Time-Series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong–Zhuhai–Macao Bridge (HZMB) from Sentinel-1A Observations
Abstract:The synthetic aperture radar interferometry (InSAR) technique has been applied in monitoring the deformation of infrastructures, such as bridges, highways, railways and subways. Persistent scatterer (PS)-InSAR is one of the InSAR techniques, which utilises persistent scatterers to derive long-term displacements. This study applied time-series methods to post-process the PS-InSAR-derived time-series displacements with the use of 86 Sentinel-1A acquisitions spanning from 6 January 2018 to 27 November 2020. Empir… Show more
“…Data acquisition is carried out by sensors, which may be installed in-situ like optical fibers [8], piezoelectric sensors [9], GNSS (Global Navigation Satellite Systems) receivers [10], accelerometers [11], and inclinometers [12]. Alternatively, the sensors might gather information relevant to assessing structural soundness remotely as is the case with robotic total stations [13], 3D-laser scanners [14], or radar systems [15][16][17][18]. Subsequently experts interpret the observations, diagnose the health status of the structure, and initiate on-site inspection of maintenance work in case of irregularities.…”
Section: Mimo-sar In Structural Health Monitoringmentioning
Terrestrial Radar Interferometry (TRI) is a measurement technique capable of measuring displacements with high temporal resolution at high accuracy. Current implementations of TRI use large and/or movable antennas for generating two-dimensional displacement maps. Multiple Input Multiple Output Synthetic Aperture Radar (MIMO-SAR) systems are an emerging alternative. As they have no moving parts, they are more easily deployable and cost-effective. These features suggest the potential usage of MIMO-SAR interferometry for structural health monitoring (SHM) supplementing classical geodetic and mechanical measurement systems. The effects impacting the performance of MIMO-SAR systems are, however, not yet sufficiently well understood for practical applications. In this paper, we present an experimental investigation of a MIMO-SAR system originally devised for automotive sensing, and assess its capabilities for deformation monitoring. The acquisitions generated for these investigations feature a 180∘ Field-of-View (FOV), distances of up to 60 m and a temporal sampling rate of up to 400 Hz. Experiments include static and dynamic setups carried out in a lab-environment and under more challenging meteorological conditions featuring sunshine, fog, and cloud-cover. The experiments highlight the capabilities and limitations of the radar, while allowing quantification of the measurement uncertainties, whose sources and impacts we discuss. We demonstrate that, under sufficiently stable meteorological conditions with humidity variations smaller than 1%, displacements as low as 25m can be detected reliably. Detecting displacements occurring over longer time frames is limited by the uncertainty induced by changes in the refractive index.
“…Data acquisition is carried out by sensors, which may be installed in-situ like optical fibers [8], piezoelectric sensors [9], GNSS (Global Navigation Satellite Systems) receivers [10], accelerometers [11], and inclinometers [12]. Alternatively, the sensors might gather information relevant to assessing structural soundness remotely as is the case with robotic total stations [13], 3D-laser scanners [14], or radar systems [15][16][17][18]. Subsequently experts interpret the observations, diagnose the health status of the structure, and initiate on-site inspection of maintenance work in case of irregularities.…”
Section: Mimo-sar In Structural Health Monitoringmentioning
Terrestrial Radar Interferometry (TRI) is a measurement technique capable of measuring displacements with high temporal resolution at high accuracy. Current implementations of TRI use large and/or movable antennas for generating two-dimensional displacement maps. Multiple Input Multiple Output Synthetic Aperture Radar (MIMO-SAR) systems are an emerging alternative. As they have no moving parts, they are more easily deployable and cost-effective. These features suggest the potential usage of MIMO-SAR interferometry for structural health monitoring (SHM) supplementing classical geodetic and mechanical measurement systems. The effects impacting the performance of MIMO-SAR systems are, however, not yet sufficiently well understood for practical applications. In this paper, we present an experimental investigation of a MIMO-SAR system originally devised for automotive sensing, and assess its capabilities for deformation monitoring. The acquisitions generated for these investigations feature a 180∘ Field-of-View (FOV), distances of up to 60 m and a temporal sampling rate of up to 400 Hz. Experiments include static and dynamic setups carried out in a lab-environment and under more challenging meteorological conditions featuring sunshine, fog, and cloud-cover. The experiments highlight the capabilities and limitations of the radar, while allowing quantification of the measurement uncertainties, whose sources and impacts we discuss. We demonstrate that, under sufficiently stable meteorological conditions with humidity variations smaller than 1%, displacements as low as 25m can be detected reliably. Detecting displacements occurring over longer time frames is limited by the uncertainty induced by changes in the refractive index.
