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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Bibliographic Details
Published in:	Applied sciences Vol. 11; no. 11; p. 5242
Main Authors:	Taheri, Ehsan, Esgandarzadeh Fard, Saeid, Zandi, Yousef, Samali, Bijan
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-06-2021
Subjects:	Accuracy Algorithms Artificial intelligence Cold-formed steel Correlation coefficient Correlation coefficients Deflection Feature selection feature selection method Finite element method Flexural strength Investigations Learning algorithms Machine learning Methods Modulus of rupture in bending multi-layer perceptron Multilayers Neural networks Numerical analysis Optimization particle swarm optimization Performance evaluation Regression analysis Reinforcement Root-mean-square errors Ultimate loads upright
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Summary:	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 the previous experimental studies, several CFS upright profiles with different lengths, thicknesses and reinforcement spacings are modeled and analyzed under flexural loading. The finite element method (FEM) is employed to evaluate the proposed reinforcement method in different upright sections and to provide a valid database for the analytical study. To detect the most influential factor on flexural strength, the “feature selection” method is performed on the FEM results. Then, by using the feature selection method, a hybrid neural network (a combination of multi-layer perceptron algorithm and particle swarm optimization method) is developed for the prediction of normalized ultimate load. The correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE) and Wilmot’s index of agreement (WI) are used as the measure of precision. The results show that the geometrical parameters have almost the same contribution in the flexural capacity and deflection of the specimens. According to the performance evaluation indexes, the best model is detected and optimized by tuning other algorithm parameters. The results indicate that the hybrid neural network can successfully predict the normalized ultimate load and deflection.
ISSN:	2076-3417 2076-3417
DOI:	10.3390/app11115242