"Effective Approach for Truss Optimization Using Deep Learning" by Mehriniso Zokhirova

Acta of Turin Polytechnic University in Tashkent

Article Title

Effective Approach for Truss Optimization Using Deep Learning Based on Multi – Classification

Authors

Mehriniso Zokhirova, Turin Polytechnic University in Tashkent

Abstract

Since deep learning is providing an effective way to train systems and gaining high performance, it is being transforming action in various fields. This study defines the deep learning approach for structural analysis and its predictions for exploring optimum design variables. The basic originality of this work is training dataset and prediction of design variables in the case of two types of truss structures. In order to train the neural network, various numbers of sets and classes are created for 10 bar and 25 bar trusses. In this case, implementation of this experiment involves two manners: regression and multi-classification. By these two manners, the power of deep learning for truss optimization is determined without using any structural analyzers and algorithms.

First Page

132

Last Page

134

References

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Seunghye Lee, Jingwan Ha, Mehriniso Zokhirova, Hyeonjoon Moon, and Jaehong Lee. Background Information of Deep Learning for Structural Engineering. Archives of Computational Methods in Engineering, January 2018.
A. Kaveh, V. Kalatjari. Size, geometry optimization of trusses by the force method and genetic algorithm. International Journal for Numerical Methods in Engineering, October, 2003.

Recommended Citation

Zokhirova, Mehriniso (2019) "Effective Approach for Truss Optimization Using Deep Learning Based on Multi – Classification," Acta of Turin Polytechnic University in Tashkent: Vol. 9 : Iss. 2 , Article 10.
Available at: https://uzjournals.edu.uz/actattpu/vol9/iss2/10

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