Research on Aviation Material with Aviation Mishap Prediction Model Based on Neural Network and its BP Algorithm

2014 ◽  
Vol 540 ◽  
pp. 492-495
Author(s):  
Xu Sheng Gan ◽  
Hua Ping Li ◽  
Jing Shun Duanmu
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Prediction Model ◽  
Safety Management ◽  
Network Modeling ◽  
Bp Algorithm ◽  
Accurate Prediction ◽  
Neural Network Modeling ◽  
Rate Data ◽  
Aviation Material

In order to reduce the appearance of aviation material mishap, it is important to predict the aviation material mishap for safety management and decision-making body. Considering the advantage of neural network modeling, an aviation material mishap prediction based on neural network and its BP algorithm model is proposed. An actual example on fight mishap 10000-Hour-Rate data of USAF illustrates that the proposed prediction model has an accurate prediction.

scholarly journals Identifying the Effective Parameters in Soil Arching for Retaining Structure; A case study of Line 2 of Mashhad Subway

2020 ◽  
Vol 10 (5) ◽  
pp. 42-52
Author(s):  
Mostafa Vahedian ◽  
Masoud Cheraghi Seifabd ◽  
Alireza Baghbanan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Elastic Modulus ◽  
Internal Friction ◽  
Prediction Model ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Effective Parameters ◽  
Soil Arching ◽  
Artificial Neural Network Modeling

The effective parameters on soil arching in retaining structures composed of the steel piles (2PIE300) and the steel anchors were considered using PLAXIS 3D TUNNEL for a three-dimensional numerical model. To better compare, it was assumed that external loading conditions and technical features of structural elements were the same. To determine the limits of effective parameters in fine (CL-ML) and coarse grains (SC-SM), according to the soil specifications of the stations A2 to L2 in in Mashhad urban railway line 2 (Iran), Hardening Soil Model (HS) was used. Modeling started with a horizontal and vertical distance of 2 meters and increased to a distance of 4 meters. The parameters of the soils including angles of internal friction, cohesion, density and elastic modulus and the distance between anchors have been selected to present the prediction model. All parameters of the soils have been used for multiple regression and artificial neural network modeling statistical analysis. To present a prediction model, 5 parameters including internal friction angles of soil, cohesion, soil density, distance between anchors and elastic modulus have been selected and all of them except final parameter have been used to analyze multiple regression and artificial neural network modeling. The results showed that the best regression model that could be presented is the correlation of 94% between measured and predicted values. The prediction effectiveness of the neural network model has been found to be acceptable as they produced higher correlation coefficient (99%) between the variables and for the prediction of the factor of safety.

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