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.

[1]  J. Brian Gray,et al.  Introduction to Linear Regression Analysis , 2002, Technometrics.

[2]  Gangbing Song,et al.  Structural Health Monitoring (SHM) of Civil Structures , 2017 .

[3]  Wei-Xin Ren,et al.  Empirical formulas to estimate cable tension by cable fundamental frequency , 2005 .

[4]  Miroslav Pástor,et al.  Modal Assurance Criterion , 2012 .

[5]  Filipe Magalhães,et al.  Automated modal tracking in a football stadium suspension roof for detection of structural changes , 2017 .

[6]  Mehrisadat Makki Alamdari,et al.  Automated Operational Modal Analysis of a Cable-Stayed Bridge , 2017 .

[7]  Lei Liu,et al.  A MapReduce-based improvement algorithm for DBSCAN , 2018 .

[8]  Wei-Hua Hu,et al.  Structural Health Monitoring of a Prestressed Concrete Bridge Based on Statistical Pattern Recognition of Continuous Dynamic Measurements over 14 years , 2018, Sensors.

[9]  Álvaro Cunha,et al.  Structural health monitoring of a stress-ribbon footbridge , 2013 .

[10]  Patrick Guillaume,et al.  Structural health monitoring of offshore wind turbines using automated operational modal analysis , 2014 .

[11]  Huajun Li,et al.  Model order determination and noise removal for modal parameter estimation , 2010 .

[12]  Guido De Roeck,et al.  Fully automated (operational) modal analysis , 2012 .

[13]  Gethin Wyn Roberts,et al.  Separating damage from environmental effects affecting civil structures for near real-time damage detection , 2018 .

[14]  Hiroshi Zui,et al.  Practical Formulas for Estimation of Cable Tension by Vibration Method , 1996 .

[15]  Gangbing Song,et al.  Frequency Co-integration-based damage detection for bridges under the influence of environmental temperature variation , 2018, Measurement.

[16]  P. Guillaume,et al.  Operational modal analysis for estimating the dynamic properties of a stadium structure during a football game , 2007 .

[17]  Jammalamadaka.  Introduction to Linear Regression Analysis (3rd ed.) , 2003 .

[18]  Duc Truong Pham,et al.  Neural Networks for Identification, Prediction and Control , 1995 .

[19]  Raimondo Betti,et al.  On the selection of user-defined parameters in data-driven stochastic subspace identification , 2018 .

[20]  Zhuo Chen,et al.  Automated eigensystem realisation algorithm for operational modal analysis , 2014 .

[21]  Filipe Magalhães,et al.  Online automatic identification of the modal parameters of a long span arch bridge , 2009 .

[22]  Adrian C. Orifici,et al.  Fully Automated Operational Modal Analysis using multi-stage clustering , 2017 .

[23]  Carlo Rainieri,et al.  Influence of model order and number of block rows on accuracy and precision of modal parameter estimates in stochastic subspace identification , 2014 .

[24]  Sebastian Thöns,et al.  Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties , 2015 .

[25]  Gangbing Song,et al.  Damage identification of a large cable‐stayed bridge with novel cointegrated Kalman filter method under changing environments , 2018 .

[26]  Guido De Roeck,et al.  One-year monitoring of the Z24-Bridge : environmental effects versus damage events , 2001 .

[27]  I. S. Sitanggang,et al.  Determination of Optimal Epsilon (Eps) Value on DBSCAN Algorithm to Clustering Data on Peatland Hotspots in Sumatra , 2016 .

[28]  Filipe Magalhães,et al.  LabVIEW toolkits for output-only modal identification and long-term dynamic structural monitoring , 2010 .

[29]  Ivan Markovsky,et al.  Structured low-rank approximation and its applications , 2008, Autom..

[30]  Sebastian Thöns,et al.  Vibration-based structural health monitoring of a wind turbine system. Part I: Resonance phenomenon , 2015 .

[31]  Xijun Ye,et al.  An Integrated Machine Learning Algorithm for Separating the Long-Term Deflection Data of Prestressed Concrete Bridges , 2018, Sensors.

[32]  Maurice Goursat,et al.  Output-Only Subspace-Based Structural Identification: From Theory to Industrial Testing Practice , 2001 .