A Buffeting-Net for buffeting response prediction of full-scale bridges. (15th January 2023)

• Record Type: Journal Article
• Authors: Laima, Shujin; Feng, Hui; Li, Hui; Jin, Yao; Han, Feiyang; Xu, Wencheng
• Is Part Of: Engineering structures. Volume 275(2023)Part A
• Publication Date: 2023-01-15
• Subjects: Buffeting response -- Full-scale bridge -- Machine learning -- Field monitoring data; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.115289
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A neural network-based model (Buffeting-Net) is proposed to predict buffeting response of full-scale bridges. Inspired by the mechanism of buffeting, the model is composed of two sub-neural networks. Buffeting-Net can make more accurate predictions for full-scale bridges than FEM-based buffeting theory. Abstract: In order to predict power spectral density (PSD) and root of mean squares (RMS) of buffeting responses of full-scale bridges in real operation conditions, a neural network-based model in frequency domain (Buffeting-Net) is proposed. Inspired by the mechanism of buffeting, the model is composed of two sub-neural networks that output the buffeting force and the generalized frequency response function. These two sub-neural networks are connected according to the model of buffeting response in the frequency domain to output the predicted spectrum of response. Sub-neural networks are constructed by bi-directional gated recurrent unit neural networks for their capacity of modeling complex mappings among sequences. Monitoring data of two long-span bridges are used for model validation, and the accuracy of prediction is evaluated by the cosine similarity of the shape of power spectrum and the relative error of RMS responses. The prediction results of Buffeting-Net are also compared with the traditional FEM-based buffeting method. The results indicate that trained models can make much higher accurate predictions for the bridges in real operational conditions including typhoon and season wind than regular finite element model-based buffeting analysis method. The generalization of the Buffeting-Net is validated, the result indicates the Buffeting-Net can predict the buffeting response with high prediction accuracy even when the wind speed is much greater than that in training.