Computational Technology Resources - CCC

W. Zhang^1,2, S.-K. Youn^1,2 and X. Guo^1,3

¹Department of Engineering Mechanics, Dalian University of Technology, Dalian, China
²Ningbo Institute of Dalian University of Technology, Ningbo, China
³Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

doi:10.4203/ccc.5.2.2

W. Zhang, S.-K. Youn, X. Guo, "Machine-learning assisted topology optimization with structural gene inheritance", in P. Iványi, J. Logo, B.H.V. Topping, (Editors), "Proceedings of the Sixth International Conference on Soft Computing, Machine Learning and Optimisation in Civil, Structural and Environmental Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 5, Paper 2.2, 2023, doi:10.4203/ccc.5.2.2

Keywords: topology optimization, structural gene, bio-inspired structure, machinelearning, neural style transfer, VGG-19 model.

Abstract

A machine-learning assisted topology optimization approach is proposed for structural design with structural gene inheritance. This work establishes a novel framework to systematically integrate structural topology optimization with subjective human design preferences. To embed the structural gene into the design, neural style transfer technique is adopted to measure and generate the prior knowledge from a reference image with the concerned structural gene (such as biological characteristic, artistic flavor and manufacturing requirement, etc.). By using different convolutional layers in the VGG-19 model-based CNN, both the style and content of the structural gene can be constructed from low to high levels of abstraction. The measured knowledge can then be integrated into pixel-based topology optimization as a formal similarity constraint. Both 2D and 3D problems are solved to illustrate the effectiveness of the proposed approach where the inheritance of the structural gene can be achieved in a systematic manner.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 5 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING, MACHINE LEARNING AND OPTIMISATION IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: P. Iványi, J. Logo and B.H.V. Topping Paper 2.2 Machine-learning assisted topology optimization with structural gene inheritance W. Zhang^1,2, S.-K. Youn^1,2 and X. Guo^1,3 ¹Department of Engineering Mechanics, Dalian University of Technology, Dalian, China ²Ningbo Institute of Dalian University of Technology, Ningbo, China ³Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea doi:10.4203/ccc.5.2.2 Full Bibliographic Reference for this paper W. Zhang, S.-K. Youn, X. Guo, "Machine-learning assisted topology optimization with structural gene inheritance", in P. Iványi, J. Logo, B.H.V. Topping, (Editors), "Proceedings of the Sixth International Conference on Soft Computing, Machine Learning and Optimisation in Civil, Structural and Environmental Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 5, Paper 2.2, 2023, doi:10.4203/ccc.5.2.2 Keywords: topology optimization, structural gene, bio-inspired structure, machinelearning, neural style transfer, VGG-19 model. Abstract A machine-learning assisted topology optimization approach is proposed for structural design with structural gene inheritance. This work establishes a novel framework to systematically integrate structural topology optimization with subjective human design preferences. To embed the structural gene into the design, neural style transfer technique is adopted to measure and generate the prior knowledge from a reference image with the concerned structural gene (such as biological characteristic, artistic flavor and manufacturing requirement, etc.). By using different convolutional layers in the VGG-19 model-based CNN, both the style and content of the structural gene can be constructed from low to high levels of abstraction. The measured knowledge can then be integrated into pixel-based topology optimization as a formal similarity constraint. Both 2D and 3D problems are solved to illustrate the effectiveness of the proposed approach where the inheritance of the structural gene can be achieved in a systematic manner. download the full-text of this paper (PDF, 10 pages, 410 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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