Prediction of swelling pressures of expansive soils using soft computing methods

Lateral and vertical swelling pressures associated with expansive soils cause damages on structures. These pressures must be predicted before the structures are constructed in order to prevent the damages. The magnitude of the stresses can decrease rapidly when volume changes are partly allowed. Therefore, a material, which has a high compressibility, must be placed between expansive soils and the structures in both horizontal and vertical directions in order to decrease transmitted swelling pressure on structures. There are numerous techniques recommended for estimating the swelling pressures. However, these techniques are very complex and time-consuming. In this study, a new estimation model to predict the pressures is developed using experimental data. The data were collected in the laboratory using a newly developed device and experimental setup also. In the experimental setup, a rigid steel box was designed to measure transmitted swelling pressures in lateral and vertical directions. In the estimation model, approaches of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are employed. In the first stage of the study, the lateral and vertical swelling pressures were measured with different thicknesses of expanded polystyrene geofoam placed between one of the vertical walls of the steel box and the expansive soil in the laboratory. Then, ANN and ANFIS approaches were trained using these results of the tests measured in the laboratory as input for the prediction of transmitted lateral and vertical swelling pressures. Results obtained showed that ANN-based prediction and ANFIS approaches could satisfactorily be used to estimate the transmitted lateral and vertical swelling pressures of expansive soils.     

Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks

The application of artificial neural networks (ANNs) to solve wind engineering problems has received increasing interests in recent years. This paper is concerned with developing two ANN approaches (a backpropagation neural network [BPNN] and a fuzzy neural network [FNN]) for the prediction of mean, root-mean-square (rms) pressure coefficients and time series of wind-induced pressures on a large gymnasium roof. In this study, simultaneous pressure measurements are made on a large gymnasium roof model in a boundary layer wind tunnel and parts of the model test data are used as the training sets for developing two ANN models to recognize the input–output patterns. Comparisons of the prediction results by the two ANN approaches and those from the wind tunnel test are made to examine the performance of the two ANN models, which demonstrates that the two ANN approaches can successfully predict the pressures on the entire surfaces of the large roof on the basis of wind tunnel pressure measurements from a certain number of pressure taps. Moreover, the FNN approach is found to be superior to the BPNN approach. It is shown through this study that the developed ANN approaches can be served as an effective tool for the design and analysis of wind effects on large roof structures.     

A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

     

Modelling soil behaviour in uniaxial strain conditions by neural networks

The feed-forward neural network was used to simulate the behaviour of soil samples in uniaxial strain conditions, i.e. to predict the oedometer test results only on the basis of the basic soil properties. Artificial neural network was trained using the database of 217 samples of different cohesive soils from various locations in Slovenia. Good agreement between neural network predictions and laboratory test results was observed for the test samples. This study confirms the link between basic soil properties and stress–strain soil behaviour and demonstrates that artificial neural network successfully predicts soil stiffness in uniaxial strain conditions. The comparison between the neural network prediction and empirical formulae shows that the neural network gives more accurate as well as more general solution of the problem.     

基于贝叶斯正则化BP人工神经网络的煤与瓦斯突出预测的研究

文章介绍了BP人工神经网络和贝叶斯正则化算法的原理,探讨了贝叶斯正则化BP人工神经网络模型的建立,通过改变隐含层神经元个数的实验建立了只含1个隐含层且隐含层仅需1个神经元的煤与瓦斯突出预测模型的最佳网络结构。对该网络采用煤与瓦斯突出的预测指标进行训练、检测的结果表明,该网络预测的煤与瓦斯突出的危险程度与实际情况完全吻合;对该网络输入层输入的煤与瓦斯突出的预测指标、对输出层输出的预测结果的权值进行分析的结果表明,煤层地质构造类型对煤与瓦斯突出的影响为最大。上述研究结果对煤与瓦斯突出的预测预防研究、提高煤与瓦斯突出预测的准确性具有一定的参考价值。     

Prediction of artificial soil’s unconfined compression strength test using statistical analyses and artificial neural networks

