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On-line Access: 2024-01-15

Received: 2023-01-07

Revision Accepted: 2023-08-07

Crosschecked: 2024-01-15

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 ORCID:

Xiaowei YE

https://orcid.org/0000-0003-0012-5842

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Journal of Zhejiang University SCIENCE A 2024 Vol.25 No.1 P.1-17

http://doi.org/10.1631/jzus.A2300011


Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm


Author(s):  Xiaowei YE, Xiaolong ZHANG, Yanbo CHEN, Yujun WEI, Yang DING

Affiliation(s):  MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   chenyanbo@zju.edu.cn

Key Words:  Random forest (RF), Particle swarm optimization (PSO), Upward displacement of lining, Machine learning prediction, Shield tunneling construction


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Xiaowei YE, Xiaolong ZHANG, Yanbo CHEN, Yujun WEI, Yang DING. Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm[J]. Journal of Zhejiang University Science A, 2024, 25(1): 1-17.

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year="2024",
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doi="10.1631/jzus.A2300011"
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%T Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm
%A Xiaowei YE
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%A Yanbo CHEN
%A Yujun WEI
%A Yang DING
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A1 - Xiaowei YE
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A1 - Yang DING
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DOI - 10.1631/jzus.A2300011


Abstract: 
During construction, the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield projects. Differential floating will increase the initial stress on the segments and bolts which is harmful to the service performance of the tunnel. In this study we used a random forest (RF) algorithm combined particle swarm optimization (PSO) and 5-fold cross-validation (5-fold CV) to predict the maximum upward displacement of tunnel linings induced by shield tunnel excavation. The mechanism and factors causing upward movement of the tunnel lining are comprehensively summarized. Twelve input variables were selected according to results from analysis of influencing factors. The prediction performance of two models, PSO-RF and RF (default) were compared. The Gini value was obtained to represent the relative importance of the influencing factors to the upward displacement of linings. The PSO-RF model successfully predicted the maximum upward displacement of the tunnel linings with a low error (mean absolute error (MAE)=4.04 mm, root mean square error (RMSE)=5.67 mm) and high correlation (R2=0.915). The thrust and depth of the tunnel were the most important factors in the prediction model influencing the upward displacement of the tunnel linings.

基于粒子群优化-随机森林(PSO-RF)算法的盾构隧道施工期管片最大上浮量预测

作者:叶肖伟1,2,张小龙2,陈延博1,2,魏瑜均2,丁杨3
机构:1浙江大学,软弱土与环境土工教育部重点实验室,中国杭州,310027;2浙江大学,建筑工程学院,中国杭州,310027;3浙大城市学院,城市基础设施智能化浙江省工程研究中心,中国杭州,310015
目的:盾构隧道施工阶段面临管片上浮问题,不均匀上浮将引起管片破损、开裂、渗漏水等病害,严重影响隧道施工质量与长期服役性能。本文系统地总结了管片上浮机理与影响因素,提出粒子群优化-随机森林(PSO-RF)混合算法,建立管片最大上浮量预测模型,可实现盾构隧道管片上浮量的准确预测,用以指导盾构隧道安全施工。
创新点:1.提出混合的PSO-RF算法,预测管片最大上浮量;2.总结盾构隧道施工期管片上浮机理与影响因素。
方法:1.基于管片上浮机理,筛选出12个管片上浮影响因素;2.提出PSO-RF混合优化算法,提升预测模型的预测性能;3.基于管片上浮数据库,建立管片上浮预测模型,并对比PSO-RF模型与RF模型的预测性能。
结论:1.提出的PSO-RF混合模型明显提升了管片最大上浮量预测模型的预测性能;2. PSO-RF管片上浮预测模型成功预测了管片的最大上浮量,有更小的预测误差(MAE=4.04mm,RMSE=5.67mm)与更高的相关性(R2=0.915);3.盾构机千斤顶推力与隧道埋深是影响管片最大上浮量预测模型性能的主要因素。

关键词:随机森林;粒子群优化;管片上浮;机器学习预测;盾构隧道施工

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