scholarly journals Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Qingdong Wu ◽  
Bo Yan ◽  
Chao Zhang ◽  
Lu Wang ◽  
Guobao Ning ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surrounding Rock ◽  
Support Vector ◽  
Tunnel Construction ◽  
Displacement Prediction ◽  
Vector Machines ◽  
Artificial Neural ◽  
Comparative Results ◽  
Artificial Neural Network Ann

Displacement prediction of tunnel surrounding rock plays an important role in safety monitoring and quality control tunnel construction. In this paper, two methodologies, support vector machines (SVM) and artificial neural network (ANN), are introduced to predict tunnel surrounding rock displacement. Then the two modes are texted with the data ofFangtianchongtunnel, respectively. The comparative results show that solutions gained by SVM seem to be more robust with a smaller standard error compared to ANN. Generally, the comparison between artificial neural network (ANN) and SVM shows that SVM has a higher accuracy prediction than ANN. Results also show that SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.

Research on the College Students’ Physique Test Based on Artificial Neural Network and Support Vector Machines Approach

2014 ◽  
Vol 945-949 ◽  
pp. 3558-3561
Author(s):  
Han Sheng Liu
Keyword(s):  
Neural Network ◽  
College Students ◽  
Artificial Neural Network ◽  
Support Vector Machines ◽  
Support Vector ◽  
Test Management ◽  
Vector Machines ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Health Test

In recent years, China’s college students’ physique presents trend of declining. In the colleges and universities health test management, it produces the problems of only paying high attention to the test and evaluation but neglects the link of feedback and improvement. Based on artificial neural network (ANN) and support vector machines (SVM) approach, this paper makes use of the principle component analysis to conduct discussion on China's college students’ physique health test management mechanism design so as to further perfect current procedure of China's college students’ physique health test work, improve the quality of management work, improve the college students’ physique and truly reach the target for the college students’ overall development.

Applying Support Vector Machines to Predict Tunnel Surrounding Rock Displacement

2010 ◽  
Vol 29-32 ◽  
pp. 1717-1721 ◽  
Author(s):  
Bao Zhen Yao ◽  
Cheng Yong Yang ◽  
Bing Yu ◽  
Fang Fang Jia ◽  
Bo Yu
Keyword(s):  
Support Vector Machines ◽  
High Accuracy ◽  
Surrounding Rock ◽  
Support Vector ◽  
Tunnel Construction ◽  
Displacement Prediction ◽  
Proposed Model ◽  
Vector Machines ◽  
Artificial Neural Network Ann ◽  
Safety Estimation

Displacement prediction of tunnel surrounding rock plays a significant role for safety estimation during tunnel construction. This paper presents an approach to use support vector machines (SVM) to predict tunnel surrounding rock displacement. A stepwise search is also introduced to optimize the parameters in SVM. The data of Fangtianchong tunnel is use to evaluate the proposed model. The comparison between artificial neural network (ANN) and SVM shows that SVM has a high-accuracy prediction than ANN. Results also show SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.

Displacement Prediction for Soil Nailing Based on ANN

2013 ◽  
Vol 353-356 ◽  
pp. 614-618
Author(s):  
Lang Gao ◽  
Zhao Wen Tang ◽  
Quan Zhong Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Nailing ◽  
Deep Excavation ◽  
Engineering Project ◽  
Deformation Prediction ◽  
Displacement Prediction ◽  
Artificial Neural ◽  
Excavation Support ◽  
Artificial Neural Network Ann

Soil nailing has become an important excavation support system for its good performance and cost-effectiveness. It is complicated to predict deformation of soil nailing during excavating. The Artificial Neural Network (ANN) is developed very quickly these years, which can be applied in diverse applications such as complex non-linear function mapping, pattern recognition, image processing and so on, and has been widely used in many fields, including geotechnical engineering. In this paper, the artificial neural network is applied for deformation prediction for soil nailing in deep excavation. The time series neural networks-based model for predicting deformation is presented and used in an engineering project. The results predicted by the model and those observed in the field are compared. It is shown that the artificial neural network-based method is effective in predicting the displacement of soil nailing during excavation.

scholarly journals Dielectric technique combined with artificial neural network and support vector regression in moisture content prediction of olive

2020 ◽  
Vol 66 (No. 1) ◽  
pp. 1-7
Author(s):  
Mahdi Rashvand ◽  
Mahmoud Soltani Firouz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Support Vector Regression ◽  
Prediction Models ◽  
Electronic Device ◽  
Support Vector ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Olives are one of the most important agriculture crops in the world, which are harvested in different stages of growth for various uses. One of the ways to detect the adequate time to process the olives is to determine their moisture content. In this study, to determine the moisture content of olives, a dielectric technique was used in seven periods of harvesting and three different varieties of olive including Oily, Mary and Fishemi. The dielectric properties of the olive fruits were measured using an electronic device in the range of 0.1–30 MHz. Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were applied to develop the prediction models by using the obtained data acquired by the system. The best results (R = 0.999 and MSE = 0.014) were obtained by the ANN model with a topology of 384–12–1 (384 features in the input vector, 12 neurons in the hidden layer and 1 output). The results obtained indicated the acceptable accuracy of the dielectric technique combined with the ANN model.

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