Experimental and Numerical Investigation of an Innovative Method for Strengthening Cold-Formed Steel Profiles in Bending throughout Finite Element Modeling and Application of Neural Network Based on Feature Selection Method

This study evaluates an innovative reinforcement method for cold-formed steel (CFS) upright sections through finite element assessment as well as prediction of the normalized ultimate load and deflection of the profiles by artificial intelligence (AI) and machine learning (ML) techniques. Following...

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Veröffentlicht in:	Applied sciences 2021-06, Vol.11 (11), p.5242, Article 5242
Hauptverfasser:	Taheri, Ehsan, Esgandarzadeh Fard, Saeid, Zandi, Yousef, Samali, Bijan
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Artificial intelligence Chemistry Chemistry, Multidisciplinary Cold-formed steel Correlation coefficient Correlation coefficients Deflection Engineering Engineering, Multidisciplinary Feature selection feature selection method Finite element method Flexural strength Investigations Learning algorithms Machine learning Materials Science Materials Science, Multidisciplinary Methods Modulus of rupture in bending multi-layer perceptron Multilayers Neural networks Numerical analysis Optimization particle swarm optimization Performance evaluation Physical Sciences Physics Physics, Applied Regression analysis Reinforcement Root-mean-square errors Science & Technology Technology Ultimate loads upright
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