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An Artificial Neural Network With a Metaheuristic Basis for Plastic Limit Frames Analysis
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Author(s): Selvapriya R. (Muthayammal Engineering College, India), Gopinath M. (Muthayammal Engineering College, India), Velmurugan D. (Muthayammal Engineering College, India)and Tamilchelvan P. (Muthayammal College of Engineering, India)
Copyright: 2024
Pages: 14
Source title:
Metaheuristics Algorithm and Optimization of Engineering and Complex Systems
Source Author(s)/Editor(s): Thanigaivelan R. (AKT Memorial College of Engineering and Technology, India), Suchithra M. (SRM Institute of Science and Technology, India), Kaliappan S. (KCG College of Technology, India)and Mothilal T. (KCG College of Technology, India)
DOI: 10.4018/979-8-3693-3314-3.ch011
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Abstract
The plastic limit analysis of structures has several benefits, but it also has certain disadvantages, such high computing costs. In the past twenty years, plastic limit analysis has performed better thanks to metaheuristic algorithms, particularly when it comes to structural issues. Graph theoretical techniques have also significantly reduced the process's processing time. But until recently, the iterative process and its proportional computer memory and time have proven difficult. In order to quickly ascertain the collapse load factors of two-dimensional frames, a metaheuristic-based artificial neural network (ANN), which falls under the category of supervised machine learning techniques, has been utilized in this work. The numerical examples show that the accuracy and performance of the suggested method are adequate.
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