Prediction of swelling pressures of expansive soils using artificial neural networks

Swelling behavior of expansive soil is a complicated phenomenon. In order to cope with the complications in describing the swelling behavior of expansive soil, researchers developed alternative approaches. In this paper, the prediction model of transmitted lateral swelling pressure, and vertical swelling pressures on a retaining structure was developed using artificial neural network (ANN) approach. In the first stage of this study, the lateral and vertical swelling pressures were measured with different thicknesses of expanded polystyrene (EPS) geofoam placed between one of the vertical walls of the steel testing box and the expansive soil. Then, artificial neural network was trained using these pressures for prediction transmitted lateral swelling pressure, and vertical swelling pressures on a retaining structure. Results obtained from this study showed that neural network-based prediction models could satisfactorily be used in obtaining the swelling pressures of the expansive soils.     

Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set

Traditional methods on creating diesel engine models include the analytical methods like multi-zone models and the intelligent based models like artificial neural network (ANN) based models. However, those analytical models require excessive assumptions while those ANN models have many drawbacks such as the tendency to overfitting and the difficulties to determine the optimal network structure. In this paper, several emerging advanced machine learning techniques, including least squares support vector machine (LS-SVM), relevance vector machine (RVM), basic extreme learning machine (ELM) and kernel based ELM, are newly applied to the modelling of diesel engine performance. Experiments were carried out to collect sample data for model training and verification. Limited by the experiment conditions, only 24 sample data sets were acquired, resulting in data scarcity. Six-fold cross-validation is therefore adopted to address this issue. Some of the sample data are also found to suffer from the problem of data exponentiality, where the engine performance output grows up exponentially along the engine speed and engine torque. This seriously deteriorates the prediction accuracy. Thus, logarithmic transformation of dependent variables is utilized to pre-process the data. Besides, a hybrid of leave-one-out cross-validation and Bayesian inference is, for the first time, proposed for the selection of hyperparameters of kernel based ELM. A comparison among the advanced machine learning techniques, along with two traditional types of ANN models, namely back propagation neural network (BPNN) and radial basis function neural network (RBFNN), is conducted. The model evaluation is made based on the time complexity, space complexity, and prediction accuracy. The evaluation results show that kernel based ELM with the logarithmic transformation and hybrid inference is far better than basic ELM, LS-SVM, RVM, BPNN and RBFNN, in terms of prediction accuracy and training time.     

Chlorophyll-a Predicting Model Based on Dynamic Neural Network

Algal blooms are one of the most prevalent global problems. Studying the Chlorophyll-a (Chl-a) predicting model helps to control algal blooms. Predicting the behavior of algae is difficult because of the complex physical, chemical, and biological processes involved. Artificial neural network (ANN) models have been determined to be useful and efficient, especially for such problems for which the characteristics of the processes are difficult to describe using numerical models. An indoor simulated environment is designed for algal cultivation to analyze the temporal change in the algae biomass of Taihu Lake during summer. A Chl-a prediction model based on a nonlinear autoregressive neural network with exogenous inputs (NARX) that can detect and consider within the time dependency is proposed. The NARX model is compared to a static neural network and a dynamic neural network: feedforward neural network (FNN) and Elman recurrent neural network (ERNN). The performance of the proposed NARX model was examined with experimental data collected over 3 months in 2010. The results showed that the NARX model outperformed the other ANN models and significantly enhance the accuracy of Chl-a prediction.     

