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P. Lacki, A. Derlatka, J. Niemiro-Maźniak, M. Lacki, "Digital Twin of the Reinforced Concrete Slab Based on the Artificial Neural Network", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Seventh International Conference on Artificial Intelligence, Soft Computing, Machine Learning and Optimization in Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 11, Paper 1.1, 2025, doi:10.4203/ccc.11.1.1

Keywords: digital twin, artificial neural network, finite element method, reinforced concrete, genetic algorithm, slab.

Abstract

The article presents a Digital Twin of the reinforced concrete slab to analyse the load-bearing capacity using artificial intelligence algorithms. To achieve this goal, the authors developed three-dimensional parametric numerical model of slab using the finite element method (FEM). When design the numerical model of reinforced concrete members, it is particularly important to select the concrete material model, the type of finite elements of concrete and reinforcing bars, and interactions between these elements. The parametric model served as the base for generating a comprehensive set of solutions, which were subsequently used to train an artificial neural network (ANN). This neural network was designed to predict the behaviour of the slab. By training the ANN with this extensive dataset, the authors of the paper aimed to create an efficient and accurate tool capable of reproducing the structural characteristics of the reinforced concrete slabs. For all analysed variants, boundary conditions were assumed reflecting the slab restrained over the entire perimeter. The numerical analysis was carried out in the ADINA System program.

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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: 11 PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, SOFT COMPUTING, MACHINE LEARNING AND OPTIMIZATION IN ENGINEERING Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 1.1 Digital Twin of the Reinforced Concrete Slab Based on the Artificial Neural Network P. Lacki, A. Derlatka, J. Niemiro-Maźniak and M. Lacki Czestochowa University of Technology, Poland doi:10.4203/ccc.11.1.1 Full Bibliographic Reference for this paper P. Lacki, A. Derlatka, J. Niemiro-Maźniak, M. Lacki, "Digital Twin of the Reinforced Concrete Slab Based on the Artificial Neural Network", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Seventh International Conference on Artificial Intelligence, Soft Computing, Machine Learning and Optimization in Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 11, Paper 1.1, 2025, doi:10.4203/ccc.11.1.1 Keywords: digital twin, artificial neural network, finite element method, reinforced concrete, genetic algorithm, slab. Abstract The article presents a Digital Twin of the reinforced concrete slab to analyse the load-bearing capacity using artificial intelligence algorithms. To achieve this goal, the authors developed three-dimensional parametric numerical model of slab using the finite element method (FEM). When design the numerical model of reinforced concrete members, it is particularly important to select the concrete material model, the type of finite elements of concrete and reinforcing bars, and interactions between these elements. The parametric model served as the base for generating a comprehensive set of solutions, which were subsequently used to train an artificial neural network (ANN). This neural network was designed to predict the behaviour of the slab. By training the ANN with this extensive dataset, the authors of the paper aimed to create an efficient and accurate tool capable of reproducing the structural characteristics of the reinforced concrete slabs. For all analysed variants, boundary conditions were assumed reflecting the slab restrained over the entire perimeter. The numerical analysis was carried out in the ADINA System program. download the full-text of this paper (PDF, 10 pages, 992 Kb) go to the next paper return to the table of contents return to the volume description
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