## 4 Conclusions  
This work assesses data-driven damage-sensitive features (DSFs) in terms of their potential to identify earthquake-induced damage and manifested nonlinearity in the response of low-to-midrise residential buildings. Based on a thorough simulated analysis on an exemplary nonlinear hysteretic spring-mass system and a half-scale building specimen experimentally tested on a shake table, the following conclusions are drawn:  
* Damage-sensitive features derived in the frequency domain, using either transmissibility or wavelet decomposition, are promising for data-driven nonlinearity detection. However, reference linear data are required to detect the onset of nonlinearity.
* Damage-sensitive features do not only indicate presence of damage and evolution of nonlinearity but further carry the potential to localize damage and quantify its extent.
* Damage-sensitive features allow to identify the evolution of the structural state and the accumulation of damage over the course of multiple earthquake instances, in addition to picking up the severity of damage that is incrementally introduced by a specific earthquake in a sequence.
* DSFs calculated for short time windows are necessary to track nonlinearity over time, which enables differentiating between reversible and irreversible stiffness drops.  
The findings of this thorough comparative assessment of DSFs further show that an exclusive reliance on WN reference data may lead to false alarms, as the frequency content of earthquake signals may lead to DSFs that differ from the reference values even for linear responses. In addition, WN data recorded after an earthquake event can only serve for offering information on (possible) residual damage. Instead, the continuous monitoring and appropriate windowing (segmentation) of the vibration response, which is recorded during strong ground motions, is key to retrieving additional information on temporarily experienced nonlinearity. The latter, as well, may weaken the structure and consume part of its capacity to absorb hysteretic energy. Finally, the formulations of DSFs and their target domain differ and thus, robust damage diagnosis should involve a combination of multiple DSFs.  
Future work will focus on the fusion of individual DSFs in a classification framework, which can more precisely localize damage and assess its influence on the residual seismic capacity. Moreover, the reliability of purely data-driven assessment will be compared against model-based approaches, which are required to diagnose the consequence of damage on the seismic performance.  
Conceptualization, Y.R. and E.C.; methodology, Y.R., P.M. and E.C.; software, Y.R.; validation, Y.R. and P.M.; formal analysis, Y.R.; investigation, Y.R. and P.M.; resources, Y.R., P.M. and E.C.; data curation, Y.R.; writing--original draft preparation, Y.R.; writing--review and editing, Y.R., P.M. and E.C.; visualization, Y.R.; supervision, E.C.; funding acquisition, E.C. All authors have read and agreed to the published version of the manuscript.  
The research described in this paper was financially supported by the Real-time Earthquake Risk Reduction for a Resilient Europe 'RISE' project, financed under the European Union's Horizon 2020 research and innovation programme, under grant agreement No. 821115.  
The data presented in this study and the functions to derive DSFs from acceleration response data are available on request from the corresponding author.  
The authors would like to thank [PERSON] and [PERSON], MSc students at ETH Zurich, for their contributions in testing and validating damage-sensitive features.  
The authors declare no conflict of interest.