Monitoring of the Manhattan Bridge and interferometric radar systems

Mayer, Lorenzo; Yanev, Bojidar; Olson, Larry D.; Smyth, Andrew W.

doi:10.1201/b10430-517

Cited by 17 publications

(10 citation statements)

References 1 publication

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“…For mode V2 and V3, the frequencies identified from the displacements matched very well with the reference data. The frequency of mode T1 (0.40 Hz) identified from the EEM (Mayer et al, 2010b) Before 2009 0.23 -0.30 0.49 displacement was the same to that identified from the acceleration data collected in 2016, but was 8.1% larger than the reference data (0.37 Hz) recorded in earlier years in literature before 2009 (Mayer et al, 2010b;Jang and Smyth, 2017). The increase in the torsional mode frequency T1 as identified from the EEM displacements obtained in 2017 was due to an increase of torsional stiffness by installing truss stiffening to reduce twisting in the Manhattan Bridge Reconstruction Program, which was completed around 2010.…”

Section: Frequency Domain Comparison For the Secondsupporting

confidence: 54%

“…However, they are limited by accessibility to the structures. The accuracy of the displacement obtained by GPS is around

\pm 1.5 cm

(Celebi, ; Mayer et al., ). For accelerometers, errors can be significant in the displacements converted from accelerations through double integration because the initial condition is difficult to estimate and acceleration measurements always have some errors (Park et al., ).…”

Section: Introductionmentioning

confidence: 99%

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Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

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Computer vision‐based displacement measurement for structural monitoring has grown popular. However, tracking natural low‐contrast targets in low‐illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity‐based vision‐sensing techniques. A new edge‐enhanced‐matching (EEM) technique improved from the previous orientation‐code‐matching (OCM) technique is proposed to enable robust tracking of low‐contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked‐segmentation filtering technique is also developed to post‐process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low‐contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long‐span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy.

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Section: Frequency Domain Comparison For the Secondsupporting

confidence: 54%

“…However, they are limited by accessibility to the structures. The accuracy of the displacement obtained by GPS is around

\pm 1.5 cm

Section: Introductionmentioning

confidence: 99%

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

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“…There are existing examples of systems which utilise FMCW radar for displacement detection in structures [38], [39], [40]. These devices are placed off-structure and measure the displacement of specific points of the structure.…”

Section: B Obtaining Structural Displacementmentioning

confidence: 99%

Experimental Validation of a Radar-Based Structural Health Monitoring System

Amies

Pretty

Rodgers

et al. 2019

IEEE/ASME Trans. Mechatron.

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This paper presents a new structural health monitoring (SHM) method using frequency-modulated continuous wave (FMCW) radar. The method was developed to circumvent issues with SHM methods' need for displacement measurements, which can be difficult to obtain robustly through integrated accelerations, or through other displacement measurement methods. Instead, interstorey drift ratios were estimated through the direct measurement of interstorey displacement using FMCW radar. Simulation of this method using historical structural response data verified suitably accurate displacement measurements could be obtained using FMCW radar, and prompted the construction of a prototype system. Experimental validation of this prototype was carried out on a shake table. The precision of the system in terms of mean interstorey drift ratio was found to be 1.09 × 10 −3. These results are encouraging for the future deployment of this SHM approach.

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“…Laser scanners and more recently Ground Based Synthetic Aperture Radar (GBSAR) have been used for deformation monitoring, in particular of landslides [Farina et al, 2012]. Deflection monitoring of structures and bridges using GBSAR have also been carried out [Gentile, 2009;Mayer et al, 2010]. The use of laser scanners for deflection monitoring of bridges is not seen as being very feasible, for two reasons.…”

Section: Summary Of the Researchmentioning

confidence: 99%

A Tale of Five Bridges; the use of GNSS for Monitoring the Deflections of Bridges

Roberts

Brown

Tang

et al. 2014

Journal of Applied Geodesy

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The first Bridge Monitoring surveying was carried out in 1996 by the authors, through attaching Ashtech ZXII GPS receivers onto the Humber Bridge' parapet, and gathering and further analysing the resulting 1Hz RTK GPS data. Various surveys have subsequently been conducted on the Humber Bridge, the Millennium Bridge, the Forth Road Bridge, the Severn Suspension Bridge and the Avonmouth Viaduct. These were all carried out using survey grade carrier phase/pseudorange GPS and later GNSS receivers. These receivers were primarily dual frequency receivers, but the work has also investigated the use of single frequency receivers, gathering data at 1Hz, 10Hz, 20Hz and even 100Hz. Various aspects of the research conducted are reported here, as well as the historical approach. Conclusions are shown in the paper, as well as lessons learnt during the development of this work. The results are compared to various models that exist of the bridges' movements, and compare well. The results also illustrate that calculating the frequencies of the movements, as well as looking at the magnitudes of the movements, is an important aspect of this work. It is also shown that in instances where the magnitudes of the movements of the bridge under investigation are small, it is still possible to derive very accurate frequencies of the movements, in comparison to the existing models.

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Monitoring of the Manhattan Bridge and interferometric radar systems

Cited by 17 publications

References 1 publication

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Experimental Validation of a Radar-Based Structural Health Monitoring System

A Tale of Five Bridges; the use of GNSS for Monitoring the Deflections of Bridges

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