Finite Element Analysis of Perforated Prestressed Concrete Frame Enhanced by Artificial Neural Networks
Abstract
:1. Introduction
2. Finite Element Methods Enhanced by Artificial Neural Networks
2.1. Surrogate-Based Finite Element Methods
2.2. Artificial Neural Networks
3. Verifications
4. Numerical Examples
4.1. The Perforated Prestressed Concrete Frame
4.2. Numerical Experiments for Frames with Different Distances between Openings
4.3. Numerical Experiments for Perforated Prestressed Concrete under Different Loadings
5. Conclusions
- (1)
- When using a linear regression model to predict the displacement and stress fields of prestressed concrete simply supported by beams with an open hole and a constant total stiffness matrix, the final SMAPE is less than 1%, and the differences in various indicators between the training and testing datasets are small.
- (2)
- When only changing the opening position, for each type of opening position of the beam, 2000 samples were generated for training by changing the stress levels of each node on the top surface and the tension control stress of the prestressed reinforcement. The SMAPE of the final predicted displacement field and stress field are both less than 5%. When both the opening position and the reinforcement area are changed simultaneously, there is only one sample for each total stiffness matrix. The SMAPE of the final predicted displacement field and stress field is also less than 5%.
- (3)
- Due to the vertical displacement of each node of the frame column under a vertical load being close to zero, the SMAPE value is relatively high. However, for the vertical displacement field and stress field of the frame beam, the SMAPE is less than 5%, indicating high prediction accuracy.
- (4)
- Compared with the results of a finite element analysis, the relative error is larger at areas with lower stress values. However, most cases have a relative error of less than 5% in areas with high stress values. Therefore, it is feasible to use trained deep learning models for predictions.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Element Style | Number of Elements | Number of Nodes | |
---|---|---|---|
Concrete | C3D8R | 5850 | 7992 |
Longitudinal bar | T3D2 | 750 | 756 |
Prestressed bar | T3D2 | 375 | 378 |
Beam stirrup | T3D2 | 806 | 806 |
Chord stirrup | T3D2 | 144 | 144 |
Total | - | 7925 | 10,076 |
Element Style | Number of Elements | Number of Nodes | |
---|---|---|---|
Concrete | C3D8R | 9208 | 12,699 |
Prestress bar | T3D2 | 316 | 318 |
Beam longitudinal bar | T3D2 | 465 | 468 |
Twisted bar in beam | T3D2 | 276 | 282 |
Beam stirrup | T3D2 | 1848 | 1848 |
Beam chord stirrup | T3D2 | 440 | 440 |
Beam suspension bar | T3D2 | 264 | 264 |
Beam reinforcement bars | T3D2 | 180 | 192 |
Column longitudinal bar | T3D2 | 552 | 564 |
Column stirrup | T3D2 | 1120 | 1120 |
Total | - | 15,134 | 18,663 |
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Wu, Y.; Chen, J.; Zhu, P.; Zhi, P. Finite Element Analysis of Perforated Prestressed Concrete Frame Enhanced by Artificial Neural Networks. Buildings 2024, 14, 3215. https://doi.org/10.3390/buildings14103215
Wu Y, Chen J, Zhu P, Zhi P. Finite Element Analysis of Perforated Prestressed Concrete Frame Enhanced by Artificial Neural Networks. Buildings. 2024; 14(10):3215. https://doi.org/10.3390/buildings14103215
Chicago/Turabian StyleWu, Yuching, Jingbin Chen, Peng Zhu, and Peng Zhi. 2024. "Finite Element Analysis of Perforated Prestressed Concrete Frame Enhanced by Artificial Neural Networks" Buildings 14, no. 10: 3215. https://doi.org/10.3390/buildings14103215