Research Article of Scientific Research and Reviews
Forecast of Highway Subgrade Settlement Based on Improved BP Neural Network
China Academy of Safety Science and Technology, Beijing, China
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.
Keywords: Roadbed settlement; Factor analysis method; BP neural network; Simulation prediction
How to cite this article:
Shengxiang Ma. Forecast of Highway Subgrade Settlement Based on Improved BP Neural Network. Scientific Research and Reviews, 2020; 13:118. DOI:10.28933/srr-2020-10-0105
1. MA Yunfeng. Application of Difference Method in Subgrade Settlement Prediction [J]. Highway Engineering, 2012, 37(5): 180-182.
2. Hu Rongguang. Multivariate nonlinear regression analysis of the time series law of roadbed settlement based on the layered sum method [J]. Journal of Railway Engineering Society, 2009, 26(3): 7-10.
3. PAN Linyou, XIE Xinyu. Using curve fitting method to predict the settlement of soft soil foundation [J]. Rock and Soil Mechanics, 2004, 25(7): 1053-1058.
4. LI Hongfeng, SHAN Wei, WANG Lihai. The application of empirical formula method in the settlement prediction of soft soil foundation in seasonally frozen area [J]. Journal of Northeast For-estry University, 2007, 35(12): 45-47.
5. DENG Chengfa, YANG Fenggen. Non-linear finite element analysis of soft soil roadbed settlement [J]. National Defense Traffic Engineering and Technology, 2007, 5(1): 23-25.
6. SU Xiaocheng, ZHOU Tianlai, LIU Zhifeng. Re-search on the seismic stability of highway subgrade structures using pseudo-static method [J]. Chinese Journal of Earthquake Engineering, 2014, 36(3): 482-488.
7. YU Huan, ZHENG Junjie, CAO Wenzhao, etc. Analysis of dynamic characteristics of widened embankment under asymmetric traffic load [J]. Chinese Journal of Earthquake Engineering, 2017, 39(6): 1112-1117.
8. ZHU Hongqing, CHANG Wenjie, Zhang Bin. Prediction model of BP neural network for gas emission in mining face and its application [J]. Journal of Coal, 2007,32 (5): 504-508.
9. LIANG Shengkai, Cao Qiong, LUO Yangyang. Neural network prediction of gas outburst in coal mine [J]. Chinese Journal of Solid Mechanics, (S1): 180-183.