MEMS Based Bridge Monitoring Supported By Image-Assisted Total Station

Research output: Contribution to journalConference articleResearchpeer review

Authors

  • Mohammad Omidalizarandi
  • Ingo Neumann
  • Eva Kemkes
  • Boris Kargoll
  • Dmitri Diener
  • Jürgen Rüffer
  • Jens-André Paffenholz

Research Organisations

External Research Organisations

  • Anhalt University of Applied Sciences
  • Allsat GmbH
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Details

Original languageEnglish
Pages (from-to)833-842
Number of pages10
JournalThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume42
Issue number4/W18
Publication statusE-pub ahead of print - 18 Oct 2019
EventISPRS International GeoSpatial Conference 2019, Joint Conferences of 5th Sensors and Models in Photogrammetry and Remote Sensing (SMPR) and 3rd Geospatial Information Research (GI Research) - Karaj, Iran, Islamic Republic of
Duration: 12 Oct 201914 Oct 2019

Abstract

In this study, the feasibility of Micro-Electro-Mechanical System (MEMS) accelerometers and an image-assisted total station (IATS) for short- and long-term deformation monitoring of bridge structures is investigated. The MEMS sensors of type BNO055 from Bosch as part of a geo-sensor network are mounted at different positions of the bridge structure. In order to degrade the impact of systematic errors on the acceleration measurements, the deterministic calibration parameters are determined for fixed positions using a KUKA youBot in a climate chamber over certain temperature ranges. The measured acceleration data, with a sampling frequency of 100 Hz, yields accurate estimates of the modal parameters over short time intervals but suffer from accuracy degradation for absolute position estimates with time. To overcome this problem, video frames of a passive target, attached in the vicinity of one of the MEMS sensors, are captured from an embedded on-axis telescope camera of the IATS of type Leica Nova MS50 MultiStation with a practical sampling frequency of 10 Hz. To identify the modal parameters such as eigenfrequencies and modal damping for both acceleration and displacement time series, a damped harmonic oscillation model is employed together with an autoregressive (AR) model of coloured measurement noise. The AR model is solved by means of a generalized expectation maximization (GEM) algorithm. Subsequently, the estimated model parameters from the IATS are used for coordinate updates of the MEMS sensor within a Kalman filter approach. The experiment was performed for a synthetic bridge and the analysis shows an accuracy level of sub-millimetre for amplitudes and much better than 0.1 Hz for the frequencies. </jats:p>

Keywords

    Displacement and vibration analysis, MEMS accelerometer, Image-assisted total station, Modal parameter identification, Robust parameter estimation, Kalman filter, Bridge monitoring

ASJC Scopus subject areas

Cite this

MEMS Based Bridge Monitoring Supported By Image-Assisted Total Station. / Omidalizarandi, Mohammad; Neumann, Ingo; Kemkes, Eva et al.
In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 42, No. 4/W18, 18.10.2019, p. 833-842.

Research output: Contribution to journalConference articleResearchpeer review

Omidalizarandi, M, Neumann, I, Kemkes, E, Kargoll, B, Diener, D, Rüffer, J & Paffenholz, J-A 2019, 'MEMS Based Bridge Monitoring Supported By Image-Assisted Total Station', The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, no. 4/W18, pp. 833-842. https://doi.org/10.5194/isprs-archives-xlii-4-w18-833-2019, https://doi.org/10.15488/10873
Omidalizarandi, M., Neumann, I., Kemkes, E., Kargoll, B., Diener, D., Rüffer, J., & Paffenholz, J.-A. (2019). MEMS Based Bridge Monitoring Supported By Image-Assisted Total Station. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(4/W18), 833-842. Advance online publication. https://doi.org/10.5194/isprs-archives-xlii-4-w18-833-2019, https://doi.org/10.15488/10873
Omidalizarandi M, Neumann I, Kemkes E, Kargoll B, Diener D, Rüffer J et al. MEMS Based Bridge Monitoring Supported By Image-Assisted Total Station. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019 Oct 18;42(4/W18):833-842. Epub 2019 Oct 18. doi: 10.5194/isprs-archives-xlii-4-w18-833-2019, 10.15488/10873
Omidalizarandi, Mohammad ; Neumann, Ingo ; Kemkes, Eva et al. / MEMS Based Bridge Monitoring Supported By Image-Assisted Total Station. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019 ; Vol. 42, No. 4/W18. pp. 833-842.
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T1 - MEMS Based Bridge Monitoring Supported By Image-Assisted Total Station

AU - Omidalizarandi, Mohammad

AU - Neumann, Ingo

AU - Kemkes, Eva

AU - Kargoll, Boris

AU - Diener, Dmitri

AU - Rüffer, Jürgen

AU - Paffenholz, Jens-André

PY - 2019/10/18

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N2 - In this study, the feasibility of Micro-Electro-Mechanical System (MEMS) accelerometers and an image-assisted total station (IATS) for short- and long-term deformation monitoring of bridge structures is investigated. The MEMS sensors of type BNO055 from Bosch as part of a geo-sensor network are mounted at different positions of the bridge structure. In order to degrade the impact of systematic errors on the acceleration measurements, the deterministic calibration parameters are determined for fixed positions using a KUKA youBot in a climate chamber over certain temperature ranges. The measured acceleration data, with a sampling frequency of 100 Hz, yields accurate estimates of the modal parameters over short time intervals but suffer from accuracy degradation for absolute position estimates with time. To overcome this problem, video frames of a passive target, attached in the vicinity of one of the MEMS sensors, are captured from an embedded on-axis telescope camera of the IATS of type Leica Nova MS50 MultiStation with a practical sampling frequency of 10 Hz. To identify the modal parameters such as eigenfrequencies and modal damping for both acceleration and displacement time series, a damped harmonic oscillation model is employed together with an autoregressive (AR) model of coloured measurement noise. The AR model is solved by means of a generalized expectation maximization (GEM) algorithm. Subsequently, the estimated model parameters from the IATS are used for coordinate updates of the MEMS sensor within a Kalman filter approach. The experiment was performed for a synthetic bridge and the analysis shows an accuracy level of sub-millimetre for amplitudes and much better than 0.1 Hz for the frequencies.

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KW - MEMS accelerometer

KW - Image-assisted total station

KW - Modal parameter identification

KW - Robust parameter estimation

KW - Kalman filter

KW - Bridge monitoring

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DO - 10.5194/isprs-archives-xlii-4-w18-833-2019

M3 - Conference article

VL - 42

SP - 833

EP - 842

JO - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 2194-9034

IS - 4/W18

T2 - ISPRS International GeoSpatial Conference 2019, Joint Conferences of 5th Sensors and Models in Photogrammetry and Remote Sensing (SMPR) and 3rd Geospatial Information Research (GI Research)

Y2 - 12 October 2019 through 14 October 2019

ER -

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