An improved time-domain spectral matching algorithm for peak adjustment and convergence efficiency optimization

Nonlinear time history analysis is necessary for the seismic design of complicated or irregular structures. Compared to artificial ground motion, real earthquakes matched with the target spectrum are more favorable, since the matched ground motions (generated by spectral matching algorithms) can better preserve the period and phase characteristics. Therefore, an improved time-domain spectral matching algorithm is proposed to generate ground motion time history that matches the target response spectrum. The proposed algorithm introduces a new period subdivision sorting strategy and an adaptive convergence parameter selection method to improve the convergence efficiency of spectral matching. In addition, a peak ground acceleration (PGA) correction algorithm is introduced to maintain PGA consistency. The effectiveness of the proposed method is validated by several real earthquake records, and the method is compared with an existing time spectral matching algorithm. Based on the matched results, the period subdivision sorting strategy can effectively reduce mutual interference from added adjustment wavelets, and the adaptive convergence parameter selection method considers the correlation between wavelet functions and provides better numerical stability to improve convergence efficiency. The proposed PGA correction algorithm provides high PGA matching accuracy and omits velocity and displacement drifts.