TY - GEN
T1 - A fully autonomous damping estimation with SVM-based fault data treatment using 1-year wireless monitoring data
AU - Kim, S.
AU - Kim, H. K.
AU - Spencer, B. F.
N1 - The first author 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. The authors are also thankful to Prof. Jian Li for cooperation to share the 1-year monitoring data.
PY - 2019
Y1 - 2019
N2 - This study reports on the estimated modal damping ratio of a parallel cable-stayed bridge by the use of automated Operational Modal Analysis (OMA). The 1-year monitoring data from a dense wireless smart sensor network (WSSN) of 113 smart sensors were utilized for damping estimation. A novel data treatment strategy for sensor fault in WSSN data was proposed to remove a static trend, recover the unexpected spikes, and exclude the fault measurements autonomously. The automated covariance driven Stochastic Subspace Identification (SSI-COV) is determined as the OMA algorithm. In order to achieve more reliable damping estimates, the three-stages of validations were implemented in SSI-COV for the purpose of eliminating spurious poles from physical poles. The improvement in the integrated damping estimation procedure was demonstrated by comparative results of OMA-based damping estimation of the Jindo Bridge, by using a raw and treated data. The effect of data length on the accuracy of damping estimates was evaluated statistically.
AB - This study reports on the estimated modal damping ratio of a parallel cable-stayed bridge by the use of automated Operational Modal Analysis (OMA). The 1-year monitoring data from a dense wireless smart sensor network (WSSN) of 113 smart sensors were utilized for damping estimation. A novel data treatment strategy for sensor fault in WSSN data was proposed to remove a static trend, recover the unexpected spikes, and exclude the fault measurements autonomously. The automated covariance driven Stochastic Subspace Identification (SSI-COV) is determined as the OMA algorithm. In order to achieve more reliable damping estimates, the three-stages of validations were implemented in SSI-COV for the purpose of eliminating spurious poles from physical poles. The improvement in the integrated damping estimation procedure was demonstrated by comparative results of OMA-based damping estimation of the Jindo Bridge, by using a raw and treated data. The effect of data length on the accuracy of damping estimates was evaluated statistically.
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M3 - Conference contribution
AN - SCOPUS:85091453732
T3 - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings
SP - 442
EP - 447
BT - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure
A2 - Chen, Genda
A2 - Alampalli, Sreenivas
PB - International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII
T2 - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019
Y2 - 4 August 2019 through 7 August 2019
ER -