# [1]裴浩东,叶社保,杨 平,等.软土地层盾构掘进参数分析及掘进速度预测[J].郑州大学学报(工学版),2024,45(01):107-113.[doi:10.13705/j.issn.1671-6833.2023.04.003] 　PEI Haodong,YE Shebao,YANG Ping,et al.Analysis of Boring Parameters of Shield in Soft Soil Strata and Prediction of Driving Speed[J].Journal of Zhengzhou University (Engineering Science),2024,45(01):107-113.[doi:10.13705/j.issn.1671-6833.2023.04.003] 点击复制 软土地层盾构掘进参数分析及掘进速度预测() 分享到： var jiathis_config = { data_track_clickback: true };

45

2024年01期

107-113

2024-01-19

## 文章信息/Info

Title:
Analysis of Boring Parameters of Shield in Soft Soil Strata and Prediction of Driving Speed

1. 南京林业大学 土木工程学院,江苏 南京 210037;2. 中交隧道工程局有限公司,江苏 南京 210000
Author(s):
1. School of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China;
2. CCCC Tunnel Engineering Co., Ltd., Nanjing 210000, China

Keywords:
DOI:
10.13705/j.issn.1671-6833.2023.04.003

A

Abstract:
Taking the tunnel project from Fengsha Station to Creative Park Station of Foshan Metro Line 3 as background, the inherent variation tendency of boring parameters of shield when the EPB shield tunnel crossed the soft soil strata was analyzed in detail through the on-site measured data, and the different prediction models of driving speed were built. Firstly, the shield tunneling parameters were analyzed by mathematical statistics, and the distribution of each tunneling parameter was tested. Secondly, the Pearson correlation analysis was performed to find out the variation law between the parameters with strong linear correlation. Then using the feature selection algorithm based on mutual information, the parameter variables with high nonlinear correlation with the driving speed were screened. Finally, the random forest regression prediction and the BP neural network prediction model based on genetic algorithm optimization were established respectively to predict the driving speed. The research results showed that in shield tunnel projects in soft formations, lower cutterhead speed, cutterhead torque, higher tunneling speed, penetration, total shield thrust and soil silo pressure were usually used. The parameters such as the excavation speed passed the normality test using the K-S test method. There was a strong correlation between the speed of excavation and the degree of penetration. The average absolute error, root mean square error and goodness of fit of the random forest regression prediction model in the test set were 4. 055, 5. 038 and 0. 871, respectively, while the optimization of the BP neural network prediction model based on genetic algorithm was 0. 822, 1. 244 and 0. 991, respectively

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