Comparison of the Finite Element Method and Rigid Finite Element Method During Dynamic Calculations of Steel–Concrete Composite Beams Based on Experimental Results
Dynamic analysis of structures is a key challenge in structural engineering, especially in choosing effective and accurate numerical methods. Steel–concrete composite structures, commonly used in bridges and floors, require calculations of dynamic parameters to ensure safety and comfort. Few studies...
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| Published in | Materials Vol. 17; no. 24; p. 6081 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
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MDPI AG
12.12.2024
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| Online Access | Get full text |
| ISSN | 1996-1944 1996-1944 |
| DOI | 10.3390/ma17246081 |
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| Abstract | Dynamic analysis of structures is a key challenge in structural engineering, especially in choosing effective and accurate numerical methods. Steel–concrete composite structures, commonly used in bridges and floors, require calculations of dynamic parameters to ensure safety and comfort. Few studies compare the effectiveness of the finite element method (FEM) and the rigid finite element method (RFEM) in the dynamic analysis of such structures. This study fills this gap by comparing the methods using experimental results. FEM and RFEM models were developed using Abaqus, Python, and Matlab. The main parameters were identified, i.e., the Young’s modulus of the concrete slab (EC) and the stiffness of the connection (Kx, KRX, Kv, Kh). Both methods closely matched the experimental results. The RFEM matched natural frequencies with 2–3% deviations, while the FEM showed 3–4% deviations for the torsional, axial, and first three flexural frequencies. The RFEM reduced the computation time by about 65%, making it suitable for large-scale applications. The FEM provided a finer resolution of local effects due to its higher element density. The results can be applied to the design of bridges, floors, and other structures under dynamic loads. It will also provide the authors with a basis for developing structural health monitoring (SHM). |
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| AbstractList | Dynamic analysis of structures is a key challenge in structural engineering, especially in choosing effective and accurate numerical methods. Steel–concrete composite structures, commonly used in bridges and floors, require calculations of dynamic parameters to ensure safety and comfort. Few studies compare the effectiveness of the finite element method (FEM) and the rigid finite element method (RFEM) in the dynamic analysis of such structures. This study fills this gap by comparing the methods using experimental results. FEM and RFEM models were developed using Abaqus, Python, and Matlab. The main parameters were identified, i.e., the Young’s modulus of the concrete slab (E[sub.C]) and the stiffness of the connection (K[sub.x], K[sub.RX], K[sub.v], K[sub.h]). Both methods closely matched the experimental results. The RFEM matched natural frequencies with 2–3% deviations, while the FEM showed 3–4% deviations for the torsional, axial, and first three flexural frequencies. The RFEM reduced the computation time by about 65%, making it suitable for large-scale applications. The FEM provided a finer resolution of local effects due to its higher element density. The results can be applied to the design of bridges, floors, and other structures under dynamic loads. It will also provide the authors with a basis for developing structural health monitoring (SHM). Dynamic analysis of structures is a key challenge in structural engineering, especially in choosing effective and accurate numerical methods. Steel-concrete composite structures, commonly used in bridges and floors, require calculations of dynamic parameters to ensure safety and comfort. Few studies compare the effectiveness of the finite element method (FEM) and the rigid finite element method (RFEM) in the dynamic analysis of such structures. This study fills this gap by comparing the methods using experimental results. FEM and RFEM models were developed using Abaqus, Python, and Matlab. The main parameters were identified, i.e., the Young's modulus of the concrete slab (EC) and the stiffness of the connection (Kx, KRX, Kv, Kh). Both methods closely matched the experimental results. The RFEM matched natural frequencies with 2-3% deviations, while the FEM showed 3-4% deviations for the torsional, axial, and first three flexural frequencies. The RFEM reduced the computation time by about 65%, making it suitable for large-scale applications. The FEM provided a finer resolution of local effects due to its higher element density. The results can be applied to the design of bridges, floors, and other structures under dynamic loads. It will also provide the authors with a basis for developing structural health monitoring (SHM).Dynamic analysis of structures is a key challenge in structural engineering, especially in choosing effective and accurate numerical methods. Steel-concrete composite structures, commonly used in bridges and floors, require calculations of dynamic parameters to ensure safety and comfort. Few studies compare the effectiveness