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Numerical and analytical investigations on cold-formed steel stud wall system using FEA and validation using ANN and RSM

Numerical and analytical investigations on cold-formed steel stud wall system using FEA and... Experimental investigations require special efforts to investigate the behavior of steel stud walls subjected to blast loading. Therefore, finite-element modelling (FEM) is employed in the present study for validation. A previous research work performed on cold-formed steel stud walls, conducted by earlier researchers, is considered as a reference to validate the FEM performed by the authors. The novelty of the present study lies influence of flange width of channel section under blasting. The explosive mass used in the experiments was 1.56 kg, and studs of different thickness are analyzed under blast loading. ABAQUS-6.14 was employed for modelling and analysis of the studs of varying thickness. Deformation is observed at four locations of the stud wall. One of the aims of the current study is to develop a mathematical model to validate the current results. Two mathematical models are validated using Artificial Neural Network (ANN). The regression values obtained for the recommended models indicate a match between the finite-element data set and the analyzed data set. The training values achieved a high regression value, demonstrating that the finite-element data set aligns with the analyzed data set by more than 99%. Response surface methodology (RSM) was used to assess the performance of models. Surface plots developed helps to find the influence of factors and hence the related displacement. The main objective of the present study to investigate cold-formed steel stud wall with different sizes of stud under blast loading by conducting finite-element analysis and ANN is performed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

Numerical and analytical investigations on cold-formed steel stud wall system using FEA and validation using ANN and RSM

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References (29)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1563-0854
eISSN
2522-011X
DOI
10.1007/s42107-023-00884-y
Publisher site
See Article on Publisher Site

Abstract

Experimental investigations require special efforts to investigate the behavior of steel stud walls subjected to blast loading. Therefore, finite-element modelling (FEM) is employed in the present study for validation. A previous research work performed on cold-formed steel stud walls, conducted by earlier researchers, is considered as a reference to validate the FEM performed by the authors. The novelty of the present study lies influence of flange width of channel section under blasting. The explosive mass used in the experiments was 1.56 kg, and studs of different thickness are analyzed under blast loading. ABAQUS-6.14 was employed for modelling and analysis of the studs of varying thickness. Deformation is observed at four locations of the stud wall. One of the aims of the current study is to develop a mathematical model to validate the current results. Two mathematical models are validated using Artificial Neural Network (ANN). The regression values obtained for the recommended models indicate a match between the finite-element data set and the analyzed data set. The training values achieved a high regression value, demonstrating that the finite-element data set aligns with the analyzed data set by more than 99%. Response surface methodology (RSM) was used to assess the performance of models. Surface plots developed helps to find the influence of factors and hence the related displacement. The main objective of the present study to investigate cold-formed steel stud wall with different sizes of stud under blast loading by conducting finite-element analysis and ANN is performed.

Journal

Asian Journal of Civil EngineeringSpringer Journals

Published: Feb 1, 2024

Keywords: Blast load; Stud wall; Energy absorption; Strain energy; Kinetic energy; Artificial neural network,; Response surface method

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