Physics-informed few-shot learning for wind pressure prediction of low-rise buildings

This research proposes a physics-informed few-shot learning model to predict the wind pressures on full-scale specimens based on scaled wind tunnel experiments. Existing machine learning approaches in the wind engineering domain are incapable of accurately extrapolating the prediction from scaled data to full-scale data. The model presented in this research, on the other hand, is capable of extrapolating prediction from large-scale or small-scale models to full-scale measurements. The proposed ML model combines a few-shot learning model with the existing physical knowledges in the design standards related to the zonal information. This physical information helps in clustering the few-shot learning model and improves prediction performance. Using the proposed techniques, the scaling issue observed in wind tunnel tests can be partially resolved. A low mean-squared error, mean absolute error, and a high coefficient of determination were observed when predicting the mean and standard deviation wind pressure coefficients of the full-scale dataset. In addition, the benefit of incorporating physical knowledge is verified by comparing the results with a baseline few-shot learning model. This method is the first of its type as it is the first time to extrapolate in wind performance prediction by combining prior physical knowledge with a few-shot learning model in the field of wind engineering. With the benefit of the few-shot learning model, only a low-resolution of the measuring tap configuration is required, and the reliance on physical wind tunnel experiments can be reduced. The physics-informed few-shot learning model is an efficient, robust, and accurate alternate solution to predicting wind pressures on full-scale structures based on various modeled scale experiments.     

计及光伏及风电并网的电力系统短期负荷预测

为提升光伏、风电等分布式能源大量接入电网后短期电力负荷的预测精度,促进电网消纳能力提升,本文对光伏出力及短期用电负荷采用小波——径向基函数(RBF)神经网络预测方法;对风力发电首先利用总体平均经验模态分解(EEMD)方法对其功率数据分解,再采用BP神经网络、RBF神经网络、小波神经网络、ELMAN神经网络四种神经网络预测方法进行预测,并用粒子群算法(PSO)和灰色关联度(GRA)修正。最后,利用等效负荷的概念,分析光伏、风力发电并网对于短期电力负荷预测的影响,并将三种模型有效结合,得到了考虑光伏及风力发电并网的电力系统短期负荷预测的等效负荷预测模型。实例分析表明,本文所提方法相较于其他方法在该预测项目上具有相对更高的预测精度。     

Predicting piezometric water level in dams via artificial neural networks

The safety control of dams is based on measurements of parameters of interest such as seepage flows, seepage water clarity, piezometric levels, water levels, pressures, deformations or movements, temperature variations, loading conditions, etc. Interpretation of these large sets of available data is very important for dam health monitoring and it is based on mathematical models. Modelling seepage through geological formations located near the dam site or dam bodies is a challenging task in dam engineering. The objective of this study is to develop a feedforward neural network (FNN) model to predict the piezometric water level in dams. An improved resilient propagation algorithm has been used to train the FNN. The measured data have been compared with the results of FNN models and multiple linear regression (MLR) models that have been widely used in analysis of the structural dam behaviour. The FNN and MLR models have been developed and tested using experimental data collected during 9 years. The results of this study show that FNN models can be a powerful and important tool which can be used to assess dams.     

Flank wear prediction in drilling using back propagation neural network and radial basis function network

In the present work, two different types of artificial neural network (ANN) architectures viz. back propagation neural network (BPNN) and radial basis function network (RBFN) have been used in an attempt to predict flank wear in drills. Flank wear in drill depends upon speed, feed rate, drill diameter and hence these parameters along with other derived parameters such as thrust force, torque and vibration have been used to predict flank wear using ANN. Effect of using increasing number of sensors in the efficacy of predicting drill wear by using ANN has been studied. It has been observed that inclusion of vibration signal along with thrust force and torque leads to better prediction of drill wear. The results obtained from the two different ANN architectures have been compared and some useful conclusions have been made.     

基于支持向量机的丙酮精制建模

采用支持向量机建立了丙酮精制过程的产品质量与生产工艺参数之间的预测模型,并将其与反向传播神经网络和径向基神经网络模型相比较。在实际工业数据上进行的实验结果表明,支持向量机模型对丙酮纯度具有良好的预测效果,性能优于反向传播神经网络和径向基网络模型。     

Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China)

