PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Finite Element Analysis of Cold-Formed Steel Stud Wall Subjected to Blast Loading and Validation Using Artificial Neural Network and Response Surface Methodology
Version 1
: Received: 19 September 2023 / Approved: 19 September 2023 / Online: 20 September 2023 (04:44:01 CEST)
How to cite:
S.A., V.S.; Nambiappan, U. Finite Element Analysis of Cold-Formed Steel Stud Wall Subjected to Blast Loading and Validation Using Artificial Neural Network and Response Surface Methodology. Preprints2023, 2023091310. https://doi.org/10.20944/preprints202309.1310.v1
S.A., V.S.; Nambiappan, U. Finite Element Analysis of Cold-Formed Steel Stud Wall Subjected to Blast Loading and Validation Using Artificial Neural Network and Response Surface Methodology. Preprints 2023, 2023091310. https://doi.org/10.20944/preprints202309.1310.v1
S.A., V.S.; Nambiappan, U. Finite Element Analysis of Cold-Formed Steel Stud Wall Subjected to Blast Loading and Validation Using Artificial Neural Network and Response Surface Methodology. Preprints2023, 2023091310. https://doi.org/10.20944/preprints202309.1310.v1
APA Style
S.A., V.S., & Nambiappan, U. (2023). Finite Element Analysis of Cold-Formed Steel Stud Wall Subjected to Blast Loading and Validation Using Artificial Neural Network and Response Surface Methodology. Preprints. https://doi.org/10.20944/preprints202309.1310.v1
Chicago/Turabian Style
S.A., V.S. and Umamaheswari Nambiappan. 2023 "Finite Element Analysis of Cold-Formed Steel Stud Wall Subjected to Blast Loading and Validation Using Artificial Neural Network and Response Surface Methodology" Preprints. https://doi.org/10.20944/preprints202309.1310.v1
Abstract
This paper focused on the finite element analysis of structural system in extreme loading condition. Two different stud shape and thicknesses were analyzed under blast. The stud thickness such as 1.19 mm and 1.5 mm were modelled and analyzed using ABAQUS 6.14. FEM is a tool which predicts the engineering physics of the real structure. To validate the finite element modelling performed by authors, a reference work published by earlier researchers on cold-formed steel stud wall is considered and examined in the present study. The novelty of this study was web corrugation and influence of flange width on stud. The models mimic like an air bag in a car to delay the pressure timing inside the stud wall. The mass of explosive used as 1.56 kg at a standard scaled distance. Time versus displacement was captured out at four locations in the stud wall. One of the objectives is to develop mathematical model to validate the deformation of stud under blast loading. Two mathematical models were validated using Artificial Neural Network (ANN). The results captured in ANN model was error histogram, regression plot, best performance fit and training data. The models were capable of resisting the moderate blast load. The response surface methodology (RSM) was employed to evaluate model performance Regression equations are useful for predicting future trends and outcomes, which is crucial for planning and decision-making. The primary goal of this work is to evaluate cold-formed steel stud walls with varying stud sizes under blast loading using finite element analysis and validated by ANN and RSM.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
