Novel hybridized adaptive neuro-fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Ben Seghier, ME (Author) Other; Carvalho, H (Author) Other; Keshtegar, B (Author) Other; Corria, JAFO (Author) FEUP; Berto, F (Author) Other

Journal

Title: Fatigue and Fracture of Engineering Materials and Structures

Vol. 43 No. 2

Pages: 2653-2667

ISSN: 8756-758X

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-00S-KCE

DOI: 10.1111/ffe.13325

Abstract (EN): The aim of this study is to develop a new framework for the prediction of stress intensity factor (SIF) using newly developed hybrid artificial intelligence (AI) models. To do so, an adaptive neuro-fuzzy inference system optimized by two meta-heuristic algorithms as genetic algorithm (ANFIS-GA) and particle swarm optimization (ANFIS-PSO) is proposed. Moreover, a database composed of 150 SIF values obtained using the finite element method (FEM) calculations is used for training and validating the two proposed AI models. The efficiency and accuracy of the proposed AI models were investigated through several assessment criteria. Results showed the outperformance of the ANFIS-PSO model for accurate prediction of SIF values withR(2)=0.9913, root mean square error (RMSE) = 23.6 and mean absolute error (MAE) = 18.07, whereas both AI models indicate a robust performance in the presence of input variability. Overall, the performed study provides a hybrid AI framework that can serve as an efficient numerical tool for SIF prediction and analysis.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15