CLC number: TU433

Deformation Monitoring Technology and Early Warning Management for Large-Scale Railway Adjacent Operating Lines

大型铁路邻近运营线的变形监测技术和预警管理

¹ College of Design and Engineering, National University of Singapore, Singapore 119077, Singapore
² School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710000, Shaanxi, China
³ China Railway Seventh Bureau Group Third Engineering Co., Xi'an 710000, Shaanxi, China
⁴ China Railway First Bureau Group Construction and Installation Engineering Co., Xi'an 710000, Shaanxi, China

^† Corresponding author. E-mail: 783806197@qq.com

Received: 9 January 2025

Abstract

This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades, catenary columns, and tracks. Emphasis is placed on data acquisition and processing methods using total stations and automated monitoring systems. Through a comprehensive analysis of lateral, longitudinal, and vertical displacement data from 26 subgrade monitoring points, catenary columns, and track sections, this research evaluates how construction activities influence railway structures. The results show that displacement variations in the subgrades, catenary columns, and tracks remained within the established alert thresholds, exhibiting stable deformation trends and indicating that any adverse environmental impact was effectively contained. Furthermore, this paper proposes an early warning mechanism based on an automated monitoring system, which can promptly detect abnormal deformations and initiate emergency response procedures, thereby ensuring the safe operation of the railway. The integration of big data analysis and deformation prediction models offers a practical foundation for future safety management in railway construction.

摘要

本研究利用浩吉(浩勒报吉至吉安)铁路大型桥梁施工期间获取的变形监测数据，探讨了路基、接触网支柱及轨道的位移行为。重点研究了使用全站仪和自动化监测系统的数据采集与处理方法。通过对26个路基监测点、接触网支柱及轨道区段的横向、纵向和竖向位移数据进行全面分析，评估了施工活动对铁路结构的影响。结果表明，路基、接触网支柱及轨道的位移变化均保持在预设的预警阈值内，呈现出稳定的变形趋势，表明任何不利的环境影响均得到了有效控制。此外，本文提出了一种基于自动化监测系统的预警机制，能够及时检测异常变形并启动应急响应程序，从而确保铁路的安全运营。本研究通过将大数据分析与变形预测模型相结合，为未来施工过程中实施安全措施提供了坚实的科学依据。