A novel methodology for modal parameter identification of arch dam based on multi-level information fusion

Operational modal analysis plays an important role in the structural health monitoring and safety diagnosis of arch dam. However, due to background noise, it is difficult to accurately extract the effective characteristics information of arch dam from the vibration responses whose amplitudes are too small under ambient vibration, and the deviation caused by traditional identification methods will directly affect the estimation accuracy for structural modal parameters. Therefore, a novel methodology for modal parameter identification of arch dam based on multi-level infor-mation fusion is proposed in this paper. The proposed method is based on multi-sensor data-level fusion to identify the structural natural frequency and damping ratio, which greatly preserves and extracts structural modal properties in the vibration responses. Meanwhile, structural mode shapes are identified based on dynamic feature-level fusion, which significantly improves the identification accuracy. The effectiveness and feasibility of the proposed method are verified by the modal results of digital signals and simulated signals in the 7-DOF system. Prototype engi-neering case shows that the closely spaced and high-frequency modes can be decomposed and identified by the proposed method, and this method has a higher identification accuracy, which can provide a new idea for modal parameter identification of arch dam.

Automated summary

Operational modal analysis is crucial in the monitoring and diagnosis of arch dam's structural health and safety. However, extracting accurate information from the vibration responses of arch dam is challenging due to background noise and small amplitudes. Traditional identification methods may lead to deviation that affects the accuracy of structural modal parameter estimation. Therefore, this paper proposes a novel methodology based on multi-level information fusion for modal parameter identification of arch dam. The proposed method identifies the structural natural frequency and damping ratio through multi-sensor data-level fusion and improves identification accuracy by identifying structural mode shapes based on dynamic feature-level fusion. The effectiveness and feasibility of the method are verified through modal results from digital and simulated signals in a 7-DOF system. An engineering case study demonstrates that the proposed method can decompose and identify closely spaced and high-frequency modes with higher accuracy, providing a new approach for modal parameter identification of arch dam.