Laboratory prediction of the unconfined compression strength (UCS) of cohesive soils is important to determine the shear strength properties. However, this study presents the application of different methods simple–multiple analysis and artificial neural networks for the prediction of the UCS from basic soil properties. Regression analysis and artificial neural networks prediction indicated that there exist acceptable correlations between soil properties and unconfined compression strength. Besides, artificial neural networks showed a higher performance than traditional statistical models for predicting UCS. Regression analysis and artificial neural network prediction indicated strong correlations (R² = 0.71–0.97) between basic soil properties and UCS. It has been shown that the correlation equations obtained by regression analyses are found to be reliable in practical situations.     

可变速智能车辆车道变换控制研究

提出了一种能够兼顾横向控制和纵向控制的可变速自动换道控制方法。首先建立了可变速的车辆运动方程,并采用基于动态目标位置概念的控制机制,以模糊逻辑为控制策略,以T-S模糊模型为控制结构,以自适应神经网络为隶属度函数的参数调整手段,设计出一种兼顾智能车辆横向控制和纵向控制的运动控制器。仿真结果表明,提出的控制方法是可行的、有效的,并且较为理想地模拟实际交通环境中车辆换道的行为特性。     

ANN层状介质结构识别方法的鲁棒性研究

通过对理论信号的实测信号的分析,研究了人工神经网络对层状介质结构识别的 鲁棒性,分析了层状介质物理参数的变化对神经网络识别效果的影响.实验结果表明.各介 质参数在一定范围内变化时,所得神经网络具有较强的鲁棒性.该研究结果反映出利用神经 网络进行层状介质结构识别具有较强的实用价值.     

Estimation of ultimate loads of eccentric-inclined loaded strip footings rested on sandy soils

Apart from the vertical axial loads, the footings of portal-framed buildings are often subjected to eccentric and eccentric-inclined loads caused by forces of earth pressures, earthquakes, water, wind, etc. Sometimes the corner of the column of these portal-framed buildings is located very close to the property line and hence subjected to eccentric and eccentric-inclined loading. The study presented herein describes the use of artificial neural networks (ANNs) and multi-linear regression model for the prediction of ultimate loads of eccentric and eccentric-inclined loaded strip footings. The data used in running of network models have been obtained from extensive series of laboratory model tests. The parameters investigated are the eccentricity ratio, the load inclination angle, the footing size, and the density of sand soil. A total of 50 laboratory model tests with four different footings rested on loose and dense sand have been performed. The eccentricity ratio (e/B) and inclination angle (i) changed from 0 to 0.5 and 0° to 30° with vertical direction, respectively. The measured ultimate load response of the strip footings has been compared with available theoretical prediction. The results of the experimental study proved that the soil density, the load eccentricity, and the load inclination had considerable effects on the load of the strip footings, and the ANN model serves as simple and reliable tool for predicting the behavior of eccentric and eccentric-inclined loaded strip footings.     

基于小波神经网络的结晶器漏钢预报技术研究

介绍了预报粘结性漏钢的基本方法,并对结晶器热电偶测得的大量温度数据进行预处理,再利用小波神经网络技术对经过预处理的检测数据进行训练,优化神经网络系统的结构和参数,识别出具有漏钢征兆的波形,提高了预报系统的精度和快速性;给出了用MATLAB实现的网络训练和测试的仿真结果,同时用VC开发了能识别结晶器内单偶、横向、纵向漏钢征兆温度波形的仿真系统。     

基于进化神经网络的短期电网负荷预测算法

本文提出了一种基于进化神经网络的短期电网负荷预测算法。该算法使用了改进的人工蜂群算法与BP神经网络融合生成的进化神经网络,并且使用改进的人工蜂群算法对进化神经网络的偏置和权重进行优化。该算法将火电历史负荷数据作为输入,使用进化神经网络训练预测模型,预测未来一段时间内的电网负荷。首先获取历史负荷数据,然后将收集到的数据应用于进化神经网络模型训练。人工蜂群算法作为一种全局搜索算法,可以有效地探索模型参数空间,寻找最佳的模型参数组合以提升预测精度。为了验证所提出的负荷预测方法的有效性,本文使用了火电网负荷数据进行测试。实验结果表明,在短期电网负荷预测方面,本文提出的进化神经网络比传统方法预测结果更加准确可靠。     

