考虑多源数据融合的隧道结构健康监测云平台研究  

Research on a Cloud Platform for Tunnel Structure Health Monitoring Considering Multi-Source Data Fusion

作　　者：张保卫[1] ZHANG Baowei(China Railway 18th Bureau Group 2nd Engineering Co.Ltd.,Tangshan Hebei 064000,China)

机构地区：[1]中铁十八局集团第二工程有限公司,河北唐山064000

出　　处：《铁道建筑技术》2025年第1期41-44,49,共5页Railway Construction Technology

基　　金：中铁十八局集团第二工程有限公司技术开发项目(2-JF-2021-滇中引水-2-001)。

摘　　要：针对隧道工程在复杂环境中的运行挑战,提出了一种基于多源数据融合的隧道结构健康监测云平台。该平台涵盖数据采集、传输、处理及应用的全流程,依托感知层、传输层、数据处理层和应用层的协同工作,实现对隧道结构的智能化全方位监测。研究以亭子口引水隧洞穿越董家隧道的实际工程为背景,构建自动化监测方案,通过多种传感器实现对沉降、应力、收敛等参数的高精度监测。系统利用主成分分析降维与高斯混合模型进行健康状态评估,并通过小波降噪提升数据质量,噪声减少幅度达到30%,显著提升了监测数据的稳定性和精度,为预警机制提供支持。测试结果显示,该系统具备高稳定性和适应性,能够实时识别潜在结构风险,为隧道工程的安全管理与优化提供了有力支撑。This study addresses the operational challenges faced by tunnel engineering in complex environments by proposing a tunnel structural health monitoring cloud platform based on leveraging multi-source data fusion.The platform encompasses the entire process from data acquisition,transmission,and processing to application,achieving intelligent and comprehensive monitoring of tunnel structures through the collaborative operation of sensing,transmission,data processing,and application layers.Using the Tingzikou Diversion Tunnel′s intersection with the Dongjia Tunnel as a case study,an automated monitoring framework was developed,employing various sensors to achieve high-precision monitoring of parameters such as settlement,stress,and convergence.Principal Component Analysis(PCA)and Gaussian Mixture Models(GMM)were utilized for health state assessment,while wavelet denoising reduced noise by 30%,significantly enhancing data quality and precision to support an effective early warning system.Testing results demonstrate the system′s high stability and adaptability,enabling real-time identification of potential structural risks.This platform provides robust support for the safety management and optimization of tunnel engineering projects.

关 键 词：隧道工程 多源数据融合 健康监测 云平台 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置] U456[自动化与计算机技术—控制科学与工程]