeling techniques. The present study illustrates the ability to predict and/or approve the output values of previously measured water quality parameters of the recharge and discharge areas at the Mamasin dam site by means of ANN techniques. Using the ANN model is appreciated in such environmental research. Here, the ANN is used for estimating if the field parameters are agreeable to the results of this model or not. The present study simulates a situation in the past by means of ANN. But in case any field measurements of some relative parameters at the outlet point “discharge area” have been missed, it could be possible to predict the approximate output values from the detailed periodical water quality parameters. Because of the high variance and the inherent non-linear relationship of the water quality parameters in time series, it is difficult to produce a reliable model with conventional modeling approaches. In this paper, the ANN modeling technique is used to establish a model for evaluating the change in electrical conductivity (EC) and dissolved oxygen (DO) values in recharge (input) and discharge (output) areas of the dam water under pollution risks. A general ANN modeling scheme is also recommended for the water parameters. The modeling process includes four main stages: (1) source data analysis, (2) system priming, (3) system fine-tuning and (4) model evaluation. Results of the ANN modeling scheme indicate that the output values are agreeable to the water quality parameters, which were measured at the field in the static water mass of the Mamasın dam lake. Water contamination at the dam site is caused by the continuous increase of nutrient contents and decrease of the O₂ level in water causing an anaerobic condition. It may stimulate algae growth flow in such water bodies, consequently reducing water quality.     

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.     

软土物理力学性质指标与微结构参数的灰色关联-神经网络模型

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。     

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。     

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。     

高铁路基粗颗粒土水力学参数测试方法研究

高铁路基粗颗粒土的水力学特性对路基内部水分运移及路基的长期累积变形有重要影响。而对这种高压实度的粗颗粒土,常规非饱和土试验仪器存在试样尺寸小、制样难度大的缺点,难以应用。介绍了一种用于测试高压实度路基粗颗粒土-水力学参数的试验装置,利用张力计和时域反射计量器,分别测量路基粗颗粒土在浸湿、干燥阶段不同高度处土体基质吸力、介电常数的变化情况,获得高压实度下高铁路基粗颗粒土土-水特征曲线;并通过瞬态剖面法获得路基粗颗粒土非饱和渗透系数与基质吸力的关系。试验结果表明,该套装置能够适用于最大粒径为20 mm的粗颗粒土,试样压实度最大可达到0.95。通过对一组试验结果分析,并结合Ekblad等试验结果,发现随着填料细颗粒含量增大,?（与进气值有关的参数）值逐渐减小,土体进气值增加;粒径越大,细颗粒含量越低,土体储水能力越低,对应n（与排水程度有关的参数）值越大。为路基粗颗粒土-水力学参数的测定提供了方法。     

Caspian Sea level prediction using satellite altimetry by artificial neural networks

The demand for accurate predictions of sea level fluctuations in coastal management and ship navigation activities is increasing. To meet such demand, accessible high-quality data and proper modeling process are critically required. This study focuses on developing and validating a neural methodology applicable to the short-term forecast of the Caspian Sea level. The input and output data sets used contain two time series obtained from Topex/Poseidon and Jason-1 satellite altimetry missions from 1993 to 2008. The forecast is performed by multilayer perceptron network, radial basis function, and generalized regression neural networks. Several tests of different artificial neural network (ANN) architectures and learning algorithms are carried out as alternative methods to the conventional models to assess their applicability for estimating Caspian Sea level anomalies. The results derived from the ANN are compared with observed sea level values and with the forecasts calculated by a routine autoregressive moving average (ARMA) model. Different ANNs satisfactorily provide reliable results for the short-term prediction of Caspian Sea level anomalies. The root mean square errors of the differences between observations and predictions from artificial intelligence approaches can be significantly reduced by about 50 % compared with ARMA techniques.     

Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt

Ras Fanar field is one of the largest oil-bearing carbonate reservoirs in the Gulf of Suez. The field produces from the Middle Miocene Nullipore carbonate reservoir, which consists mainly of algal-rich dolomite and dolomitic limestone rocks, and range in thickness between 400 and 980 ft. All porosity types within the Nullipore rocks have been modified by diagenetic processes such as dolomitization, leaching, and cementation; hence, the difficulty arise in the accurate determination of certain petrophysical parameters, such as porosity and permeability, using logging data only. In this study, artificial neural networks (ANN) are used to estimate and predict the most important petrophysical parameters of Nullipore reservoir based on well logging data and available core plug analyses. The different petrophysical parameters are first calculated from conventional logging and measured core analyses. It is found that pore spaces are uniform all over the reservoirs (17–23%), while hydrocarbon content constitutes more than 55% and represented mainly by oil with little saturations of secondary gasses. A regular regression analysis is carried out over the calculated and measured parameters, especially porosity and permeability. Fair to good correlation (R <65%) is recognized between both types of datasets. A predictive ANN module is applied using a simple forward backpropagation technique using the information gathered from the conventional and measured analyses. The predicted petrophysical parameters are found to be much more accurate if compared with the parameters calculated from conventional logging analyses. The statistics of the predicted parameters relative to the measured data, show lower sum error (<0.17%) and higher correlation coefficient (R >80%) indicating that good matching and correlation is achieved between the measured and predicted parameters. This well-learned artificial neural network can be further applied as a predictive module in other wells in Ras Fanar field where core data are unavailable.     

人工神经网络在桩基工程中的应用综述

对人工神经网络在桩基工程中的应用研究工作进行了回顾与评述。总结了神经网络在单桩承载力、荷载－位移关系预测以及基桩动测完整性判释等方面的技术成果与水平，并分析和探讨了进一步的研究方向和应用前景。     

Estimating leaf chlorophyll contents by combining multiple spectral indices with an artificial neural network

Estimating leaf chlorophyll contents through leaf reflectance spectra is efficient and nondestructive, but the actual dataset always based on a single or a few kinds of specific species, has a limitation and instability for a common use. To address this problem, a combination of multiple spectral indices and a model simulated dataset are proposed in this paper. Six spectral indices are selected, including Blue Green Index (BGI), Photochemical Reflectance Index (PRI_5), Triangle Vegetation Index (TVI), Chlorophyll Absorption Ratio Index (CARI), Carotenoid Reflectance Index (CRI) and the green peak reflectance (R₅₂₅). Both stepwise linear regression (SLR) and back-propagation artificial neural network (ANN) are used to combine the six spectral indices for the estimation of chlorophyll content (Cab). In addition, to overcome the limitation of actual dataset, a “big data” is applied by a within-leaf radiation transfer model (PROSPECT) to generate a large number of simulated samples with varying biochemical and biophysical parameters. 30% of the simulated dataset (SIM30) and an experimental dataset are used for validation. Compared with linear regression method, NN yields better result with R² = 0.96 and RMSE = 5.80ug.cm^?2 for Cab if validated by SIM30, while R² = 0.95 and RMSE = 6.39ug.cm^?2 for SLR. NN also gives satisfactory result with R² = 0.80 and RMSE = 5.93ug.cm^?2 for Cab if validated by LOPEX dataset, however, the SLR only gets 0.72 of R² and 12.20ug.cm^?2 of RMSE. The results indicate that integrating multiple spectral indices can improve the Cab estimating accuracy with a better stability in different kind of species and the model simulated dataset can make up the shortfall of actual measured dataset.     

Predicting effective stress parameter of unsaturated soils using neural networks

In the effective stress equation for unsaturated soils proposed by Bishop, shear strength in these soils depends on the effective stress parameter, χ, a function of soil suction [1]. To estimate the shear strength of unsaturated soils, one must already know this parameter and its variation with soil suction. Though theories on the shear strength of unsaturated soils are consistent with experimental measurements, estimating the effective stress parameter directly from tedious laboratory tests is impractical. Thus, researchers have performed numerous intensive studies to effectively obtain the unsaturated shear strength using simplified empirical methods.This paper shows an adaptive learning neural network method for predicting this parameter, χ. The proposed network is a multilayer perceptron network with six neurons in the input layer representing the air entry value, the volumetric water content at residual and saturated conditions, the slope of soil water characteristic curve, the net confining stress and suction. The available literature uses a database prepared from triaxial shear test results to train and test the network. The results show the suitability of the proposed approach for estimating the effective stress parameter. Network analysis indicates that the χ-parameter depends strongly on the net mean stress.