en Applications Applications پژوهشي Research <span style="font-size:11.5pt"><span new="" roman="" style="font-family:" times=""><span lang="EN-AU" style="font-size:12.0pt"><span style="layout-grid-mode:both">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&#39;s performance and accuracy are satisfactory.</span></span></span></span><br> &nbsp; Optimization, Metaheuristic Algorithms, Plastic Limit Analysis, Artificial Neural Networks, Machine Learning, Finite Element Method, Nonlinear Structural Analysis. 143 154 http://ijoce.iust.ac.ir/browse.php?a_code=A-10-66-367&slc_lang=en&sid=1 A. Kaveh alikaveh@iust.ac.ir 180031947532846002581 180031947532846002581 Yes School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran M. R. Seddighian 180031947532846002582 180031947532846002582 No Center of Excellence in Structures and Earthquake Engineering, Department of Civil Engineering, Sharif University of Technology, Tehran, Iran N. Farsi 180031947532846002583 180031947532846002583 No School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran