Artificial neural networks for predicting ultimate strength of steel plates with a single circular opening under axial compression. (2nd November 2022)

• Record Type: Journal Article
• Authors: Hosseinpour, Parisa; Hosseinpour, Mahmoud; Sharifi, Yasser
• Is Part Of: Ships and offshore structures. Volume 17:Number 11(2022)
• Page Start: 2454
• Page End: 2469
• Publication Date: 2022-11-02
• Subjects: Perforated plate -- finite element modelling -- circular opening -- axial compressive loading -- artificial neural network; Ships -- Periodicals; Offshore structures -- Periodicals; Marine engineering -- Periodicals; Marine engineering -- Technological innovations -- Periodicals; Ocean engineering -- Periodicals; Ocean engineering -- Technological innovations -- Periodicals; 623.8
• DOI: 10.1080/17445302.2021.2000265
• Languages: English
• ISSNs: 1744-5302

Abstract

ABSTRACT: In the current paper, using finite element models (FEM), an extensive numerical study is performed on the behaviour of the steel plates with a circular hole in their centre subjected to compressive axial loading. For this purpose, 270 perforated steel plates were modelled and analysed using ABAQUS software. The effects of four main variables including plate length, hole diameter, plate thickness, and yield stress were discussed. Then, using the database provided by FEM, the artificial neural network (ANN) method was used to develop a predictive model to estimate the ultimate strength of steel plates with a circular hole in the centre. Finally, an ANN-based formula was proposed to predict the ultimate strength of perforated steel plates and its accuracy was compared with the formulations presented in previous studies. Based on the results, the proposed formula provided high accuracy and can be used as a reliable formula in practice.