Automated data processing and analysis method for shaking table tests of masonry structures based on python open-source toolchain

Shaking table testing is a crucial method for evaluating the seismic performance of structures; however, the resulting data are typically characterized by massive volumes, high sampling rates, and complex multi-channel arrays. Traditional manual processing methods relying on commercial spreadsheet software (e.g., Excel, Origin) present significant limitations regarding processing efficiency, mathematical transparency, and result reproducibility. To address these methodological gaps, this paper proposes a novel, fully automated data processing and analysis framework tailored for high-density structural dynamic testing using an open-source Python toolchain. Unlike conventional “black-box' commercial software, this method provides a transparent, end-to-end pipeline—from automated raw multi-channel data alignment and signal pre-processing to advanced time-frequency domain analysis and standardized visualization. The framework’s efficacy is validated using a shaking table test of a 1:2 scaled village masonry structure. The extracted experimental results clearly indicate that the masonry structure exhibits a significant low-pass filtering effect on high-frequency inputs (5–15 Hz), with response energy concentrated within the natural frequency range of 2–4 Hz. Furthermore, the pipeline integrates an automated structural health evaluation module; by comparing the Power Spectral Density (PSD) of white noise sweeps before and after seismic inputs, the method successfully and rapidly identified that while the structure exhibited displacement amplification under the 0.2 g operating condition, no significant stiffness degradation occurred. Ultimately, this study contributes a scalable, reproducible, and highly efficient methodological blueprint for big data analysis in structural seismic evaluation.