Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks
This article presents the development and analysis of a Physics-Informed Neural Network (PINN) model for calculating the deflection of a simply supported beam under uniformly distributed load. The training dataset was synthesized based on analytical principles of structural mechanics and included th...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Moscow State University of Civil Engineering (MGSU)
2025-03-01
|
| Series: | Железобетонные конструкции |
| Subjects: | |
| Online Access: | https://www.g-b-k.ru/jour/article/view/69 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849434703035105280 |
|---|---|
| author | F. N. Zakharov Qian Jie Xu Yi |
| author_facet | F. N. Zakharov Qian Jie Xu Yi |
| author_sort | F. N. Zakharov |
| collection | DOAJ |
| description | This article presents the development and analysis of a Physics-Informed Neural Network (PINN) model for calculating the deflection of a simply supported beam under uniformly distributed load. The training dataset was synthesized based on analytical principles of structural mechanics and included the following parameters: relative length of the measurement section l, number of measurement points N, and noise level R. The training dataset consisted of 1,296 rows describing random points within the beam span. In this study, 480 PINN models were trained to evaluate the impact of the weight of the physics-informed loss function, the number of measurements, and the noise level on prediction accuracy. The results demonstrated that PINN models achieve high accuracy (R2 ≥ 0.88) even with high noise levels (R > 20 %) and exhibit robustness to low and moderate noise levels. The study identified that adjusting the weight of the physics-informed loss function is a key parameter for achieving an optimal balance between the loss functions of physical laws and experimental data. Increasing the number of measurement points positively influences accuracy at low noise levels. However, an increase in the number of measurement points under high noise levels reduces the prediction accuracy of the model. The scientific novelty of the study lies in proposing an approach for structural analysis using PINN, which integrates physical laws into the training process. The findings confirm the potential of using PINN for engineering calculations, particularly under limited data conditions. |
| format | Article |
| id | doaj-art-b1abc37b1faa4f59b0dd6a4cf089ec06 |
| institution | Kabale University |
| issn | 2949-1622 2949-1614 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Moscow State University of Civil Engineering (MGSU) |
| record_format | Article |
| series | Железобетонные конструкции |
| spelling | doaj-art-b1abc37b1faa4f59b0dd6a4cf089ec062025-08-20T03:26:34ZengMoscow State University of Civil Engineering (MGSU)Железобетонные конструкции2949-16222949-16142025-03-0191354810.22227/2949-1622.2025.1.35-4863Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural NetworksF. N. Zakharov0Qian Jie1Xu Yi2Bluetown Leju Construction Management Co. Ltd. Research InstituteBluetown Leju Construction Management Co. Ltd. Research InstituteZhejiang Province Architectural Design and Research InstituteThis article presents the development and analysis of a Physics-Informed Neural Network (PINN) model for calculating the deflection of a simply supported beam under uniformly distributed load. The training dataset was synthesized based on analytical principles of structural mechanics and included the following parameters: relative length of the measurement section l, number of measurement points N, and noise level R. The training dataset consisted of 1,296 rows describing random points within the beam span. In this study, 480 PINN models were trained to evaluate the impact of the weight of the physics-informed loss function, the number of measurements, and the noise level on prediction accuracy. The results demonstrated that PINN models achieve high accuracy (R2 ≥ 0.88) even with high noise levels (R > 20 %) and exhibit robustness to low and moderate noise levels. The study identified that adjusting the weight of the physics-informed loss function is a key parameter for achieving an optimal balance between the loss functions of physical laws and experimental data. Increasing the number of measurement points positively influences accuracy at low noise levels. However, an increase in the number of measurement points under high noise levels reduces the prediction accuracy of the model. The scientific novelty of the study lies in proposing an approach for structural analysis using PINN, which integrates physical laws into the training process. The findings confirm the potential of using PINN for engineering calculations, particularly under limited data conditions.https://www.g-b-k.ru/jour/article/view/69physics-informed neural networksstructural mechanicslimited dataartificial intelligencebeam calculation |
| spellingShingle | F. N. Zakharov Qian Jie Xu Yi Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks Железобетонные конструкции physics-informed neural networks structural mechanics limited data artificial intelligence beam calculation |
| title | Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks |
| title_full | Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks |
| title_fullStr | Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks |
| title_full_unstemmed | Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks |
| title_short | Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks |
| title_sort | physics informed neural networks for structural mechanics and construction modeling the deflection of a single span beam physics informed neural networks |
| topic | physics-informed neural networks structural mechanics limited data artificial intelligence beam calculation |
| url | https://www.g-b-k.ru/jour/article/view/69 |
| work_keys_str_mv | AT fnzakharov physicsinformedneuralnetworksforstructuralmechanicsandconstructionmodelingthedeflectionofasinglespanbeamphysicsinformedneuralnetworks AT qianjie physicsinformedneuralnetworksforstructuralmechanicsandconstructionmodelingthedeflectionofasinglespanbeamphysicsinformedneuralnetworks AT xuyi physicsinformedneuralnetworksforstructuralmechanicsandconstructionmodelingthedeflectionofasinglespanbeamphysicsinformedneuralnetworks |