A Filter Method for Dynamic Monitoring Data of Masonry Partition Walls in Subway Stations Based on a Butterworth Filter

Under the combined effects of vibrations from train operations and wind loads, the dynamic response monitoring data of masonry partition walls in subway stations are often contaminated with high-frequency noise, which hinders the accurate identification of the structure’s true dynamic characteristics. To tackle this problem, this paper proposes employing a Butterworth low-pass filter to process the on-site monitoring data. The paper initially elaborates on the monitoring theory grounded in the pulsation method, followed by a detailed explanation of the rationale for selecting the Butterworth filter, as well as data processing techniques such as Fast Fourier Transform (FFT) and self-power spectrum analysis. By incorporating a field monitoring case from a subway station in Guangzhou, the paper compares and analyzes the acceleration time-history curves before and after filtering. Additionally, finite element analysis is performed to assess the mechanical response of the masonry wall under wind loads, train-induced vibrations, and their combined effects. The results demonstrate that after applying a 4th-order Butterworth low-pass filter with a 46 Hz cutoff frequency, the high-frequency noise in the data is effectively suppressed, thereby accentuating the main trend and low-frequency vibration characteristics of the signal. This provides a reliable data foundation for subsequent precise analysis of the dynamic response and fatigue performance of the masonry walls.