Applying artificial neural network to build engineering project bid evaluation system

Author(s):  
ChuanMing zhang ◽  
Yang Wang ◽  
ZuHe wang ◽  
Lingzhi Sun
2013 ◽  
Vol 353-356 ◽  
pp. 614-618
Author(s):  
Lang Gao ◽  
Zhao Wen Tang ◽  
Quan Zhong Liu

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.


2021 ◽  
Vol 7 (5) ◽  
pp. 2012-2023
Author(s):  
Zhenjie Li

Objectives: In recent years, with the continuous improvement of the requirements of student training quality, the evaluation results of the existing evaluation system of student training quality are mostly unsatisfactory. Therefore, by integrating c-mean algorithm and Kohonen clustering algorithm, a non-sequential artificial neural network is obtained, a student training quality evaluation system based on KOHONEN neural network is designed by automatically adjusting the size of the objective function nodes of the non-sequential artificial neural network. Then the evaluation system is applied to the expected evaluation of the training quality of students in two science classes of Xinghua Middle School in Shenyang, Liaoning Province. The comparison between the test result data and the expected results of the model after the experiment confirms that the evaluation results obtained by using the evaluation system based on KOHONEN neural network have high accuracy.


2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  

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