Artificial neural network model for strength predictions of CFST columns strengthened with CFRP. (15th April 2023)

• Record Type: Journal Article
• Authors: Zarringol, Mohammadreza; Patel, Vipulkumar Ishvarbhai; Liang, Qing Quan
• Is Part Of: Engineering structures. Volume 281(2023)
• Publication Date: 2023-04-15
• Subjects: Neural networks -- CFRP strengthened CFST columns -- Reliability analysis -- Graphical user interface; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2023.115784
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A numerical database of 450 CFST columns is developed using ABAQUS. The results of 531 CFST columns are used to train and test the optimised ANN model. Predictive equations and Graphical User Interface are developed for CFST columns. Strength reduction factors are proposed using Monte Carlo simulation. The proposed ANN model and equations predict well the strengths of CFST columns. Abstract: This paper presents an optimised Artificial Neural Network (ANN) model for predicting the ultimate axial strengths of concentrically loaded Concrete-Filled Steel Tubular (CFST) short and slender columns strengthened with Carbon Fibre-Reinforced Polymer (CFRP). Since experimental data on CFRP strengthened CFST columns is limited, an accurate Finite Element (FE) model is developed and used to provide additional numerical data. A multi-layered feed-forward back-propagation network is proposed, optimised, and trained using the results of 76 experimental tests and 450 generated FE models. The accuracy of the ANN model is assessed through comparing its computed results with experimental data. A reliability analysis is performed using Monte Carlo Simulation (MCS) to evaluate the safety of the solutions computed by the ANN model. ANN-based equations and Graphical User Interface (GUI) are developed based on the trained ANN model for the determination of the ultimate axial strengths of CFST columns. The results show that the developed ANN model is capable of accurately predicting the ultimate axial strengths of CFRP strengthened CFST columns with a high degree of accuracy.