Satellite-Based InSAR Monitoring of Highway Bridges: Validation Case Study on the North Channel Bridge in Ontario, Canada

Cusson, Daniel; Trischuk, Ken; Hebert, Daniel N.; Hewus, Glenn; Gara, Matthew; Ghuman, Parwant

doi:10.1177/0361198118795013

Cited by 35 publications

(18 citation statements)

References 7 publications

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“…Gatti et al (2010) used PS-InSAR technology to analyze the deformation of a railway in Italy over 3 years, which further proved the applicability of time series InSAR technology. Daniel et al (2018) used InSAR technology to monitor the changes in highway bridges, which verified the feasibility of InSAR technology for monitoring lifelines and other facilities. Xing et al (2018) improved the technical accuracy of PS-InSAR by installing angular reflectors, but the installation of angular reflectors requires manpower and materials and depends on the amount of SAR data generated by PS-InSAR technology.…”

Section: Introductionmentioning

confidence: 75%

“…In view of the drawbacks of D-InSAR technology affected by the temporal baseline and spatial displacement, many scholars have proposed new technologies, such as InSAR time series analysis methods, to obtain better settlement detection results. The main time series analysis methods of InSAR include the permanent scatter interferometry SAR (PS-InSAR) method (Ferreti, 2000(Ferreti, , 2001Rosi et al, 2017;Yang et al, 2018), the small baseline subset InSAR (SABS-InSAR) method (Berardino et al, 2002;Dong et al, 2014;Li et al, 2014;Corsetti et al, 2018), and the stacking method (Lundgren et al, 2001;Chang et al, 2011;Dai et al, 2018). InSAR technology in China developed later but has also achieved good research results.…”

Section: Introductionmentioning

confidence: 99%

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InSAR technique applied to the monitoring of the Qinghai–Tibet Railway

Zhang

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. The Qinghai–Tibet Railway is located on the Qinghai–Tibet Plateau and is the highest-altitude railway in the world. With the influence of human activities and geological disasters, it is necessary to monitor ground deformation along the Qinghai–Tibet Railway. In this paper, Advanced Synthetic Aperture Radar (ASAR) (T405 and T133) and TerraSAR-X data were used to monitor the Lhasa–Naqu section of the Qinghai–Tibet Railway from 2003 to 2012. The data period covers the time before and after the opening of the railway (total of 10 years). This study used full rank matrix small baseline subset InSAR (FRAM-SBAS) time-series analysis to analyze the Qinghai–Tibet Railway. Before the opening of the railway (from 2003 to 2005), the Lhasa–Naqu road surface deformation was not obvious, with a maximum deformation of approximately 5 mm yr−1; in 2007, the railway was completed and opened to traffic, and the resulting subsidence of the railway in the district of Damxung was obvious (20 mm yr−1). After the opening of the railway (from 2008 to 2010), the Damxung segment included a considerable area of subsidence, while the northern section of the railway was relatively stable. The results indicate that FRAM-SBAS technology is capable of providing comprehensive and detailed subsidence information regarding railways with millimeter-level accuracy. An analysis of the distribution of geological hazards in the Damxung area revealed that the distribution of the subsidence area coincided with that of the geological hazards, indicating that the occurrence of subsidence in the Damxung area was related to the influence of surrounding geological hazards and faults. Overall, the peripheral surface of the Qinghai–Tibet Railway is relatively stable but still needs to be verified with real-time monitoring to ensure that the safety of the railway is maintained.

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Section: Introductionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

InSAR technique applied to the monitoring of the Qinghai–Tibet Railway

Zhang

et al. 2019

Nat. Hazards Earth Syst. Sci.

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“…We designed a simulator that generates TSInSAR signals by injecting synthetic deformation rate and DEM error with real-world baseline parameters. Moreover, we apply an industry 3vGeomatics's industry-standard InSAR processing pipeline [40,51] to perform data preprocessing as well as generate the reference results to assess the performance of the proposed IGS-CMAES method on real data. The experiments were designed to estimate two important parameters: optimization cost and accuracy.…”

Section: Methodsmentioning

confidence: 99%

IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

Sun¹,

Zimmer²,

Mukherjee³

et al. 2021

Remote Sensing

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Interferometric synthetic aperture radar (InSAR) has become an increasingly recognized remote sensing technology for earth surface monitoring. Slow and subtle terrain displacements can be estimated using time-series InSAR (TSInSAR) data. However, a substantial increase in the availability of exclusive time series data necessitates the development of more efficient and effective algorithms. Research in these areas is usually carried out by solving complicated optimization problems, which is very computationally expensive and time-consuming. This work proposes a two-stage black-box optimization framework to jointly estimate the average ground deformation rate and terrain digital elevation model (DEM) error. The method performs an iterative grid search (IGS) to acquire coarse candidate solutions, and then a covariance matrix adaptive evolution strategy (CMAES) is adopted to obtain the final local results. The performance of our method is evaluated using both simulated and real datasets. Both quantitative and qualitative comparisons using different optimizers support the reliability and effectiveness of our work. The proposed IGS-CMAES achieves higher accuracy with a significantly fewer number of objective function evaluations than other established algorithms. It offers the possibility for wide-area monitoring, where high precision and real-time processing is essential.

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“…It can detect information of ground displacement with millimeter precision, showing high potential ability for the application to large traffic infrastructures (i.e., railways, highways, and bridges) [3][4][5][6]. Earlier study shows that bridge railings, curbs, and pavements can be treated as artificial corner reflector structures, which are easily to be identified as PS points in SAR images [7]. Successful cases also proved the applicability using PSI technology with high-resolution SAR images to monitor slow deformation of large-scale bridges and other artificial structures [8,9].…”

Section: Introductionmentioning

confidence: 99%

Time Series Deformation Monitoring over Large Infrastructures around Dongting Lake Using X‐Band PSI with a Combined Thermal Expansion and Seasonal Model

et al. 2021

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The long-term spatial-temporal deformation monitoring of densely distributed infrastructures near the lake area is of great significance to understand the urban health status and prevent the potential traffic safety problems. In this paper, the permanent scatterer interferometry (PSI) technology with TerraSAR-X imagery over the area around Dongting Lake was utilized to generate the long-term spatial-temporal deformation. Since the X-band SAR interferometric phases are highly influenced by the thermal dilation of the observed objects, and the deformation of large infrastructures are highly related to external temperature, a combined deformation model considering the thermal expansion and the seasonal environmental factors was proposed to model the temporal variations of the deformation. The time series deformation and the thermal dilation parameter over the area were obtained, and a comparative study with the traditional linear model was conducted. The Dongting Lake Bridge and the typical feature points distributed around the lake were analyzed in details. In order to compensate for the unavailability of external in situ measurements over the area, phase residuals and the subsidence generated through Differential Interferometric Synthetic Aperture Radar (D-InSAR) were utilized to verify the accuracy of the obtained deformation time series. Experiment results suggested that the proposed model is suitable and suggested for the selected study site. The root mean square error (RMSE) of the residual phase was estimated as 0.32 rad, and the RMSE compared with D-InSAR derived deformation was ±1.1 mm.

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Satellite-Based InSAR Monitoring of Highway Bridges: Validation Case Study on the North Channel Bridge in Ontario, Canada

Cited by 35 publications

References 7 publications

InSAR technique applied to the monitoring of the Qinghai–Tibet Railway

InSAR technique applied to the monitoring of the Qinghai–Tibet Railway

IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

Time Series Deformation Monitoring over Large Infrastructures around Dongting Lake Using X‐Band PSI with a Combined Thermal Expansion and Seasonal Model

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