Title: Predicting the Response of Laminated Composite Beams: A Comparison of Machine Learning Algorithms
Author(s): Tsiatas GC, Kotsiantis SB, Charalampakis AE.
Journal: Frontiers in Built Environment
Publisher: Frontiers
Volume: 8
Issue: -
Paper: -
Date: 2022
DOI: 10.3389/fbuil.2022.855112
Language: English
[abstract]
A comparative study of machine learning regression algorithms for predicting the deflection of laminated composite beams is presented herein. The problem of the scarcity of experimental data is solved by ample numerically prepared data, which are necessary for the training, validation, and testing of the algorithms. To this end, the pertinent geometric and material properties of the beam are discretized appropriately, and a refined higher-order beam theory is employed for the accurate evaluation of the deflection in each case. The results indicate that the Extra-Trees algorithm performs best, demonstrating excellent predictive capabilities. A computational tool implementing this algorithm is provided as supplementary material to this article.
[cite : 
Endnote, BibTex, Reference Manager, or manually as : Tsiatas GC, Kotsiantis SB, Charalampakis AE. Predicting the Response of Laminated Composite Beams: A Comparison of Machine Learning Algorithms. Frontiers in Built Environment, 8 (2022), doi:10.3389/fbuil.2022.855112.]