of the finite element method (FEM) and the rigid finite element method (RFEM) in the dynamic analysis of such structures. This study fills this gap by comparing the methods using experimental results. FEM and RFEM models were developed using Abaqus, Python, and Matlab. The main parameters were identified, i.e., the Young's modulus of the concrete slab (EC) and the stiffness of the connection (Kx, KRX, Kv, Kh). Both methods closely matched the experimental results. The RFEM matched natural frequencies with 2-3% deviations, while the FEM showed 3-4% deviations for the torsional, axial, and first three flexural frequencies. The RFEM reduced the computation time by about 65%, making it suitable for large-scale applications. The FEM provided a finer resolution of local effects due to its higher element density. The results can be applied to the design of bridges, floors, and other structures under dynamic loads. It will also provide the authors with a basis for developing structural health monitoring (SHM). Dynamic analysis of structures is a key challenge in structural engineering, especially in choosing effective and accurate numerical methods. Steel–concrete composite structures, commonly used in bridges and floors, require calculations of dynamic parameters to ensure safety and comfort. Few studies compare the effectiveness of the finite element method (FEM) and the rigid finite element method (RFEM) in the dynamic analysis of such structures. This study fills this gap by comparing the methods using experimental results. FEM and RFEM models were developed using Abaqus, Python, and Matlab. The main parameters were identified, i.e., the Young’s modulus of the concrete slab (EC) and the stiffness of the connection (Kx, KRX, Kv, Kh). Both methods closely matched the experimental results. The RFEM matched natural frequencies with 2–3% deviations, while the FEM showed 3–4% deviations for the torsional, axial, and first three flexural frequencies. The RFEM reduced the computation time by about 65%, making it suitable for large-scale applications. The FEM provided a finer resolution of local effects due to its higher element density. The results can be applied to the design of bridges, floors, and other structures under dynamic loads. It will also provide the authors with a basis for developing structural health monitoring (SHM). Dynamic analysis of structures is a key challenge in structural engineering, especially in choosing effective and accurate numerical methods. Steel-concrete composite structures, commonly used in bridges and floors, require calculations of dynamic parameters to ensure safety and comfort. Few studies compare the effectiveness of the finite element method (FEM) and the rigid finite element method (RFEM) in the dynamic analysis of such structures. This study fills this gap by comparing the methods using experimental results. FEM and RFEM models were developed using Abaqus, Python, and Matlab. The main parameters were identified, i.e., the Young's modulus of the concrete slab (E ) and the stiffness of the connection (K , K , K , K ). Both methods closely matched the experimental results. The RFEM matched natural frequencies with 2-3% deviations, while the FEM showed 3-4% deviations for the torsional, axial, and first three flexural frequencies. The RFEM reduced the computation time by about 65%, making it suitable for large-scale applications. The FEM provided a finer resolution of local effects due to its higher element density. The results can be applied to the design of bridges, floors, and other structures under dynamic loads. It will also provide the authors with a basis for developing structural health monitoring (SHM). |
| Audience | Academic |
| Author | Abramowicz, Małgorzata Pełka-Sawenko, Agnieszka |
| AuthorAffiliation | Faculty of Civil and Environmental Engineering, West Pomeranian University of Technology in Szczecin, Al. Piastów 17, 70-310 Szczecin, Poland |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39769681$$D View this record in MEDLINE/PubMed |
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| Keywords | dynamic test modal parameters composite beam 3D finite element model (FEM) 3D rigid finite element model (RFEM) |
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| Snippet | Dynamic analysis of structures is a key challenge in structural engineering, especially in choosing effective and accurate numerical methods. Steel–concrete... Dynamic analysis of structures is a key challenge in structural engineering, especially in choosing effective and accurate numerical methods. Steel-concrete... |
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| SubjectTerms | Bridges Comparative analysis Composite beams Composite structures Concrete Concrete slabs Construction Deviation Dynamic loads Effectiveness Eigenvectors Finite element analysis Finite element method Mathematical analysis Methods Numerical methods Parameter identification Polymers Reinforced concrete Resonant frequencies Steel pipes Structural engineering Structural health monitoring |
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| Title | Comparison of the Finite Element Method and Rigid Finite Element Method During Dynamic Calculations of Steel–Concrete Composite Beams Based on Experimental Results |
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