基于PSO-RF模型的复杂地层双模盾构土压掘进模式下密封舱压力预测

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摘要依托广州地铁7号线2期工程洪圣沙-裕丰围区间隧道工程，采用粒子群算法优化随机森林方法（PSO-RF）建立双模盾构土压掘进模式下密封舱压力预测模型。通过对盾构掘进参数进行相关性分析，筛选出对密封舱压力影响较大的掘进参数，包括螺机转速、螺机扭矩、刀盘转速、推进速度、贯入度、刀盘扭矩、总推力，将筛选出的掘进参数作为预测模型的输入，密封舱压力作为模型的输出，对密封舱压力进行预测。结果表明：采用粒子群算法优化的随机森林PSO-RF预测模型能够有效预测双模盾构密封舱压力；相比于传统神经网络预测模型，PSO-RF模型预测精度更高，平均绝对误差均在10%以内，预测值和实际值的拟合优度R2为0.9014，在预测精度及模型的泛化能力上明显优于BP神经网络。

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	张斌
	佟彬
	刘国强
	周子豪
	王树英

关键词: 双模盾构密封舱压力随机森林粒子群优化算法

Abstract: Relying on the Hongshengsha-Yufengwei Section Tunnel project of the second phase of Guangzhou Metro Line 7, this paper used the particle swarm optimization random forest method (PSO-RF) to establish a sealed cabin pressure prediction model in the dual-mode shield soil pressure boring mode. Through the correlation analysis of shield boring parameters, the tunneling parameters that had a great influence on the pressure of the sealing chamber were screened out, including screw speed, screw torque, cutterhead speed, propulsion speed, penetration degree, cutterhead torque, and total thrust, and the screened tunneling parameters were used as the input parameters of the prediction model, and the pressure of the sealing chamber was used as the output parameter of the model to predict the pressure of the sealing chamber. The results showed that the random forest PSO-RF prediction model improved by particle swarm algorithm could effectively predict the pressure of the dual-mode shield sealed cabin, and compared with the traditional neural network prediction model, the PSO-RF model had higher prediction accuracy, the average absolute error was within 10%, and the predicted value and the actual goodness-of-fit R2 were 0.9014, which was significantly superior to the BP neural network in terms of prediction accuracy and generalization ability of the model.

Key words: dual-mode shield sealed cabin pressure random forest particle swarm optimization algorithm

收稿日期: 2022-11-24 修回日期: 2023-02-17 发布日期: 2023-02-17

中图分类号:

U455

通讯作者: 周子豪(1997—)，男，江苏睢宁人，硕士研究生，主要研究方向为盾构隧道工程。 E-mail: zihao_zhou25@163.com

作者简介: 张斌(1985—)，男，四川南充人，学士，工程师，主要研究方向盾构施工技术管理。E-mail: binnyzh_85@163.com

引用本文:

张斌, 佟彬, 刘国强, 周子豪, 王树英. 基于PSO-RF模型的复杂地层双模盾构土压掘进模式下密封舱压力预测[J]. 隧道与地下工程灾害防治, .
ZHANG Bin, TONG Bin, LIU Guoqiang, ZHOU Zihao, WANG Shuying. Prediction of sealed cabin pressure in the earth pressure excavation mode of dual-mode shield tunneling mode in complex formations based on PSO-RF model. Hazard Control in Tunnelling and Underground Engineering, 0, (): 1-10.

链接本文:

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