基于ABC-BP神经网络的地铁盾构隧道地层识别及复合比预测

郭勇; 郭小霖; 简永洲; 张箭; 丰土根; 陈子昂

doi:10.3973/j.issn.2096-4498.2024.03.006

隧道建设 >

2024 , Vol. 44 >Issue 3: 484 - 495

DOI: https://doi.org/10.3973/j.issn.2096-4498.2024.03.006

研究与探索

基于ABC-BP神经网络的地铁盾构隧道地层识别及复合比预测

郭勇 ,
郭小霖 ,
简永洲 ,
张箭 ,
丰土根 ,
陈子昂

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(1. 上海华铁工程咨询有限公司，上海200040; 2. 河海大学岩土力学与堤坝工程教育部重点实验室，江苏南京210098; 3. 中交二公局第四工程有限公司，河南洛阳471013)

郭勇（1976—），男，河南浚县人，2015年毕业于长沙理工大学，交通土建专业，本科，高级工程师，主要从事隧道工程施工与管理工作。Email: 76061637@qq.com。*通信作者：张箭， Email: zhangj0507@163.com。

网络出版日期: 2024-04-28

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Identification and Prediction of Composite Ratios for Strata in Metro Shield Tunnel Using Artificial Bee ColonyBack Propagation Neural Network

GUO Yong ,
GUO Xiaolin ,
JIAN Yongzhou ,
ZHANG Jian ,
FENG Tugen ,
CHEN Zi′ang

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(1. Shanghai Huatie Engineering Consulting Co., Ltd., Shanghai 200040, China; 2. Key Laboratory of Geomechanics and Embankment Engineering, the Ministry of Education, Hohai University, Nanjing 210098, Jiangsu, China; 3. CCCCSHB Fourth Engineering Co., Ltd., Luoyang 471013, Henan, China)

Online published: 2024-04-28

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摘要

为研究盾构掘进过程中掘进参数与地层情况的关联性，建立盾构掘进过程中的机-岩关系，依托南京地铁6号线某盾构施工区间数据进行复合地层下掘进参数的统计分析。首先，利用掘进参数与地层的相关性，采用人工蜂群算法优化的BP神经网络，建立可根据掘进参数识别开挖面地层并描述复合地层组合情况的ABC-BP神经网络模型；然后，针对盾构区间进行地层识别和区间内2种复合地层的复合比预测。结果表明： 1）盾构掘进参数的波动范围与均值随开挖面所处地层变化,且依地层不同呈现一定规律性； 2）地层类别预测结果表明，模型对上软下硬地层、中风化泥质砂岩、粉质黏土的识别召回率分别为94.1%、96.6%、96%，总体识别准确率为95%； 3）针对复合比的预测结果表明，相较于其他机器学习模型，ABC-BP模型的平均绝对误差、均方根误差均减小且样本回归值提升，在预测精度和预测稳定性方面具有一定的优越性。

关键词： 地铁盾构隧道; 地层识别; 复合地层; 掘进参数; 神经网络; 复合比; 机器学习; ABC算法

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郭勇 , 郭小霖 , 简永洲 , 张箭 , 丰土根 , 陈子昂 . 基于ABC-BP神经网络的地铁盾构隧道地层识别及复合比预测[J]. 隧道建设, 2024 , 44(3) : 484 -495 . DOI: 10.3973/j.issn.2096-4498.2024.03.006

Abstract

A machinerock relationship model is developed to investigate the correlation between tunneling parameters and stratum conditions during shield tunneling. Statistical analysis of tunneling parameters in Nanjing metro line 6, operating under composite strata, is conducted. Initially, a back propagation(BP) neural network model is established and optimized using the artificial bee colony(ABC) algorithm, thereby creating an ABCBP neural network model capable of identifying the tunnel face strata and describing the composite ratio of these strata using tunneling parameters. Subsequently, strata identification and composite ratios prediction for two composite strata within the tunneling section are conducted. The following findings emerge: (1) The shield tunneling parameters′ fluctuation range and average values exhibit discernible patterns that vary with the strata at the tunnel face. (2) The model achieves high identification accuracies of upper soft and lower hard strata, moderatelyweathered argillaceous sandstone, and silty clay, with recall rate reaching 94.1%, 96.6%, and 96%, respectively, resulting in an overall accuracy of 95%. (3) Compared with similar machine learning models, the ABCBP model demonstrates superior performance in average absolute error, root mean square error, and sample regression values, indicating high prediction accuracy and stability.

Key words： metro shield tunnel; stratum identification; composite strata; tunneling parameters; neural network; composite ratio; machine learning; artificial bee colony algorithm

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