Abstract
A methodology of data-driven damage state quantification with a probability estimation for structural hysteresis of RC columns is presented in this paper. The knowledge learned from a large-volume structural behavior database is fully considered for developing monitoring-oriented damage indicators, with the established relationship between data-driven damage prediction and physics-based damage state evaluation. In the present study, a database of the hysteresis behavior of 1015 RC columns with different design parameters is first generated by adopting OpenSees, with categorization according to the primary design parameter of the axial load ratio. Four limit states of seismic performance with the corresponding values of the proposed damage index are calculated to generate an informative mapping between critical damage states and damage index values. By fitting probabilistic models on the grouped data of damage indices, the exceeding probabilities of damage states corresponding to damage index values can be obtained. Illustrative examples of full-scale RC columns with cyclic loading and shaking table tests are adopted to illustrate the proposed performance-based damage evaluation process.
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Acknowledgements
This study is sponsored by the National Natural Science Foundation of China (Grant No: 51878483), the Shanghai Rising-Star program (Grant No. 20QC1400700), the Key Laboratory of Shock and Vibration of Engineering Materials and Structures, Sichuan Province (No. 19kfgk03), the Shanghai Qi Zhi Institute (Grant No. SYXF0120020109), the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100), and the Peak Discipline Construction Project of Shanghai (No. 2021-CE-03).
Funding
The National Natural Science Foundation of China (Grant No: 51878483); the Shanghai Rising-Star program (Grant No. 20QC1400700); the Key Laboratory of Shock and Vibration of Engineering Materials and Structures; Sichuan Province (No. 19kfgk03); the Shanghai Qi Zhi Institute (Grant No. SYXF0120020109); the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100); the Peak Discipline Construction Project of Shanghai (No. 2021-CE-03).
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Shan, J., Gong, Y., Liu, J. et al. Damage tracking and evaluation of RC columns with structural performances by using seismic monitoring data. Bull Earthquake Eng 20, 4561–4587 (2022). https://doi.org/10.1007/s10518-022-01396-x
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DOI: https://doi.org/10.1007/s10518-022-01396-x