Volume 13, Issue 2 (4-2023)                   IJOCE 2023, 13(2): 143-154 | Back to browse issues page


XML Print


1- School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
2- Center of Excellence in Structures and Earthquake Engineering, Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
Abstract:   (11455 Views)
Despite the advantages of the plastic limit analysis of structures, this robust method suffers from some drawbacks such as intense computational cost. Through two recent decades, metaheuristic algorithms have improved the performance of plastic limit analysis, especially in structural problems. Additionally, graph theoretical algorithms have decreased the computational time of the process impressively. However, the iterative procedure and its relative computational memory and time have remained a challenge, up to now. In this paper, a metaheuristic-based artificial neural network (ANN), which is categorized as a supervised machine learning technique, has been employed to determine the collapse load factors of two-dimensional frames in an absolutely fast manner. The numerical examples indicate that the proposed method's performance and accuracy are satisfactory.
 
Full-Text [PDF 815 kb]   (3680 Downloads)    
Type of Study: Research | Subject: Applications
Received: 2022/12/29 | Accepted: 2023/01/11 | Published: 2023/01/11

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.