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Reliability assessment of a reinforced concrete chimney under seismic and wind loading by using structural health monitoring

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2015
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Abstract (EN)

This study presents the reliability assessment of a 100.5 m tall reinforced concrete chimney at a glass factory under earthquake and wind loading by using structural health monitoring (SHM) tools. Ambient vibration measurements were taken and recorded in order to identify modal parameters of the chimney. Modal frequencies and shapes were vibration-based identified by using Frequency Domain Decomposition (FDD) method. In addition, damping ratio of the chimney was identified by carrying out random decrement signature technique (RDST). FEM of the chimney was built up based on design drawings and then updated by using identified modal parameters. Updated and non-updated models were considered for reliability assessment of the chimney under wind and earthquake loadings. As for earthquake loading, probabilistic seismic hazard analysis (PSHA) was carried out to determine earthquake demand distribution of the chimney under an earthquake scenario. Demand distribution of the chimney was developed by performing linear time history analyses under selected earthquake records resulting from PSHA results. On the other hand, multivariate stochastic wind fields were generated along the height of chimney. Demand distribution of the chimney was developed under repetitive seeds. Capacity distribution of the chimney's base section was also developed by using Monte Carlo simulation for both earthquake and wind loading. Eventually, capacity and demand distribution of the system were obtained both for updated and non-updated cases under two types of loading. The reliability estimation was performed for non-updated FEM with 5% damping ratio and updated model with identified damping ratio by using RDST.

Author

Mehmet Orçun Tokuç

How to Cite

Mehmet Orçun Tokuç (Doctorate thesis). Reliability assessment of a reinforced concrete chimney under seismic and wind loading by using structural health monitoring, 2015, Boğaziçi University.

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