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Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods: Optimum Prediction Methods on Advance Ensemble Algorithms – Bagging Combinations

Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods: Optimum Prediction Methods on Advance Ensemble Algorithms – Bagging Combinations

Melda Yucel, Aylin Ece Kayabekir, Sinan Melih Nigdeli, Gebrail Bekdaş
Copyright: © 2022 |Pages: 19
ISBN13: 9781668462911|ISBN10: 1668462915|EISBN13: 9781668462928
DOI: 10.4018/978-1-6684-6291-1.ch018
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MLA

Yucel, Melda, et al. "Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods: Optimum Prediction Methods on Advance Ensemble Algorithms – Bagging Combinations." Research Anthology on Machine Learning Techniques, Methods, and Applications, edited by Information Resources Management Association, IGI Global, 2022, pp. 308-326. https://doi.org/10.4018/978-1-6684-6291-1.ch018

APA

Yucel, M., Kayabekir, A. E., Nigdeli, S. M., & Bekdaş, G. (2022). Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods: Optimum Prediction Methods on Advance Ensemble Algorithms – Bagging Combinations. In I. Management Association (Ed.), Research Anthology on Machine Learning Techniques, Methods, and Applications (pp. 308-326). IGI Global. https://doi.org/10.4018/978-1-6684-6291-1.ch018

Chicago

Yucel, Melda, et al. "Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods: Optimum Prediction Methods on Advance Ensemble Algorithms – Bagging Combinations." In Research Anthology on Machine Learning Techniques, Methods, and Applications, edited by Information Resources Management Association, 308-326. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6291-1.ch018

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Abstract

In this chapter, an application for demonstrating the predictive success and error performance of ensemble methods combined via various machine learning and artificial intelligence algorithms and techniques was performed. For this reason, two single methods were selected, and combination models with a Bagging ensemble were constructed and operated with the goal of optimally designing concrete beams covering with carbon-fiber-reinforced polymers (CFRP) by ensuring the determination of the design variables. The first part was an optimization problem and method composing an advanced bio-inspired metaheuristic called the Jaya algorithm. Machine learning prediction methods and their operation logics were detailed. Performance evaluations and error indicators were represented for the prediction models. In the last part, performed prediction applications and created models were introduced. Also, the obtained predictive success of the main model, as generated with optimization results, was utilized to determine the optimal predictions of the test models.

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