Benchmarking time-domain, frequency-domain, and subspace estimators for ambient-vibration modal damping identification

Abstract

The damping ratio is the modal parameter that is hardest to identify reliably and exhibits the largest scatter in structural health monitoring and operational modal analysis; a trustworthy baseline relies on multi-estimator cross-validation and uncertainty quantification. Measured damping data for high-rise reinforced-concrete (RC) frame–shear-wall buildings under low-amplitude ambient vibration are scarce. For a 26-storey RC frame–shear-wall residential building, an operational modal analysis pipeline built only on open-source Python libraries is developed from three-component ambient-vibration records of eight measured floors and a continuous top-floor reference point. Three independent estimators—the random decrement technique, covariance-driven stochastic subspace identification, and the half-power bandwidth method—are applied and benchmarked against one another to estimate the damping ratios of the horizontal and vertical modes and to quantify their uncertainty. The measured ambient modal damping ratios are about 0.26%–0.6% in the horizontal direction and about 1.0%–1.6% in the vertical, the vertical damping being markedly higher than the horizontal. For spectrally isolated modes, the random decrement technique and the stochastic subspace identification yield highly consistent estimates, with per-mode differences within about 0.2%; for the fundamental horizontal mode, whose structural peak adjoins another spectral peak, the parametric subspace estimate is adopted, whereas the half-power bandwidth method is systematically biased high and serves only as a coarse reference. A trustworthy ambient-damping baseline with quantified uncertainty, together with an identification workflow built on open-source libraries (no commercial software), is thus provided for this class of high-rise buildings, supporting subsequent SHM baseline establishment and long-term condition tracking.