Application of BP Neural Network Embankment Settlement Prediction in Seasonal Frozen Areas

2013 ◽  
Author(s):  
Jia-feng Chen ◽  
Hai-bin Wei ◽  
Bao-ping An ◽  
Zhang Peng ◽  
Yang-peng Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Settlement Prediction

Settlement Prediction of Buildings Based on BP Neural Network

2014 ◽  
Vol 602-605 ◽  
pp. 3232-3234
Author(s):  
Jian Cheng Li
Keyword(s):  
Neural Network ◽  
Seismic Performance ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Existing Buildings ◽  
Large Area ◽  
Settlement Prediction ◽  
Prediction Result ◽  
Study Results

At present, the traditional neural network model have been used in settlement prediction of buildings area, but there are some limitations. In this paper, BP neural network is applied in the settlement prediction of buildings and the prediction result is compared with the measured values. The results show that: the use of BP neural network to predict the settlement of existing buildings is feasible. The study results can provide a reference for the anti-seismic performance census of existing large area buildings.

scholarly journals Forecast of Highway Subgrade Settlement Based on Improved BP Neural Network

2020 ◽  
pp. 118
Author(s):  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Average Error ◽  
Good Prediction ◽  
Settlement Prediction ◽  
Training Samples ◽  
Good Prediction Accuracy ◽  
Neural Network Prediction ◽  
Network Prediction

In order to improve the feasibility and accuracy of the roadbed settlement prediction model, the factor analysis method is combined with the BP neural network method, and an improved BP neural network roadbed settlement prediction model is proposed. Select example data to test the improved BP neural network roadbed settlement prediction model. The test results: The relative average error of the 10 sets of training samples’ predicted and actual roadbed settlements was 4.287%, and the roads of five predicted samples The relative error of subgrade settlement is 1.79%, 1.93%, 6.62%, 7.19%, 4.05%, all less than 10%, which proves that the improved BP neural network prediction model has good prediction accuracy.

The Application of BP Neural Network in Settlement Prediction of Highway Soft Foundation

2011 ◽  
Vol 250-253 ◽  
pp. 3440-3443
Author(s):  
Yi Xue ◽  
Zheng Zheng Cao ◽  
Shan Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Linear Mapping ◽  
Point Method ◽  
Learning Ability ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Bp Neural Network Model

In view of the settlement of highway soft foundation, the paper proposes a method to predict soft foundation settlement based on BP neural network model, taking advantage of the strong non-linear mapping and learning ability of BP neural network. Then it is compared with the three-point method, obtaining some useful conclusions. Since the BP neural network model is directly based on real samples, it could avoid the mistakes due to factitiousness in the three-point method. It is proved that the BP neural network model is accurate and the settlement has least error.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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