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隧道建设(中英文) ›› 2023, Vol. 43 ›› Issue (8): 1425-1437.DOI: 10.3973/j.issn.2096-4498.2023.08.017

• 监控与维护 • 上一篇    下一篇

隧道结构健康管理大数据平台研发及应用

王华1, 2, 3, 路耀邦4, 冯国峰1, 2, 3, 王百泉1, 2, 3, 林春刚1, 2, 3, 闫贺1, 2, 3   

  1. 1. 中铁隧道局集团有限公司, 广东 广州 511458 2. 广东省隧道结构智能监控与维护企业重点实验室, 广东 广州 511458; 3. 中铁隧道勘察设计研究院有限公司, 广东 广州 511458;4. 中铁云网信息科技有限公司, 北京 100160)

  • 出版日期:2023-08-20 发布日期:2023-09-11
  • 作者简介:王华(1977—),男,江西萍乡人,2010 年毕业于西南交通大学,地质工程专业,博士,教授级高级工程师,现从事地质勘察、隧道超前地质预报及隧道设计管理工作。 Email: whua97@126.com。

Development and Application of Big Data Platform for Tunnel Structure Health Management

WANG Hua1, 2, 3, LU Yaobang4, FENG Guofeng1, 2, 3, WANG Baiquan1, 2, 3LIN Chungang1, 2, 3, YAN He1, 2, 3   

  1. (1.China Railway Tunnel Group Co.,Ltd.,Guangzhou 511458,Guangdong,China;2.Guangdong Provincial Key Laboratory of Intelligent Monitoring and Maintenance of Tunnel Structure, Guangzhou 511458,Guangdong,China;3.China Railway Tunnel Consultants Co.,Ltd.,Guangzhou 511458,Guangdong,China;4.CREC CloudNet Information Technology Co.,Ltd.,Beijing 100160,China)

  • Online:2023-08-20 Published:2023-09-11

摘要: 为有效提高隧道管养效率,在总结提炼隧道内部与表观病害类型的基础上,采用Spring Boot+MyBatis Plus架构,应用分布式的RedisMySQL部署开发技术研发隧道结构健康管理大数据平台,并从设备管理、病害管理、大数据智能分析处理等方面进行现场应用。结果表明: 1)研发出的隧道结构健康管理大数据平台能够满足对常见病害数据进行管理展示的功能要求,且界面简洁友好、使用简单方便,实现了隧道病害由传统的依据经验判断到依托数据进行科学决策的转变; 2)该平台基于神经网络模型,实现了隧道表观病害的智能识别,减小了病害识别方面对人工的依赖; 3)该平台支持通过PC端或移动端对病害信息进行多维度查询,解决了以前隧道病害信息获取滞后且查阅不便的问题。

关键词:

铁路隧道, 病害检测, 大数据平台, 病害预测

Abstract: To effectively improve tunnel management and maintenance efficiency, the internal and apparent tunnel diseases are summarized and a tunnel structure health management big data platform is established using Spring Boot with MyBatisPlus architecture and Redis and MySQL distributed deployment. Additionally, the field application is conducted through equipment management, disease management, and big data analysis. Consequently, the results reveal that (1) the big data platform for tunnel structural health management can meet the functional requirements of common disease data management and display, and the interface is simple and friendly, realizing the scientific decisionmaking of tunnel diseases; (2) based on the neural network model, the platform achieves the intelligent recognition of apparent tunnel disease and reduces the dependence on manual disease recognition; and (3) the platform supports multidimensional queries of disease information via personal computers and mobile terminals, thereby solving the problem of delayed and inconvenient access to tunnel disease information. 〖BP(〗Thus, this platform, which has a friendly interface and convenient operation, can provide realtime data support for professional personnel to evaluate the health status of tunnels, thereby making it worth popularizing.

Key words: railway tunnel, disease detection, big data platform; disease forecast