Research on Health Monitoring of Flying‐Swallow‐Typed Tied Arch Bridge Based on PSO‐GRNN Algorithm

• Published in: Modelling and Simulation in Engineering Vol. 2024; no. 1
• Main Authors: Zhang, Tianpeng, Liu, Long, Ji, Pengfei
• Format: Journal Article
• Language: English
• Published: New York John Wiley & Sons, Inc 2024; Wiley
• Subjects: Accuracy; Algorithms; Analysis; Approximation; Arch bridges; Back propagation networks; Bridges; Concrete; Data acquisition; Finite element method; Flight; General regression neural networks; Mathematical models; Mathematical optimization; Neural networks; Neurons; Optimization; Parameters; Particle swarm optimization; Prediction models; Root-mean-square errors; Standard scores; Steel columns; Steel pipes; Steel tubes; Velocity; China
• ISSN: 1687-5591; 1687-5605; 1687-5605
• DOI: 10.1155/2024/7664816

In the health monitoring work of long‐span concrete‐filled steel tube tied arch bridges, finite element models have been commonly employed to indicate the practical stress state, and providing accurate data in real time and efficiently has been confirmed as the weakness of the finite element model. The prediction model is built in accordance with the general regression neural network (GRNN), and the parameters of the GRNN model are optimized using particle swarm optimization (PSO) to build the PSO‐GRNN prediction model, with the aim of modifying the finite element model. A finite element analysis model is built using the Qiuhuli flying‐swallow‐typed tied arch bridge to verify the effect of the PSO‐GRNN prediction model. The model test data are acquired using the horizontal thrust of arch foot, the bulk weight of main beam, and the tension of tied rod as the input variables and using the stress of main arch rib steel pipe, the stress of main arch concrete, and the displacement of mid span as the output variables. As revealed by the results, the average prediction accuracy of the PSO‐GRNN model constructed in this article is 96.706%, 98.531%, and 99.634%, respectively, which are 1.980%, 1.706%, and 0.40% higher than the back propagation (BP) neural network model and 2.262%, 1.632%, and 0.387% higher than the GRNN model. The mean absolute percent error (MAPE), root mean square error (RMSE), coefficient of determination ( R 2 ), and Nash‐Sutcliffe efficiency (NSE) coefficient were used to evaluate the prediction performance of the model. The PSO‐GRNN model has the highest fitting accuracy, indicating that the established PSO‐GRNN prediction model can more effectively predict the relevant parameters of concrete‐filled steel tube tied arch bridges and has high accuracy.