Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 833-842 |
| Seitenumfang | 10 |
| Fachzeitschrift | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Jahrgang | 42 |
| Ausgabenummer | 4/W18 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 18 Okt. 2019 |
| Veranstaltung | 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) - Karaj, Iran Dauer: 12 Okt. 2019 → 14 Okt. 2019 |
Abstract
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
- Informatik (insg.)
- Information systems
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in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jahrgang 42, Nr. 4/W18, 18.10.2019, S. 833-842.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
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
Y1 - 2019/10/18
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.
AB - 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.
KW - Displacement and vibration analysis
KW - MEMS accelerometer
KW - Image-assisted total station
KW - Modal parameter identification
KW - Robust parameter estimation
KW - Kalman filter
KW - Bridge monitoring
UR - http://www.scopus.com/inward/record.url?scp=85083168026&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-xlii-4-w18-833-2019
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 -