Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement

This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacing...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ehsan Taheri, Peyman Mehrabi, Shervin Rafiei, Bijan Samali
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence finite element method cold-formed rack upright feature-selection method multi-layer perceptron Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/10431878d66c456d9b38580340031a5b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:10431878d66c456d9b38580340031a5b
record_format	dspace
spelling	oai:doaj.org-article:10431878d66c456d9b38580340031a5b2021-11-25T16:43:27ZNumerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement10.3390/app1122110562076-3417https://doaj.org/article/10431878d66c456d9b38580340031a5b2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11056https://doaj.org/toc/2076-3417This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The finite element method (FEM) was employed to increase the available datasets and evaluate the proposed reinforcement method in different geometrical types of sections. The most influential factors on the axial strength were investigated using a feature-selection (FS) method within a multi-layer perceptron (MLP) algorithm. The MLP algorithm was developed by particle swarm optimization (PSO) and FEM results as input. In terms of accuracy evaluation, some of the rolling criteria including results showed that geometrical parameters have almost the same contribution in compression capacity and displacement of the specimens. According to the performance evaluation indexes, the best model was detected and specified in the paper and optimised by tuning other parameters of the algorithm. As a result, the normalised ultimate load and displacement were predicted successfully.Ehsan TaheriPeyman MehrabiShervin RafieiBijan SamaliMDPI AGarticleartificial intelligencefinite element methodcold-formedrack uprightfeature-selection methodmulti-layer perceptronTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11056, p 11056 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence finite element method cold-formed rack upright feature-selection method multi-layer perceptron Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	artificial intelligence finite element method cold-formed rack upright feature-selection method multi-layer perceptron Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Ehsan Taheri Peyman Mehrabi Shervin Rafiei Bijan Samali Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement
description	This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The finite element method (FEM) was employed to increase the available datasets and evaluate the proposed reinforcement method in different geometrical types of sections. The most influential factors on the axial strength were investigated using a feature-selection (FS) method within a multi-layer perceptron (MLP) algorithm. The MLP algorithm was developed by particle swarm optimization (PSO) and FEM results as input. In terms of accuracy evaluation, some of the rolling criteria including results showed that geometrical parameters have almost the same contribution in compression capacity and displacement of the specimens. According to the performance evaluation indexes, the best model was detected and specified in the paper and optimised by tuning other parameters of the algorithm. As a result, the normalised ultimate load and displacement were predicted successfully.
format	article
author	Ehsan Taheri Peyman Mehrabi Shervin Rafiei Bijan Samali
author_facet	Ehsan Taheri Peyman Mehrabi Shervin Rafiei Bijan Samali
author_sort	Ehsan Taheri
title	Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement
title_short	Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement
title_full	Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement
title_fullStr	Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement
title_full_unstemmed	Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement
title_sort	numerical evaluation of the upright columns with partial reinforcement along with the utilisation of neural networks with combining feature-selection method to predict the load and displacement
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/10431878d66c456d9b38580340031a5b
work_keys_str_mv	AT ehsantaheri numericalevaluationoftheuprightcolumnswithpartialreinforcementalongwiththeutilisationofneuralnetworkswithcombiningfeatureselectionmethodtopredicttheloadanddisplacement AT peymanmehrabi numericalevaluationoftheuprightcolumnswithpartialreinforcementalongwiththeutilisationofneuralnetworkswithcombiningfeatureselectionmethodtopredicttheloadanddisplacement AT shervinrafiei numericalevaluationoftheuprightcolumnswithpartialreinforcementalongwiththeutilisationofneuralnetworkswithcombiningfeatureselectionmethodtopredicttheloadanddisplacement AT bijansamali numericalevaluationoftheuprightcolumnswithpartialreinforcementalongwiththeutilisationofneuralnetworkswithcombiningfeatureselectionmethodtopredicttheloadanddisplacement
_version_	1718412999949025280

Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement

Ejemplares similares