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Analysis and Evaluation of Land Subsidence along Linear Engineering Based on InSAR Data

  • Surveying and Geo-Spatial Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Land subsidence is a worldwide geological environment problem, which can bring about lasting and serious harm to linear engineering and urban construction. In this paper, land subsidence along the linear engineering was monitored and analyzed by the Synthetic Aperture Radar (SAR) images, high-precision leveling results, groundwater level monitoring data. Combined with Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) technology, spatial analysis technology of geographic information system and contribution rate method, the spatial and temporal evolution characteristics, influencing factors, and the contribution rate of each factor to land subsidence along the Lunan high-speed railway from 2016 to 2018 were researched. The accuracy of subsidence results by InSAR monitoring was verified by the linear fitting method and root mean square error method. The influence of uneven subsidence on linear engineering was discussed based on the evaluation results of subsidence gradient zoning. The results indicate that the maximum accumulated subsidence along the Lunan high-speed railway is 499 mm. Multiple subsidence center areas have been formed in the study area, with the maximum subsidence rate exceeding -55 mm/yr. The maximum subsidence gradient and curvature radius of the Lunan high-speed railway meet the requirements of line smoothness when the train speed is 350 km/h. The coal mining, compressible layer thickness, and changes in groundwater level are positively correlated with land subsidence, the total contribution rate to land subsidence is more than 90%. The research results provide scientific support for the prevention and control of land subsidence along the linear engineering.

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Acknowledgments

We would like to acknowledge the financial support from the National Natural Science Foundation of China (Grant No.: 42007234).

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Correspondence to Chao Jia.

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Ding, P., Jia, C., Di, S. et al. Analysis and Evaluation of Land Subsidence along Linear Engineering Based on InSAR Data. KSCE J Civ Eng 25, 3477–3491 (2021). https://doi.org/10.1007/s12205-021-0201-z

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  • DOI: https://doi.org/10.1007/s12205-021-0201-z

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