Study on three-dimensional deformation monitoring of railway slopes via synergistic integration of Beidou and InSAR

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2026
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Abstract
Abstract Remote-sensing-based slope stability monitoring is vital for railway safety. This study is grounded in the demands for high timeliness and multi-element monitoring of three-dimensional deformation of modern railway slopes. Aiming at the issue that existing technologies struggle to balance spatial and temporal resolution in the process of fusing and resolving three-dimensional deformation using the BeiDou Navigation Satellite System (BDS) and Interferometric Synthetic Aperture Radar (InSAR), a point-surface hierarchical fusion strategy is proposed. After constructing a unified fused observation equation, spatial regularization constraints and local joint adjustment are introduced to balance spatiotemporal resolution from two levels. This ensures the consistency and reliability of 3D accuracy under the hierarchical strategy, while achieves constrained representation of the large-scale deformation trend and high-precision temporal evolution of the 3D deformation field. Building on the hierarchical fusion results, this paper proposes a 3D deformation risk classification method for railway slopes, enabling refined slope monitoring and early disaster warning with a feasible technical scheme. The effectiveness of the method is validated on typical slopes along the Shijiazhuang-Taiyuan Railway. The results indicated that (1) the average 3D deformation accuracy obtained by the point–surface hierarchical fusion strategy is superior to 10 mm, ensuring both accuracy balance and reliability under the hierarchical strategy while taking into account both spatial and temporal resolutions. (2) The proposed three-dimensional deformation risk classification method for slopes preliminarily verifies the feasibility of the hierarchical fusion strategy for 3D deformation monitoring of railway slopes considering both spatial and temporal resolutions.
Reference Key
openalex_W7221525571 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nengpu Yang, Ru Chen, Shiya Zeng, Yongchun Li, Quan Huang, Fenling Feng
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Journal Transportation Safety and Environment
Year 2026
DOI
10.1093/tse/tdag061
URL
https://doi.org/10.1093/tse/tdag061
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