TY - GEN
T1 - Automated long-term damping estimation of the cable-stayed bridge using faulty data in wireless sensor network
AU - Kim, S.
AU - Spencer, B. F.
AU - Kim, H. K.
N1 - Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2017R1A2B4008973) through the Integrated Research Institute of Construction and Environmental Engineering at Seoul National University, and also was supported by grants (19SCIP-B119963-04) from the Ministry of Land, Infrastructure and Transport of the Korean Government through the Korea Bridge Design and Engineering Research Center (KBRC) at Seoul National University. The authors also are thankful to Prof. Jian Li, Associate Professor, University of Kansas, for sharing operational monitoring data.
Publisher Copyright:
© 2021 Taylor & Francis Group, London
PY - 2021
Y1 - 2021
N2 - Operational Modal Analysis (OMA)-based damping estimation of large-scale structures is challenging issues due to its large error. Meanwhile, the rapid development of wireless sensor networks (WSN) has enabled dense instrumentation in civil infrastructure, and this dense deployment of WSN allows a more stable modal analysis. Nevertheless, WSN still exhibits difficulties in damping identification owing to the existence of the faulty data resulted from its vulnerability to harsh environments than wired monitoring systems. This study focuses on the enhanced damping estimation by the newly proposed fault-data management combined with an automated OMA algorithm. The Support Vector Machine is utilized for automated detecting, recovering, and isolating faulty data in WSN. A new feature called Maximum Correlation Factor (MCF) is proposed to measure the similarity between simultaneously measured data sets. The three features of kurtosis, Dispersion Rate, and MCF are utilized in combination for the training and validation process with two different kernel functions. A fully automated OMA algorithm of Covariance driven Stochastic Subspace Identification is utilized to estimate a damping ratio from the output-only measurement on operational condition with three-stage validations for eliminating spurious poles. The accuracy and reliability of the proposed damping estimation algorithm are demonstrated by a 9-DOF numerical simulation and to the 1-year WSN monitoring data collected from the Jindo Bridge.
AB - Operational Modal Analysis (OMA)-based damping estimation of large-scale structures is challenging issues due to its large error. Meanwhile, the rapid development of wireless sensor networks (WSN) has enabled dense instrumentation in civil infrastructure, and this dense deployment of WSN allows a more stable modal analysis. Nevertheless, WSN still exhibits difficulties in damping identification owing to the existence of the faulty data resulted from its vulnerability to harsh environments than wired monitoring systems. This study focuses on the enhanced damping estimation by the newly proposed fault-data management combined with an automated OMA algorithm. The Support Vector Machine is utilized for automated detecting, recovering, and isolating faulty data in WSN. A new feature called Maximum Correlation Factor (MCF) is proposed to measure the similarity between simultaneously measured data sets. The three features of kurtosis, Dispersion Rate, and MCF are utilized in combination for the training and validation process with two different kernel functions. A fully automated OMA algorithm of Covariance driven Stochastic Subspace Identification is utilized to estimate a damping ratio from the output-only measurement on operational condition with three-stage validations for eliminating spurious poles. The accuracy and reliability of the proposed damping estimation algorithm are demonstrated by a 9-DOF numerical simulation and to the 1-year WSN monitoring data collected from the Jindo Bridge.
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U2 - 10.1201/9780429279119-414
DO - 10.1201/9780429279119-414
M3 - Conference contribution
AN - SCOPUS:85117564688
SN - 9780367232788
T3 - Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020
SP - 3049
EP - 3053
BT - Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020
A2 - Yokota, Hiroshi
A2 - Frangopol, Dan M.
PB - CRC Press/Balkema
T2 - 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020
Y2 - 11 April 2021 through 15 April 2021
ER -