scholarly journals Experimental Test and Prediction Model of Soil Thermal Conductivity in Permafrost Regions

2020 ◽  
Vol 10 (7) ◽  
pp. 2476 ◽  
Author(s):  
Fu-Qing Cui ◽  
Zhi-Yun Liu ◽  
Jian-Bing Chen ◽  
Yuan-Hong Dong ◽  
Long Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Prediction Model ◽  
Rbf Neural Network ◽  
Soil Thermal Conductivity ◽  
Engineering Applications ◽  
Frozen Soils ◽  
Fitting Method ◽  
Network Method ◽  
Permafrost Regions

Soil thermal conductivity is a dominant parameter of an unsteady heat-transfer process, which further influences the stability and sustainability of engineering applications in permafrost regions. In this work, a laboratory test for massive specimens is performed to reveal the distribution characteristics and the parameter-influencing mechanisms of soil thermal conductivity along the Qinghai–Tibet Engineering Corridor (QTEC). Based on the measurement data of 638 unfrozen and 860 frozen soil specimens, binary fitting, radial basis function (RBF) neural network and ternary fitting (for frozen soils) prediction models of soil thermal conductivity have been developed and compared. The results demonstrate that, (1) particle size and intrinsic heat-conducting capacity of the soil skeleton have a significant influence on the soil thermal conductivity, and the typical specimens in the QTEC can be classified as three clusters according to their thermal conductivity probability distribution and water-holding capacity; (2) dry density as well as water content sometimes does not have a strong positive correlation with thermal conductivity of natural soil samples, especially for multiple soil types and complex compositions; (3) both the RBF neural network method and ternary fitting method have favorable prediction accuracy and a wide application range. The maximum determination coefficient (R2) and quantitative proportion of relative error within ±10% ( P ± 10 % ) of each prediction model reaches up to 0.82, 0.88, 81.4% and 74.5%, respectively. Furthermore, because the ternary fitting method can only be used for frozen soils, the RBF neural network method is considered the optimal approach among all three prediction methods. This study can contribute to the construction and maintenance of engineering applications in permafrost regions.

Prediction on China's Energy Demand Based on RBF Neural Network Model

2013 ◽  
Vol 805-806 ◽  
pp. 1421-1424
Author(s):  
Xue Feng ◽  
Wuyunbilige Bao ◽  
Ben Ha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Neural Network Model ◽  
Energy Demand ◽  
Rbf Neural Network ◽  
Bp Network ◽  
Factors Affecting ◽  
Rbf Network ◽  
Network Prediction ◽  
Main Factors

Choose factors which influence the energy demand by the method of path analysis, build radial basis function (RBF) neural network model to predict energy demand in China. The RBF neural network is trained with the actual data of the main factors affecting energy demand during 1989-2003 and energy demand during 1993-2007 as learning sample with a good fitting effect. After testing network with the actual data of the main factors affecting energy demand during 2004-2007 and energy demand during 2008-2011, higher prediction accuracy can be obtained. By comparison with the BP network, RBF network prediction model outperforms BP network prediction model, finally RBF network is applied to make prediction of energy consumption for the year 2013-2015.

The Research Based on RBF Neural Network in the Power of Prediction of Grain Depot

2012 ◽  
Vol 182-183 ◽  
pp. 1358-1361
Author(s):  
Le Xiao ◽  
Min Peng Hu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Electricity Consumption ◽  
Nonlinear Time Series ◽  
Modeling And Prediction ◽  
Radial Basis

According to the fact that the use of electricity in grain depot is nonlinear time series, the article introduces the prediction model of electricity based on Radial Basis Function Neural Network, and conducts the modeling and prediction by adopting the historical electricity consumption of a typical grain depot. As the result of simulation shows, the model obtains better forecasting results in grain depot electricity.

Thermal Errors Simulation and Modeling of Motorized Spindle

2010 ◽  
Vol 154-155 ◽  
pp. 1305-1309 ◽  
Author(s):  
Liang Yu Cui ◽  
Da Wei Zhang ◽  
Wei Guo Gao ◽  
Xiang Yang Qi ◽  
Yu Shen
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Simulation Analysis ◽  
Rbf Neural Network ◽  
Thermal Characteristics ◽  
Cnc Machine ◽  
Motorized Spindle ◽  
Neural Network Method ◽  
Thermal Errors ◽  
Network Method

Thermal errors of motorized spindle are of great importance to affect final machining precision of CNC machine tool. Thermal characteristics simulation analysis of motorized spindle is realized by ANSYS; thermal errors test measurement is completed based on 5-point method; and prediction models of thermal errors are constructed by multiple linear regression (MLR) method, Back Propagation (BP) neural network method and Radial Basis Function (RBF) neural network method respectively. The results of simulation and experiments illustrate that simulation results can represent thermal characteristics of motorized spindle, whose degree of confidence mainly depending on setting of thermal load and boundary conditions properly or not; RBF neural network model has highest prediction precision for thermal errors of motorized spindle based on test data.

Research on Predictive Model of Gas Concentration for Driving Ventilation in Coal Mine Based on RBF and BP

2013 ◽  
Vol 706-708 ◽  
pp. 1805-1809
Author(s):  
Xiao Yan Gong ◽  
Jun Guo ◽  
He Xue ◽  
Dong Hui Yan ◽  
Zhe Wu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Rbf Neural Network ◽  
Coal Mines ◽  
Observation Data ◽  
Prediction Errors ◽  
Application Research ◽  
Gas Concentration ◽  
Mine Sites

In order to predict accurately gas concentration and design ventilation scheme in driving ventilation process under different gas emission in coal mine, based on the analysis of various ventilation factors, the prediction model structure of gas concentration for driving ventilation was designed based on RBF and BP neural network in this paper. Then MATLAB software and the observation data obtained from the coal mine sites were used to compare and analyze the prediction errors of two models, and a RBF neural network model with higher prediction precision was obtained. After that, the prediction model was used for practical application research on the gas concentration of the heading face in concrete coal mines. The research shows that the settled prediction model can not only predict the gas concentration precisely of driving ventilation, but also provide a certain theory basis for different driving ventilation equipment layout and parameters configuration in the driving ventilation process of coal mines.

The Analysis of Combined Prediction Model of Building Energy Consumption with Grey Theory and RBF Neural Network

2011 ◽  
Vol 374-377 ◽  
pp. 90-93
Author(s):  
Yan Bai ◽  
Qing Chang Ren ◽  
Hong Mei Jiang
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Rbf Neural Network ◽  
Grey Theory ◽  
The Novel ◽  
Grey Prediction ◽  
Consumption Data ◽  
Building Area ◽  
Simulation And Experiment

A kind of new combined modeling method with GM(1,1) and RBNN (Radial Basis Neural Network) is brought forward, according to the idea that the method of neural network can bring grey prediction model a good modified effect. Based on the analysis of the energy consumption data of the existing and the annually-increased building area, the GM(1,1) model was then constructed. And the RBF neural network was used for the model residual error revising. The simulation and experiment results show that the novel model is more effective than the common grey model.

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