装配式地铁车站大尺寸构件光纤监测与裂纹识别研究

洪成雨; 黎宏; 饶伟; 陈湘生; 雷振

doi:10.3973/j.issn.2096-4498.2025.03.005

隧道建设 >

2025 , Vol. 45 >Issue 3: 499 - 510

DOI: https://doi.org/10.3973/j.issn.2096-4498.2025.03.005

研究与探索

装配式地铁车站大尺寸构件光纤监测与裂纹识别研究

洪成雨 ,
黎宏 ,
饶伟 ,
陈湘生 ,
雷振

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（1. 深圳大学土木与交通工程学院，广东深圳 518060； 2. 极端环境岩土和隧道工程智能建养全国重点实验室，广东深圳 518060； 3. 中国电建集团南方投资有限公司，广东深圳 518052）

洪成雨（1982—），男，黑龙江哈尔滨人，2011年毕业于香港理工大学，岩土工程专业，博士，副教授，现从事岩土监测、地下装配式结构相关研究工作。E-mail： cyhong@szu.edu.cn。*通信作者: 黎宏, E-mail: 2210474067@email.szu.edu.cn。

网络出版日期: 2025-03-20

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Fiber Optics Monitoring and Crack Identification of Large-Scale Components in Prefabricated Metro Stations

HONG Chengyu ,
LI Hong ,
RAO Wei ,
CHEN Xiangsheng ,
LEI Zhen

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(1. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China; 2. State Key Laboratory of Intelligent Geotechnics and Tunnelling (Shenzhen University), Shenzhen 518060, Guangdong, China; 3. PowerChina Southern Investment Co., Ltd., Shenzhen 518052, Guangdong, China)

Online published: 2025-03-20

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摘要

为研究地铁地下车站大尺寸装配式构件在拼装过程中的应变特征与裂纹识别问题，以某装配式地铁车站工程为研究背景，针对大尺寸构件拼装过程中的分布式光纤（distributed fiber optic system，DFOS）监测开展研究，提出主成分层次聚类模型（PCA-HC），融合多个应变分布参数，对裂纹区域应变分布进行特征识别，并开展现场底板拼装裂纹验证。研究结果表明： 1）根据DFOS获取的应变特征，拼装顶板时受拉区平均应变增长率为105.42%，受压区平均应变增长率为110.27%。就底板而言，顶板拼装对其变形影响最大。2）DFOS清晰地反映了裂纹应变异常信号，拼装过程中底板与支撑结构接触处出现裂纹，建议增大支座与拼装构件的接触面积，以减小拼装施工对结构应力/应变集中的影响。3）PCA-HC模型相互融合的降维、特征提取和层次化聚类方法有效识别了裂纹潜在特征，实现了大尺寸装配式构件的裂纹自动化识别与分类，为保障地铁车站大尺寸装配式结构安全施工提供了方法。

关键词： 地铁车站; 预制拼装结构; 分布式光纤; 裂纹识别; 聚类分析

本文引用格式

洪成雨 , 黎宏 , 饶伟 , 陈湘生 , 雷振 . 装配式地铁车站大尺寸构件光纤监测与裂纹识别研究[J]. 隧道建设, 2025 , 45(3) : 499 -510 . DOI: 10.3973/j.issn.2096-4498.2025.03.005

Abstract

The strain characteristics and crack identification of large-scale prefabricated components during the assembly process in underground metro stations are crucial. Focusing on a prefabricated metro station project, where the distributed fiber optic system (DFOS) is utilized to monitor the strain distribution during the assembly of large-scale components, a principal component analysis-hierarchical clustering (PCA-HC) model is proposed, integrating multiple strain distribution parameters to identify strain distribution features in crack-prone zones. The model′s effectiveness is validated through field tests on slab assembly crack detection. Findings are as follows: (1) The strain features obtained from DFOS indicate average strain growth rates of 105.42% in tension zones and 110.27% in compression zones during roof assembly. The roof assembly process has the most significant effect on the base slab deformation. (2) DFOS clearly reflects the abnormal strain signals at the crack locations. Cracks appear at the contact area between the base slab and the supporting structure during assembly. Increasing the contact area between the support and the assembled components is recommended to mitigate the stress/strain concentration caused by assembly construction. (3) The PCA-HC model effectively identifies potential crack features through dimensionality reduction, feature extraction, and hierarchical clustering, enabling automated crack recognition and classification of large-scale prefabricated components, providing a methodological basis for the safe construction of large-scale prefabricated structures in metro stations.

Key words： metro station; prefabricated assembly structures; distributed fiber optics; crack identification; cluster analysis

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