| Issue |
E3S Web Conf.
Volume 276, 2021
2021 5th International Conference on Water Conservancy, Hydropower and Building Engineering (WCHBE 2021)
|
|
|---|---|---|
| Article Number | 01014 | |
| Number of page(s) | 4 | |
| Section | Water Conservancy and Hydropower and Natural Geological Exploration | |
| DOI | https://doi.org/10.1051/e3sconf/202127601014 | |
| Published online | 23 June 2021 | |
Research on Prediction of Ground Settlement of Deep Foundation Pit Based on Improved PSO-BP Neural Network
1 School of Civil Engineering, Chongqing Jiaotong University, 400074 Chongqing, China
2 School of Civil Engineering, Chongqing Jiaotong University, 400074 Chongqing, China
*a Corresponding author: 1457251985@qq.com
b 794263718@qq.com
In view of the limitations of the existing prediction methods for ground subsidence of deep foundation pit, a BP neural network prediction model based on improved particle swarm optimization algorithm was proposed. The mutation and crossover of genetic algorithm are integrated into particle swarm optimization algorithm, which makes full use of the global characteristics of genetic algorithm and the fast convergence speed of particle swarm optimization algorithm. In order to reduce the network output error, improve the convergence speed and enhance the network generalization ability, the final value of the optimized particle iteration was selected as the connection weight and threshold of the BP neural network. The results show that the RMSE, MAPE and R2 of the improved PSO-BP model are 0.3077, 0.7506% and 0.8811, so the improved PSO-BP model has a better prediction accuracy.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.