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H. Chung, H.-G. Kwak, "Hardware Accelerated Python Based Finite Element Analysis of Reinforced Concrete Member", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 9, Paper 14.1, 2024, doi:10.4203/ccc.9.14.1

Keywords: hardware acceleration, finite element analysis, Gaussian process, reinforced concrete, parallel processing, high performance computing.

Abstract

This paper presents a nonlinear finite element analysis (FEA) of RC beams by adopting material models based on machine learning (ML). The Gaussian Process Regression (GPR) approach is considered for constructing concrete and steel material models. However, a GPR material model has an increased computational load, so it is difficult to use in the nonlinear analysis of RC structures composed of numerous members. To solve this limitation, GPU acceleration is based on the constitution of the parallelized computing structure. GPU-accelerated Python-based FEA program is developed to trace the nonlinear behaviour of RC beams. The FEA and experimental data for two representative RC beams are compared. The results obtained from the developed program confirm that the solution procedure using GPU acceleration with GPR material models can effectively be used in the nonlinear analysis of large RC structures with nonlinear behaviours.

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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: 9 PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 14.1 Hardware Accelerated Python Based Finite Element Analysis of Reinforced Concrete Member H. Chung and H.-G. Kwak Department of Civil and Environmental Engineering, KAIST, Daejeon, Republic of Korea doi:10.4203/ccc.9.14.1 Full Bibliographic Reference for this paper H. Chung, H.-G. Kwak, "Hardware Accelerated Python Based Finite Element Analysis of Reinforced Concrete Member", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 9, Paper 14.1, 2024, doi:10.4203/ccc.9.14.1 Keywords: hardware acceleration, finite element analysis, Gaussian process, reinforced concrete, parallel processing, high performance computing. Abstract This paper presents a nonlinear finite element analysis (FEA) of RC beams by adopting material models based on machine learning (ML). The Gaussian Process Regression (GPR) approach is considered for constructing concrete and steel material models. However, a GPR material model has an increased computational load, so it is difficult to use in the nonlinear analysis of RC structures composed of numerous members. To solve this limitation, GPU acceleration is based on the constitution of the parallelized computing structure. GPU-accelerated Python-based FEA program is developed to trace the nonlinear behaviour of RC beams. The FEA and experimental data for two representative RC beams are compared. The results obtained from the developed program confirm that the solution procedure using GPU acceleration with GPR material models can effectively be used in the nonlinear analysis of large RC structures with nonlinear behaviours. download the full-text of this paper (PDF, 13 pages, 1092 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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