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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
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Author(s): Melda Yucel (Istanbul University-Cerrahpaşa, Turkey), Aylin Ece Kayabekir (Istanbul University-Cerrahpaşa, Turkey), Sinan Melih Nigdeli (Istanbul University-Cerrahpaşa, Turkey)and Gebrail Bekdaş (Istanbul University-Cerrahpaşa, Turkey)
Copyright: 2020
Pages: 19
Source title:
Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering
Source Author(s)/Editor(s): Gebrail Bekdaş (Istanbul University-Cerrahpaşa, Turkey), Sinan Melih Nigdeli (Istanbul University-Cerrahpaşa, Turkey)and Melda Yücel (Istanbul University-Cerrahpaşa, Turkey)
DOI: 10.4018/978-1-7998-0301-0.ch005
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Abstract
In this chapter, an application for demonstrating prediction 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 method was selected and combination models with Bagging ensemble was constructed and operated in the direction optimum design of concrete beams covering with carbon fiber reinforced polymers (CFRP) by ensuring the determination of design variables. The first part was optimization problem and method composing from an advanced bio-inspired metaheuristic called Jaya algorithm. Machine learning prediction methods and their operation logics were detailed. Performance evaluations and error indicators were represented for prediction models. In the last part, performed prediction applications and created models were introduced. Also, obtained prediction success for main model generated with optimization results was utilized to determine the optimum predictions about test models.
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