“…Zollini et al [17] detected the width and length of cracks and extension of weathered areas using UAV Photogrammetry. The satellite-based remote sensing techniques are also used for assessing the structural condition of bridges [18][19][20][21][22]. Gagliardi et al [20] achieved the high precision of displacement measurement of bridges using satellite remote sensing Persistent Scatterer Interferometry (PSI).…”
Section: Introductionmentioning
confidence: 99%
“…Tosti et al [21] integrated the Ground Penetrating Radar (GPR) and Interferometric Synthetic Aperture Radar (InSAR) for monitoring transport assets at network level. Xiong et al [22] derived the long-term displacements of the Hong Kong-Zhuhai-Macao Bridge (HZMB) by the PSI and InSAR technology.…”
The functional and structural characteristics of civil engineering works, in particular bridges, influence the performance of transport infrastructure. Remote sensing technology and other advanced technologies could help bridge managers review structural conditions and deteriorations through bridge inspection. This paper proposes an artificial intelligence-based methodology to solve the condition assessment of regional bridges and optimize their maintenance schemes. It includes data integration, condition assessment, and maintenance optimization. Data from bridge inspection reports is the main source of this data-driven approach, which could provide a substantial amount og condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified. Given the need to manage limited resources and ensure safety and functionality, adequate maintenance schemes for regional bridges are optimized with genetic algorithms. The proposed data-driven methodology is applied to real regional highway bridges. The regional inspection information is obtained with the help of emerging technologies. The established structural deterioration models achieve up to 85% prediction accuracy. The obtained optimal maintenance schemes could be chosen according to actual structural conditions, maintenance requirements, and total budget. Data-driven decision support can substantially aid in smart and efficient maintenance planning of road bridges.
“…For example, persistent scatter InSAR (PSInSAR) and small baseline subset (SBAS) methods were proposed to reduce the effect of unwanted phase contributions, such as atmosphere, decorrelation noise, and digital elevation model (DEM) phase residual, and to retrieve more accurate ground deformation [12,13]. Currently, MTInSAR has been widely applied in monitoring the time series of deformation in different scenarios, such as seismic motion, landslides, and ground subsidence [14][15][16][17][18][19][20][21][22]. In addition to mining activities, MTInSAR offers great potential for promoting studies of mining deformation, such as mechanism interpretation, parameter inversion, and deformation prediction [3].…”
Ground deformation related to mining activities may occur immediately or many years later, leading to a series of mine geological disasters, such as ground fissures, collapses, and even mining earthquakes. Deformation monitoring has been carried out with techniques, such as multitemporal interferometric synthetic aperture radar (MTInSAR). Over the past decade, MTInSAR has been widely used in monitoring mining deformation, and it is still difficult to retrieve mining deformation over dense vegetation areas. In this study, we use multiple-platform SAR images to retrieve mining deformation over dense vegetation areas. The high-quality interferograms are selected by the coherence map, and the mining deformation is retrieved by the MSBAS-InSAR technique. SAR images from TerraSAR-X, Sentinel-1A, Radarsat-2, and PALSAR-2 over the Fengfeng mining area, Heibei, China, are used to retrieve the deformation of mining activities covered with dense vegetation. The results show that the subsidence in the Fengfeng mining area reaches up to 90 cm over the period from July 2015 to April 2016. The root-mean-square error (RMSE) between the results from InSAR and leveling is 83.5 mm/yr at two mining sites, i.e., Wannian and Jiulong Mines.
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