Physical-data cooperative driving long-span bridge typhoon effect probability prediction method

Through the physics-data collaboration-driven typhoon effect probability prediction method of large-span bridges, combined with the laws of physics and deep learning models, the existing prediction methods have limited accuracy and lack of interpretability, achieving more accurate and stable typhoon effect prediction, and enhancing uncertain quantification performance.

CN120217900AActive Publication Date: 2025-06-27SOUTHEAST UNIV
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Patent Information

Application Number
CN202510682584.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-26
Publication Date
2025-06-27
Estimated Expiration
2045-05-26

AI Technical Summary

Technical Problem

The existing typhoon effect prediction methods have limited prediction accuracy, difficulty in quantifying uncertainty, unstable prediction performance, and lack of interpretability.

Method used

The typhoon effect prediction method of large-span bridges driven by physics-data is adopted. By acquiring bridge structure health monitoring data, combining physical laws and data-driven deep learning models, a typhoon effect probability prediction model is constructed that provides response mean and variance dynamic estimation, and an integrated learning and improved Shapley additive interpretation method are used to enhance the stability and interpretability of the model.

Benefits of technology

The prediction accuracy and uncertain quantification performance of vibration response of large-span bridges under the action of typhoons are improved, the stability and interpretability of the model are enhanced, and more comprehensive wind disaster prevention information is provided.

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Abstract

The invention discloses a long-span bridge typhoon effect probability prediction method based on physical-data cooperative driving, and the method comprises the steps: obtaining typhoon and typhoon effect monitoring data recorded by a structure health monitoring system, and calculating typhoon characteristic parameters and bridge vibration response parameters; then, taking typhoon characteristic parameters as model input and bridge vibration response parameters as model output, constructing a sample set, and dividing the sample set into a training set and a test set; a deep integration strategy of physical-data cooperative driving is further adopted to adjust a neural network architecture, and a typhoon effect probability prediction model providing response mean value and variance dynamic estimation is constructed; then, based on the divided data set and the established probability prediction model, carrying out model training and response prediction; and finally, carrying out model interpretation analysis by adopting an improved Shapril additive interpretation method. According to the method, the typhoon effect prediction accuracy and uncertainty quantification performance of the long-span bridge can be effectively improved, and the robustness and interpretability of the model are synchronously enhanced.
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