Artificial neural network for prediction of air flow in a single rock joint

In this paper, an attempt has been made to evaluate and predict the air flow rate in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure, and air outlet pressure using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network having 3-7-1 architecture network was trained using 37 data sets measured from laboratory investigation. Ten new data sets were used for the, validation and comparison of the air flow rate by ANN and multi-variate regression analysis (MVRA) to develop more confidence on the proposed method. Results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between measured and predicted values of air flow rate. It was found that CoD between measured and predicted air flow rate was 0.995 and 0.758 by ANN and MVRA, respectively, whereas MAE was 0.0413 and 0.1876.     

Optimization of weight and cost of cantilever retaining wall by a hybrid metaheuristic algorithm

Engineering with Computers - A retaining wall is a structure used to resist the lateral pressure of soil or any backfill material. Cantilever retaining walls provide resistance to overturning and...     

人工神经网络结合正交变换方法研究

本文提出一和中运用人工神经网络结合正交换变换的方法，即通过正交变换滤除噪声，通过交叉验证确定网络最佳构型，以充分发挥正交变换和神经网络各自的长处，即免了同过拟合，实现更准确的预后。     

粉体密相气力输送中的管道压降预测

利用人工神经网络技术,建立了BP网络模型,通过网络的学习训练,比较准确地预测了粉体密相气力输送过程中的管道压降,预测准确率在93.3％以上,表明该方法可以作为密相气力输送研究中的一种有效的辅助手段。     

Use of artificial neural networks for estimation of agricultural wheel traction force in soil bin

This paper presents an application of artificial neural networks (ANNs) for the prediction of traction force using readily available datasets experimentally obtained from a soil bin utilizing single-wheel tester. Aiming this, firstly the tests were carried out using two soil textures and two tire types as affected by velocity, slippage, tire inflation pressure, and wheel load. On this basis, the potential of neural modeling was assessed with multilayered perceptron networks using various training algorithms among which, backpropagation algorithm was compared to backpropagation with declining learning rate factor algorithm due to their primarily yielded superior performance. The results divulged that the latter one could better achieve the aim of study in terms of performance criteria. Furthermore, it was inferred that ANNs could reliably provide a promising tool for prediction of traction force and its modeling.     

BP与RBF比较研究

由于人工神经网络在符号处理、并行搜索、自组织联想记忆等方面有独特的优势,因此成为人工智能研究的热点。目前,人工神经网络模型形式多样,为了能够清晰地了解人工神经网络,就两种比较流行的神经网络:BP与RBF进行了介绍,研究了这两种人工神经网络的结构算法,并且对它们的结构算法以及性能进行了比较。     

基于Logistic回归、ANN、SVM的乳腺癌复发影响因素研究

为找出乳腺癌复发的影响因素,并比较人工神经网络（ANN）型、支持向量机型（SVM）和logistic回归型在乳腺癌复发中的预测效能.本文结合南斯拉夫卢布尔雅那大学医疗中心乳腺癌肿瘤研究所的277例数据,对乳腺癌复发的影响因素进行研究.分别采用了logistic回归、人工神经网络和支持向量机方法来建立乳腺癌复发的预测模型,并对这三种分析方法进行了理论方法和预测效能的比较.结果发现,肿瘤大小、有无结节冒、肿瘤恶性程度（P<0.05）是乳腺癌术后复发的主要影响因素,而在不同的预测方法中相对于logistic回归模型,支持向量机和人工神经网络具有更好的预测效能,其中支持向量机的预测效能最好.     

Development of a predictive system for car fuel consumption using an artificial neural network

A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction.