46812
2018
eng
4117, 1
28
12
18
article
MDPI
4052 Basel, Schweiz
1
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Structural health monitoring of a prestressed concrete bridge based on statistical pattern recognition of continuous dynamic measurements over 14 years
This work describes a vibration-based structural health monitoring of a prestressed-concrete box girder bridge on the A100 Highway in Berlin by applying statistical pattern recognition technique to a huge amount of data continuously collected by an integrated monitoring system during the period from 2000 to 2013. Firstly, the general condition and potential damage of the bridge is described. Then, the dynamic properties are extracted from 20 velocity sensors. Environmental variability captured by five thermal transducers and traffic intensity approximately estimated by strain measurements are also reported. Nonlinear influences of temperature on natural frequencies are observed. Subsequently, the measurements during the first year are used to build a baseline health index. The multiple linear regression (MLR) method is used to characterize the nonlinear relationship between natural frequencies and temperatures. The Euclidean distance of the residual errors is calculated to build a statistical health index. Finally, the indices extracted from the following years gradually deviate; which may indicate structural deterioration due to loss of prestress in the prestressed tendons.
sensors
10.3390/s18124117
urn:nbn:de:kobv:b43-468126
1424-8220
30.11.2018
Creative Commons - CC BY - Namensnennung 4.0 International
Wei-Hua Hu
De-Hui Tang
Jun Teng
Samir Said
Rolf Rohrmann
eng
uncontrolled
Bridge
deu
uncontrolled
Structural health monitoring
deu
uncontrolled
Statistical pattern recognition
deu
uncontrolled
Temperature effect
Ingenieurbau
7 Bauwerkssicherheit
7.2 Ingenieurbau
Infrastruktur
Verlagsliteratur
Datei für die Öffentlichkeit verfügbar ("Open Access")
Wissenschaftliche Artikel der BAM
Bundesanstalt für Materialforschung und -prüfung (BAM)
https://opus4.kobv.de/opus4-bam/files/46812/sensors-18-04117-v2 -1.pdf
47511
2019
eng
927, 1
23
4
19
article
MDPI AG
Basel, Switzerland
1
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Automated modal analysis for tracking structural change during construction and operation phases
The automated modal analysis (AMA) technique has attracted significant interest over the last few years, because it can track variations in modal parameters and has the potential to detect structural changes. In this paper, an improved density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean the abnormal poles in a stabilization diagram. Moreover, the optimal system model order is also discussed to obtain more stable poles. A numerical Simulation and a full-scale experiment of an arch bridge are carried out to validate the effectiveness of the proposed algorithm. Subsequently, the continuous dynamic monitoring system of the bridge and the proposed algorithm are implemented to track the structural changes during the construction phase. Finally, the artificial neural network (ANN) is used to remove the temperature effect on modal frequencies so that a health index can be constructed under operational conditions.
Sensors
10.3390/s19040927
https://www.mdpi.com/1424-8220/19/4/927/pdf
1424-8220
urn:nbn:de:kobv:b43-475116
11.03.2019
Creative Commons - CC BY - Namensnennung 4.0 International
Jun Teng
De-Hui Tang
Xiao Zhang
Wei-Hua Hu
Samir Said
Rolf G. Rohrmann
eng
uncontrolled
Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases
Ingenieurbau
7 Bauwerkssicherheit
7.2 Ingenieurbau
Infrastruktur
Verlagsliteratur
Datei für die Öffentlichkeit verfügbar ("Open Access")
Wissenschaftliche Artikel der BAM
Bundesanstalt für Materialforschung und -prüfung (BAM)
https://opus4.kobv.de/opus4-bam/files/47511/sensors-19-00927.pdf