Probabilistic Seismic Capacity Model of Pier Columns: A Semiparametric Regression Approach
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9, Issue 3
Abstract
Piers are usually the most vulnerable components in a bridge structure and generally undergo excessive deformation, which will lead to damage and even whole structural collapse. This paper investigates the probabilistic seismic deformation capacities of reinforced concrete piers under different limit states for two engineering demand parameters, i.e., the drift ratio and displacement ductility. Based on sample data from the UW-PEER database, a penalized generalized additive model is used for predictor variable selections and to determine whether the mechanism of each predictor on the seismic capacity is linear or nonlinear. The influence of a predictor that illustrated a nonlinear pattern is modeled by a Gaussian process, and Bayesian semiparametric regression is conducted in the R environment to obtain posteriori estimations of the capacity measures. The results indicate that the ratios of the model predictions to the experimental observations are all around 1.0, which proves the unbiasedness of the models. Compared with previous seismic capacity models, the prediction of seismic capacity measures shows higher accuracy, lower dispersion, and better portrayal of uncertainties. The proposed model based on Bayesian semiparametric regression provides a performance improvement in the seismic capacity evaluation of the bridge structures, which can be used for the subsequent bridge seismic fragility and risk assessment.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors appreciate the financial support from Natural Science Foundation of Fujian Province (2020J01478).
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Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9 • Issue 3 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Dec 20, 2022
Accepted: Mar 18, 2023
Published online: Jun 7, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 7, 2023
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Earthquake engineering
- Engineering fundamentals
- Geotechnical engineering
- Hydraulic engineering
- Hydraulic structures
- Mathematics
- Models (by type)
- Parameters (statistics)
- Piers
- Ports and harbors
- Probability
- Regression analysis
- Seismic effects
- Seismic tests
- Statistical analysis (by type)
- Statistics
- Structural models
- Tests (by type)
- Water and water resources
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Metrics & Citations
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Cited by
- Libo Chen, Ruchun Mo, Yu Chen, Zhan Guo, Zhenfeng Zheng, Investigation of Intraclass Correlation of Seismic Capacity for RC Bridge Piers Based on Hierarchical Model, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10.1061/AJRUA6.RUENG-1253, 10, 3, (2024).