Evaluation of Post-Tunneling Aging Buildings Using the InSAR Nonuniform Settlement Index

Liu, Yuzhou; Cao, Wenxi; Shi, Zhongqi; Yue, Qingrui; Chen, Tiandong; Tian, Lide; Zhong, Rumian; Liu, Yuke

doi:10.3390/rs15143467

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Landslides are one of the most common geological disasters, accounting for 69.1% of all geological disasters in China in 2022, so it is necessary to identify and monitor such disasters effectively [1]. In recent years, Synthetic Aperture Radar Interferometry (InSAR), as a microwave remote sensing technology, has been widely used in landslide monitoring [2][3][4][5]. This technology has the advantages of a wide monitoring range, high spatial resolution, and high monitoring accuracy and has become an essential tool for landslide monitoring [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm

Han,

Guo,

Jiao

et al. 2023

Remote Sensing

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InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter’s experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation.

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Section: Introductionmentioning

confidence: 99%

An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm

Han,

Guo,

Jiao

et al. 2023

Remote Sensing

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Deformation Monitoring of High‐Rise Building Clusters: Acquiring Deformation Coefficients by Combining Satellite Imagery and Persistent Scatterer Interferometry

Zhou,

Chen,

Hao

et al. 2024

Structural Control and Health Monitoring

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Persistent Scatterer Interferometry (PSI) is a remote sensing measurement technology based on electromagnetic waves, capable of simultaneously monitoring deformations in large urban building complexes with millimeter‐level precision. However, relying solely on the line of sight (LOS) deformation sequence based on a specific permanent scatterer cannot accurately analyze building deformations, particularly in cases where high‐rise buildings may simultaneously experience various deformation components such as temperature‐induced deformation, shrinkage, creep, and tilting. To accurately identify the deformation states of high‐rise buildings, the paper begins by systematically summarizing three typical deformation patterns from the unique perspective of synthetic aperture radar (SAR) satellites. These patterns include Pattern I, characterized by temperature‐induced deformation alone, and Pattern II and Pattern III, which involve a combination of deformation in different directions relative to the SAR satellite in addition to temperature‐induced deformation. To accurately monitor the LOS deformation of high‐rise buildings, the paper introduces the concept of acquiring the evolutionary trends of temperature‐related deformation coefficients and proposes a methodology for recognizing and quantifying deformation in high‐rise buildings. Subsequently, this study utilized freely available Sentinel‐1 satellite data to observe the deformation of nine high‐rise buildings in Changsha, China. The research findings indicate that the thermal expansion coefficients of most high‐rise buildings fall within the range of 6 ∼ 12 × 10−6/°C. High‐rise buildings that have been constructed for more than ten years almost no longer experience significant shrinkage or creep, while new constructions may exhibit an initial shrinkage and creep of up to 1.2 × 10−4 mm/mm. Additionally, the study results demonstrate that super‐tall buildings may exhibit centimeter‐scale lateral deformations at their tops due to uneven shrinkage. Findings from the study indicate that the proposed method can achieve cost‐effective and sustainable deformation monitoring of high‐rise building clusters within a large urban area.

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Correcting the Location Error of Persistent Scatterers in an Urban Area Based on Adaptive Building Contours Matching: A Case Study of Changsha

Hu,

Xu,

Wei

et al. 2024

Remote Sensing

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Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets and building databases is often overlooked in deformation research. This paper presents a novel strategy for matching between PS points and building contours based on spatial distribution characteristics. A convex hull is employed to simplify the building outline. Considering the influence of building height and incident angle on geometric distortion, an adaptive buffer zone is established. The PS points on a building are further identified through the nearest neighbor method. In this study, both ascending and descending TerraSAR-X orbit datasets acquired between 2016 and 2019 were utilized for PS-InSAR monitoring. The efficacy of the proposed method was evaluated by comparing the PS-InSAR results obtained from different orbits. Through a process of comparison and verification, it was demonstrated that the matching effect between PS points and building contours was significantly enhanced, resulting in an increase of 29.2% in the number of matching PS points. The results indicate that this novel strategy can be employed to associate PS points with building outlines without the need for complex calculations, thereby providing a robust foundation for subsequent building risk assessment.

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Evaluation of Post-Tunneling Aging Buildings Using the InSAR Nonuniform Settlement Index

Cited by 3 publications

References 31 publications

An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm

An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm

Deformation Monitoring of High‐Rise Building Clusters: Acquiring Deformation Coefficients by Combining Satellite Imagery and Persistent Scatterer Interferometry

Correcting the Location Error of Persistent Scatterers in an Urban Area Based on Adaptive Building Contours Matching: A Case Study of Changsha

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