Finite element analysis of cold-formed steel stud wall subjected to blast load and validated using artificial neural network combined with response surface method

Abstract

This paper focused on the Finite Element (FE) Modeling of structural systems in extreme loading conditions. Two different stud shapes and thicknesses were analyzed under blast. The stud thickness, such as 1.19 mm and 1.5 mm, were modeled and analyzed using ABAQUS 6.14. A tool that predicts the engineering physics of the real structure is Finite Element Method (FEM). The present research takes into consideration and examines a reference work produced by previous researchers on cold-formed steel (CFS) walls to validate the finite element modeling carried out by the authors. The novelty of this study was web corrugation and the influence of flange width on the stud. To delay the pressure timing inside the stud wall, the models imitate an airbag in a car. The mass of the explosive used is 1.56 kg at a standard scaled distance. Time versus displacement was captured at A1, A2, A3, and A4 in FE models. Reflected pressure and connection failure were studied. One of the goals is to create a mathematical model to substantiate the deformation of the stud after the blast. Two neural computing models were validated using Artificial Neural Network (ANN). The results captured in the ANN model were 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 stud walls with varying stud dimensions subjected to blast using FE Modeling and validated by ANN and RSM.