###### Abstract  
Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring a prompt and functional recovery of the built environment. Monitoring-based approaches have the potential to significantly improve upon current visual inspection-based condition assessment that is slow and potentially subjective. The large variety of sensing solutions that has become available at affordable cost in recent years allows the engineering community to envision permanent-monitoring applications even in conventional low-to-mid-rise buildings. When combined with adequate structural health monitoring (SHM) techniques, sensor data recorded during earthquakes have the potential to provide automated near-real-time identification of earthquake damage. Near-real time building assessment relies on the tracking of damage-sensitive features (DSFs) that can be directly and rapidly derived from dynamic monitoring data and scaled with damage. We here offer a comprehensive review of such damage-sensitive features in an effort to formally assess the capacity of such data-driven indicators to detect, localize and quantify the presence of nonlinearity in seismic-induced structural response. We employ both a parametric analysis on a simulated model and real data from shake-table tests to investigate the strengths and limitations of purely data-driven approaches, which typically involve a comparison against a healthy reference state. We present an array of damage-sensitive features which are found to be robust with respect to noise, to reliably detect and scale with nonlinearity, and to carry potential to localize the occurrence of nonlinear behavior in conventional structures undergoing earthquakes. seismic structural health monitoring; damage identification; vibration-based monitoring; post-earthquake building assessment; damage-sensitive features +
Footnote †: journal: Applied Sciences  
the treatment of these collected raw data, which is key in diagnosing structural condition under operating or extreme conditions [9]. To this end, structural health monitoring (SHM) offers a framework, which relies on the principle of continuous tracking of structural response, in order to detect, localize, and quantify damage, or even attempt to prognose remaining useful life [10]. SHM-based information has the potential to significantly improve upon current post-earthquake evaluation of buildings [11]. Features extracted from vibration measurements have been used by [PERSON] et al. [12] to track the current state and predict the performance of roller bearings and by [PERSON] et al. [13] for automated sensor diagnosis. Leveraging the correlation between the stiffness of a structure and the vibration modes, many applications of SHM for the built environment rely on changes in dynamic properties to extract indicators of damage [14; 15]. In particular, the link between modal properties, which can be extracted from ambient vibrations (AVs) using operational modal analysis (OMA), and damage has been extensively investigated [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. As a result, despite the limitations of such global indicators of the structural state [30; 31], dynamic properties derived from OMA, such as changes in fundamental frequencies, have been introduced into post-earthquake assessment frameworks [32; 33; 34; 35; 36; 37; 38; 39]. However, the robustness of such a single feature to encode the structural state has not been assessed. Modal properties constitute an essential instance of damage-sensitive features (DSFs), i.e., metrics that contain useful information to characterize the condition deterioration of a structure. Such DSFs, often more sophisticated than modal properties, can be extracted from dynamic measurements in near-real time. Such DSFs can subsequently be used to construct damage indices [9], whose probabilistic treatment allows building classifiers for damage detection and localization [40; 41; 42; 43].