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A novel deep unsupervised learning-based framework for optimization of truss structures

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Abstract

In this paper, an efficient deep unsupervised learning (DUL)-based framework is proposed to directly perform the design optimization of truss structures under multiple constraints for the first time. Herein, the members’ cross-sectional areas are parameterized using a deep neural network (DNN) with the middle spatial coordinates of truss elements as input data. The parameters of the network, including weights and biases, are regarded as decision variables of the structural optimization problem, instead of the member’s cross-sectional areas as those of traditional optimization algorithms. A new loss function of the network model is constructed with the aim of minimizing the total structure weight so that all constraints of the optimization problem via unsupervised learning are satisfied. To achieve the optimal parameters, the proposed model is trained to minimize the loss function by a combination of the standard gradient optimizer and backpropagation algorithm. As soon as the learning process ends, the optimum weight of truss structures is indicated without utilizing any other time-consuming metaheuristic algorithms. Several illustrative examples are investigated to demonstrate the efficiency of the proposed framework in requiring much lower computational cost against other conventional methods, yet still providing high-quality optimal solutions.

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Acknowledgements

This research was supported by a grant (NRF-2021R1A4A2002855) from NRF (National Research Foundation of Korea) funded by MEST (Ministry of Education and Science Technology) of Korean government.

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Authors and Affiliations

  1. Deep Learning Architectural Research Center, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006, Republic of Korea

    Hau T. Mai & Jaehong Lee

  2. Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, 12 Nguyen Van Bao Street, Ward 4, Go Vap District, Ho Chi Minh City, 70000, Vietnam

    Hau T. Mai

  3. Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, Ward 14, District 10, Ho Chi Minh City, Vietnam

    Qui X. Lieu

  4. Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Qui X. Lieu

  5. School of Architecture, Yeungnam University, 280, Daehak-Ro, Gyeongsan, Gyeongbuk, 38541, Republic of Korea

    Joowon Kang

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  1. Hau T. Mai
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Contributions

Hau T. Mai: conceptualization, methodology, software, formal analysis, investigation, writing - original draft, writing—review and editing, visualization. Qui X. Lieu: methodology, writing—original draft, writing—review and editing. Joowon Kang: data curation, validation, resources. Jaehong Lee: conceptualization, methodology, supervision, funding acquisition.

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Correspondence to Jaehong Lee.

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Mai, H.T., Lieu, Q.X., Kang, J. et al. A novel deep unsupervised learning-based framework for optimization of truss structures. Engineering with Computers 39, 2585–2608 (2023). https://doi.org/10.1007/s00366-022-01636-3

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