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 (DBS...
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doaj-6386230384aa4c0d9d52a1a54bf48fd32020-11-24T21:16:00ZengMDPI AGSensors1424-82202019-02-0119492710.3390/s19040927s19040927Automated Modal Analysis for Tracking Structural Change during Construction and Operation PhasesJun Teng0De-Hui Tang1Xiao Zhang2Wei-Hua Hu3Samir Said4Rolf. G. Rohrmann5School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, ChinaSchool of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, ChinaSchool of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, ChinaSchool of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, ChinaFederal Institute for Materials Research and Testing (BAM), 12205 Berlin, GermanyStruktur Analyse & Bauwerks Monitoring (SABM) GbR, 10965 Berlin, GermanyThe 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.https://www.mdpi.com/1424-8220/19/4/927automated modal analysis (AMA)system model orderdensity-based spatial clustering of applications with noise (DBSCAN)continuous dynamic monitoringtemperature effect |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jun Teng De-Hui Tang Xiao Zhang Wei-Hua Hu Samir Said Rolf. G. Rohrmann |
spellingShingle |
Jun Teng De-Hui Tang Xiao Zhang Wei-Hua Hu Samir Said Rolf. G. Rohrmann Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases Sensors automated modal analysis (AMA) system model order density-based spatial clustering of applications with noise (DBSCAN) continuous dynamic monitoring temperature effect |
author_facet |
Jun Teng De-Hui Tang Xiao Zhang Wei-Hua Hu Samir Said Rolf. G. Rohrmann |
author_sort |
Jun Teng |
title |
Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases |
title_short |
Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases |
title_full |
Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases |
title_fullStr |
Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases |
title_full_unstemmed |
Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases |
title_sort |
automated modal analysis for tracking structural change during construction and operation phases |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-02-01 |
description |
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. |
topic |
automated modal analysis (AMA) system model order density-based spatial clustering of applications with noise (DBSCAN) continuous dynamic monitoring temperature effect |
url |
https://www.mdpi.com/1424-8220/19/4/927 |
work_keys_str_mv |
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1716743763073695744 |