Observational Analysis of Fire-Induced Spalling of Concrete through Ensemble Machine Learning and Surrogate Modeling
Publication: Journal of Materials in Civil Engineering
Volume 33, Issue 1
Abstract
Despite ongoing research efforts, we continue to fall short of arriving at a consistent representation of fire-induced spalling of concrete. This is often attributed to the complexity and randomness of spalling as well as our persistence in favoring traditional approaches as a sole mean to examine this phenomenon. With the hope of bridging this knowledge gap, this paper demonstrates how utilizing surrogate modeling via data science and machine learning algorithms can provide us with valuable insights into fire-induced spalling. In this study, nine algorithms, namely naive Bayes, generalized linear model, logistic regression, fast large margin, deep learning, decision tree, random forest, gradient boosted trees, and support vector machine, are applied to analyze observations obtained from 185 fire tests (collected over the last 65 years). The same algorithms were also applied to identify key features that govern the tendency of fire-induced spalling in reinforced concrete columns and to develop tools for instantaneous prediction of spalling. The results of this comprehensive analysis highlight the merit in utilizing modern computing techniques in structural fire engineering applications given their extraordinary ability to comprehend multidimensional phenomena with ease, high predictivity, and potential for continuous improvement.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The author would like to thank the support and constructive comments of the editor and reviewers.
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Published In
Journal of Materials in Civil Engineering
Volume 33 • Issue 1 • January 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Apr 21, 2020
Accepted: Jun 30, 2020
Published online: Oct 27, 2020
Published in print: Jan 1, 2021
Discussion open until: Mar 27, 2021
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