Probabilistic Model Based on Bayesian Model Averaging for Predicting the Plastic Hinge Lengths of Reinforced Concrete Columns
Publication: Journal of Engineering Mechanics
Volume 147, Issue 10
Abstract
A probabilistic model is devised for predicting the plastic hinge lengths (PHLs) of RC columns. Seven existing parametric models are evaluated first using a comprehensive database comprising PHL measurements from 133 RC column tests. It is observed that the performances of these seven models are fair (as opposed to strong), and their predictions bear significant uncertainties. A novel technique is devised to combine them into a weighted-average supermodel wherein the weights are determined via Bayesian inference. This approach naturally produces the weights’ statistical moments, and thus, the resulting model is a probabilistic one that is amenable for performance-based seismic design and assessment analyses. Prediction comparisons indicate that the proposed supermodel has a higher performance than all prior models. The new model is easily expandable should more test data become available.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
The authors would like to acknowledge financial support from the National Natural Science Foundation of China (Grant No. 52078119), which enabled the first and second authors to spend terms as Visiting Scholars at UCLA.
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Published In
Journal of Engineering Mechanics
Volume 147 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jan 5, 2021
Accepted: Apr 26, 2021
Published online: Jul 26, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 26, 2021
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