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隧道建设(中英文) ›› 2026, Vol. 46 ›› Issue (9): 1846-1860.DOI: 10.3973/j.issn.2096-4498.2026.09.003

• 研究与探索 • 上一篇    下一篇

铁路隧道钻爆法全工序智能建造体系构建与管理平台研发

林春刚1, 2, 3, 李杰林4, *, 周志彬1, 2, 3, 杨承业4, 张丹枫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. 中南大学资源与安全工程学院, 湖南 长沙 410083)
  • 出版日期:2026-09-20 发布日期:2026-09-20
  • 作者简介:林春刚(1980—),男,黑龙江林口人,2004年毕业于长沙理工大学,机械设计制造及其自动化专业,本科,正高级工程师,主要从事隧道及地下工程施工装备研发、隧道智能建造及品质提升科技创新与技术管理工作。E-mail: 9617488@qq.com。*通信作者: 李杰林, E-mail: lijielin@163.com。

Development of Intelligent Construction System and Its Management Platform for Railway Tunneling With Drilling and Blasting Method

LIN Chungang1, 2, 3, LI Jielin4, *, ZHOU Zhibin1, 2, 3, YANG Chengye4, ZHANG Danfeng1, 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. School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China)
  • Online:2026-09-20 Published:2026-09-20

摘要: 针对铁路隧道钻爆法施工工序链条长、装备类型多、信息分散和质量安全管控依赖经验等问题,为提升全工序协同组织与动态管控能力,研发智能建造体系构建与管理平台。研究以开挖、装运、支护、仰拱、防水、二次衬砌、沟槽施工和检测养护等工序为对象,构建装备-工序-平台协同框架,梳理12类核心装备的功能定位、作业边界和衔接关系,明确装备配置与工序组织之间的对应关系; 围绕装备接入、状态感知、作业参数采集、多源数据协同和辅助决策,建立现场数据采集、标准化接入、过程关联和平台交互方法,并开发基于BIM(building information modeling)+GIS(geographical information system)+IoT(internet of things)的智能建造管理平台。主要研究结论如下: 1)构建的钻爆法隧道智能建造体系,可支撑钻孔定位、超欠挖分析、支护质量控制、衬砌养护和工序衔接等过程管理,促进装备状态、施工参数、质量检测和风险信息的实时汇聚与关联分析; 2)管理平台可实现关键施工过程动态展示、异常识别和辅助调度,改善信息滞后、工序衔接不畅和管理反馈不足等问题; 3)构建的钻爆法隧道智能建造体系在成渝中线高铁龙泉山隧道应用中实现了隧道开挖、支护、衬砌等核心作业线的全工序机械化、自动化与智能化协同作业,显著提升施工效率与工程质量,单工作面月进尺最高达137 m,单孔钻孔作业时间比传统钻爆法缩短70%,开挖作业人员减少40%,显著降低了作业人员的安全风险和劳动强度。

关键词: 铁路隧道, 钻爆法, 全工序, 智能建造, 管理平台

Abstract: To address the challenges in railway tunnel construction using the drilling and blasting method, including long process chains, diverse equipment types, decentralized information, and experience-dependent quality and safety control, an intelligent construction system and its supporting management platform are developed. Focusing on key processes such as excavation, mucking, support, invert, waterproofing, secondary lining, gutter construction, and inspection and maintenance, a collaborative framework integrating equipment, processes, and the developed platform is established. The study identifies the functional positioning, operational boundaries, and interrelationships of 12 core equipment types and clarifies the mapping between equipment configuration and process organization. For equipment integration, status sensing, operational parameter acquisition, multisource data collaboration, and decision support, methods for onsite data collection, standardized access, process correlation, and platform interaction are developed. Furthermore, an intelligent construction management platform based on building information modeling, geographic information systems, and Internet of Things technologies is designed. The main research conclusions are as follows: (1) The developed system supports process management, including drilling positioning, overbreak and underbreak analysis, quality control, lining curing, and process handover. It facilitates real-time aggregation and correlation analysis of equipment status, construction parameters, quality inspection data, and risk information. (2) The developed platform enables dynamic visualization of key construction processes, anomaly identification, and auxiliary scheduling, alleviating issues such as information lag, poor process connectivity, and inadequate management feedback. (3) Its application in the Longquanshan Tunnel of the Chengdu-Chongqing central high-speed railway shows that the developed system realizes fully mechanized, automated, and intelligent collaborative operation of core construction lines, including tunnel excavation, support, and lining. It greatly improves construction efficiency and engineering quality, with a maximum monthly advance rate of 137 m for a single working face. The drilling time for a single hole is reduced by 70% compared with conventional drilling and blasting, and the requirement for excavation workers is reduced by 40%, significantly lowering safety risks and labor intensity for onsite personnel.

Key words: railway tunnel, drilling and blasting method, full process, intelligent construction, management platform