Abstract
The prediction of failure mode of columns is critical in deciding the operational and recovery strategies of a bridge after a seismic event. This paper contributes to the critical need of failure mode prediction for circular reinforced concrete bridge columns by exploring the capabilities of machine learning methods. Three types of failure mode such as flexure, flexure-shear, and shear are considered in this study, and 311 specimens are compiled from experimental studies on the circular columns. The efficiency of various machine learning models such as quadratic discriminant analysis, -nearest neighbors, decision trees, random forests, naïve Bayes, and artificial neural network is evaluated using a randomly assigned test set from the collected data. It is noted that artificial neural network has superior performance amongst all the machine-learning methods, and the comparison of this classification with the existing methods underscores the advantage of the artificial neural network in failure mode recognition. Classification based on artificial neural network is 91% accurate in identifying the failure mode of the collected experimental data.
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Acknowledgments
This research was supported by the Basic Research Program in Science and Engineering through the National Research Foundation of Korea funded by the Ministry of Education (NRF-2016R1D1A1B03933842).
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Published In
Journal of Structural Engineering
Volume 145 • Issue 10 • October 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Mar 19, 2018
Accepted: Mar 4, 2019
Published online: Aug 6, 2019
Published in print: Oct 1, 2019
Discussion open until: Jan 6, 2020
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