The accuracy of shaking tables in seismic simulations is limited by harmonic distortions in acceleration signals resulting from the inherent nonlinearity of the system. This study presents a novel approach based on the Cheetah Optimization (CO) algorithm for estimating the amplitude and phase components of these harmonics. The algorithm's performance was first validated against other metaheuristic methods in the literature on a standard test signal and was found to be successful. Subsequently, the CO algorithm was applied to data obtained from a real-time shaking table experimental setup. These experiments included testing the system under loaded and unloaded conditions, at three different displacement levels (3.55 mm, 5 mm, 10 mm) and with three different waveforms: sinusoidal, triangular, and square. The results showed that the CO algorithm exhibited very high estimation accuracy for sinusoidal signals in both load cases. However, the algorithm's performance degraded under load on triangular signals and struggled to accurately model the signal in all scenarios due to the sharp transitions and high harmonic content of square waveforms. These findings suggest that while CO has proven to be an effective method for shaking table vibration analysis, particularly for sinusoidal signals, additional model improvements are necessary for more complex waveforms.
Sunca et al. (Sun,) studied this question.