Infectious diarrhea is an important public health problem around the world. Meteorological factors have been strongly linked to the incidence of infectious diarrhea. Therefore, accurately forecast the number of infectious diarrhea under the effect of meteorological factors is critical to control efforts. In recent decades, development of artificial neural network (ANN) models, as predictors for infectious diseases, have created a great change in infectious disease predictions. In this paper, a three layered feed-forward back-propagation ANN (BPNN) model trained by Levenberg–Marquardt algorithm was developed to predict the weekly number of infectious diarrhea by using meteorological factors as input variable. The meteorological factors were chosen based on the strongly relativity with infectious diarrhea. Also, as a comparison study, the support vector regression (SVR), random forests regression (RFR) and multivariate linear regression (MLR) also were applied as prediction models using the same dataset in addition to BPNN model. The 5-fold cross validation technique was used to avoid the problem of overfitting in models training period. Further, since one of the drawbacks of ANN models is the interpretation of the final model in terms of the relative importance of input variables, a sensitivity analysis is performed to determine the parametric influence on the model outputs. The simulation results obtained from the BPNN confirms the feasibility of this model in terms of applicability and shows better agreement with the actual data, compared to those from the SVR, RFR and MLR models. The BPNN model, described in this paper, is an efficient quantitative tool to evaluate and predict the infectious diarrhea using meteorological factors.     

Customer churn prediction by hybrid neural networks

As churn management is a major task for companies to retain valuable customers, the ability to predict customer churn is necessary. In literature, neural networks have shown their applicability to churn prediction. On the other hand, hybrid data mining techniques by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers two hybrid models by combining two different neural network techniques for churn prediction, which are back-propagation artificial neural networks (ANN) and self-organizing maps (SOM). The hybrid models are ANN combined with ANN (ANN + ANN) and SOM combined with ANN (SOM + ANN). In particular, the first technique of the two hybrid models performs the data reduction task by filtering out unrepresentative training data. Then, the outputs as representative data are used to create the prediction model based on the second technique. To evaluate the performance of these models, three different kinds of testing sets are considered. They are the general testing set and two fuzzy testing sets based on the filtered out data by the first technique of the two hybrid models, i.e. ANN and SOM, respectively. The experimental results show that the two hybrid models outperform the single neural network baseline model in terms of prediction accuracy and Types I and II errors over the three kinds of testing sets. In addition, the ANN + ANN hybrid model significantly performs better than the SOM + ANN hybrid model and the ANN baseline model.     

Knowledge-based systems using neural networks for electron beam welding process of reactive material (Zircaloy-4)

Bead-on-plate welding of zircaloy-4 (a reactive material) plates was conducted using electron beam according to central composite design of experiments. Its predictive models were developed in the form of knowledge-based systems in both forward and reverse directions using neural networks. Input parameters considered for this welding of reactive metals were accelerating voltage, beam current and weld speed. The responses of the welding process were measured in terms of bead width, depth of penetration and micro-hardness. Forward mapping of the welding process was conducted using regression analysis, back-propagation neural network (BPNN), genetic algorithm-tuned neural network (GANN) and particle swarm optimization algorithm-tuned neural network (PSONN). Reverse mapping of this process was also carried out using the BPNN, GANN and PSONN-based approaches. Neural network-based approaches could model this welding process of reactive material in both forward and reverse directions efficiently, which is required for the automation of the same. The performance of the neural network models was found to be data-dependent. The BPNN could outperform the other two approaches for most of the cases but not all in both the forward and reverse mappings.     

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.     

ANN构造性设计中基于GA优选神经元激活函数类型

构造性设计是ANN设计的发展方向之一。全面的高质量的ANN学习应包括神经元激活函数类型的自动优化。该文在构造性设计的框架内讨论了如何实现典型前馈网络的包括神经元激活函数类型在内的全面学习。首先,提出了典型前馈网络的一种构造性设计方法的原理和算法框架,把整个网络的设计分解成了一个个单个神经元的设计问题;然后提出了基于GA的能实现激活函数类型优选的单个神经元的设计方法。大量函数拟合的仿真实验显示:与其它几种激活函数类型不优选的常见ANN设计方法相比,该文提出的方法更有效,能用较小的网络结构获得较好的泛化性能。     

