###### Abstract
Owing to the lack of labeled data, such classifiers are typically calibrated on physics-based models before being applied to real-world data [44]. [PERSON] et al. [45] have introduced a data-driven damage index that incorporates peak and accumulated components and subsequently formulated a link between the damage index and extent of damage, which has been derived from model simulations and successfully applied to real structural elements (concrete columns). [PERSON] et al. [46] have used domain adaptation techniques to improve the performance of model-based classifiers on real data. However, DSFs were exclusively derived from ambient data, which only contains information about residual stiffness degradation. Moving to the level of prognosis, data-driven indicators can further serve as a starting point for the updating of physics-based structural models [47; 48], which can be used to forecast residual capacity after the strike of an earthquake event [49]. Several DSFs have been proposed in the past. [PERSON] et al. [50; 51] and [PERSON] [52] proposed wavelet decomposition [53] as a starting point to define DSFs; [PERSON] and [PERSON] [54] and [PERSON] et al. [55] introduced transmissibility-based DSFs to detect damage; changes in mode shapes and their curvatures were successfully introduced to detect and localize damage [56; 57; 58; 59]; and [PERSON] and [PERSON] [60] proposed the use of floor spectra for damage localization. However, a thorough comparison of DSFs and their application to highly transient signals, such as the structural response to seismic ground motions, is still lacking. Many vibration-based damage detection approaches have been applied and tested on high-rise buildings or bridges [61; 62; 63; 64; 65; 66; 67; 68]. While slender structures allow for the identification of higher modes, which are often found to be most sensitive to damage and its location [56], this is often impossible for low-to-mid rise buildings, which comprise the most common building typology and may be characterized by high lateral stiffness, such as is the case for un-reinforced masonry buildings, which present the most vulnerable elements within the European building stock. While in many applications, DSFs track changes in modal properties that are derived from AVs, the potential nonlinearity that occurs during seismic events may be undetectable at low amplitudes of shaking due to the closing of the cracks or the compensating effect of multiple damage sources [69]. Indeed, buildings have been found to slowly return to the pre-seismic natural frequencies [70; 71], which is a phenomenon that could mask structural weakening. Permanent monitoring installations allow for the continuous tracking of structural response and allow for detecting nonlinearity; this is often separate to residual damage but allows for quantification of the level of cyclic forcing and deformation sustained by a structure, which relates to damaging effects. In this work, we make such a distinction and seek for features, which are not limited to residual damage but allow for the racking of nonlinearity, which is particularly relevant for structures experiencing plasticity and energy dissipation effects. This paper evaluates the performance of acceleration-based DSFs with respect to their capacity to detect, localize, and scale with the quantity of nonlinearity. The specific contributions can be outlined as follows:  
* We offer a systematic and thorough comparison on the capacity of DSFs to detect, localize, and quantify damage using highly transient vibration response data recorded during earthquakes. * We compare the capacity of such established DSFs to distinguish between temporary nonlinearity and residual stiffness degradation. * We offer a data-driven evaluation in terms of absolute and incremental damage occurrence during seismic sequences. We restrict this study to purely data-driven schemes; hence, physics-based simulation models are not used, and exact damage identification is not sought.