Bibliographic Details
| Title: |
Prediction and optimization of structural performance of prefabricated bridges based on physical information neural network (PINN) and BlM |
| Authors: |
Bo Yang, Zhang Han, Ming Yang |
| Source: |
Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-20 (2025) |
| Publisher Information: |
Springer, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Computational linguistics. Natural language processing
LCC:Electronic computers. Computer science |
| Subject Terms: |
Physical information neural network (PINN), Building information modeling (BIM), Prefabricated bridges, Performance prediction, Structural optimization, Intelligent construction, Computational linguistics. Natural language processing, P98-98.5, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: |
Abstract This study presents an innovative performance prediction and optimization method for prefabricated bridge structures, which integrates the strengths of Physical Information Neural Network (PINN) and Building Information Modeling (BIM). By combining the computational power of PINN for solving structural mechanics problems with BIM’s data management and modeling capabilities in construction engineering, a multi-layer, multi-dimensional intelligent framework for prediction and optimization is developed. BIM models provide crucial data, including geometric details, material properties, and construction process information, which are used to create a digital twin of the bridge. Leveraging PINN’s robust fitting ability, physical laws such as stress, strain, and equilibrium equations are incorporated into the neural network to predict the bridge’s performance under various operational conditions. The integration of physical constraints during PINN training enhances both the accuracy and reliability of the predictions. Subsequently, optimization algorithms refine the bridge design based on these predictions, ensuring improvements in performance while meeting key criteria such as safety, cost-effectiveness, and environmental impact. This method offers a scientifically rigorous and highly accurate approach to supporting the design and construction of prefabricated bridges, advancing intelligent construction and sustainable building practices. Experimental results demonstrate that the integrated PINN-BIM model outperforms traditional methods, exhibiting superior accuracy, generalization capability, and offering a novel solution for efficient bridge engineering construction and performance optimization. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2731-0809 |
| Relation: |
https://doaj.org/toc/2731-0809 |
| DOI: |
10.1007/s44163-025-00245-5 |
| Access URL: |
https://doaj.org/article/64a34714c5b14021a6d3b586cfd668a4 |
| Accession Number: |
edsdoj.64a34714c5b14021a6d3b586cfd668a4 |
| Database: |
Directory of Open Access Journals |