Application of two non-linear prediction tools to the estimation of tunnel boring machine performance

Predicting tunnel boring machine (TBM) performance is a crucial issue for the accomplishment of a mechanical tunnel project, excavating via full face tunneling machine. Many models and equations have previously been introduced to estimate TBM performance based on properties of both rock and machine employing various statistical analysis techniques. However, considering the nature of the problem, it is relatively difficult to estimate tunnel boring machine performance by linear prediction models. Artificial neural networks (ANNs) and non-linear multiple regression models have great potential for establishing such prediction models. The purpose of the present study is the construction of non-linear multivariable prediction models to estimate TBM performance as a function of rock properties. For this purpose, rock properties and machine data were collected from recently completed TBM tunnel project in the City of New York, USA and consequently the database was established to develop performance prediction models utilizing the ANN and the non-linear multiple regression methods. This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations.     

Inverse estimation of wind from the waves measured by high-frequency radar

In this work, an attempt was made to derive wind speeds from the wave parameters recorded by a high-frequency (HF) radar by resorting to the techniques of an artificial neural network (ANN) and a model tree (MT) and by considering it as an inverse-modelling problem. The time series of significant wave height, average wave period, wave direction and wind direction collected over the years 2007 and 2008 by the Bodega Marine Laboratory (BML) at the Bodega Bay, California, were used along with the corresponding wind speeds measured by a floating buoy in the vicinity. The ANN and MT models were trained and tested using alternative data splits to assess their performance over varying sample sizes. Both these methods worked very well in this application, with the ANN showing better flexibility in model fitting. This study thus indicates that data-driven methods can be effectively used to derive unobserved wind speed values in HF radar measurements.     

Fuzzy neural network-based rescheduling decision mechanism for semiconductor manufacturing

Most semiconductor manufacturing systems (SMS) operate in a highly dynamic and unpredictable environment. The production rescheduling strategy addresses uncertainty and improves SMS performance. The rescheduling framework of SMS is presented as layered scheduling strategies with an optimization rescheduling decision mechanism. A fuzzy neural network (FNN) based rescheduling decision model is implemented which can rapidly choose an optimized rescheduling strategy to schedule the semiconductor wafer fabrication lines according to current system disturbances. The mapping between the input of FNN, such as disturbances, system state parameters, and the output of FNN, optimal rescheduling strategies, is constructed. An example of a semiconductor fabrication line in Shanghai is given. The experimental results demonstrate the effectiveness of proposed FNN-based rescheduling decision mechanism approach over the alternatives such as back-propagation neural network (BPNN) and multivariate regression (MR).     

Evolutionary product unit neural networks for short-term wind speed forecasting in wind farms

Combinations of physical and statistical wind speed forecasting models are frequently used in wind speed prediction problems arising in wind farms management. Artificial neural networks can be used in these models as a final step to obtain accurate wind speed predictions. The aim of this work is to determine the potential of evolutionary product unit neural networks (EPUNNs) for improving the accuracy and interpretation of these systems. Traditional neural network and EPUNN approaches have been used to develop different wind speed prediction models. The results obtained using different EPUNN models show that the functional model and the hybrid algorithms proposed provide very accurate prediction compared with standard neural networks used to solve this regression problem. One of the main advantages of the application of these EPUNNs has been the possibility of obtaining some interpretation of the non-linear relation predicted by the model, as will be shown in real data of a wind farm in Spain.     

Determinants of house prices in Turkey: Hedonic regression versus artificial neural network

Determinants of house prices in Turkey are examined in this paper using the 2004 Household Budget Survey Data. In property valuation and housing market research, the locational value is usually analyzed by hedonic methods that use multiple regression techniques on large data sets and require a formality based on microeconomic theory in the analyses. Because of potential non-linearity in the hedonic functions, artificial neural network (ANN) is employed in this study as an alternative method. By comparing the prediction performance between the hedonic regression and artificial neural network models, this study demonstrates that ANN can be a better alternative for prediction of the house prices in Turkey.