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In this study, accelerometric data from seven Japanese buildings under long-term monitoring were analysed to explore the variability of the buildings' co-seismic response over time and its within- and between-building components, using co-seismic capacity curves developed in acceleration-displacement-response-spectrum format. The data include the 2011 Tohoku Mw9.1 earthquake, which caused building damage of different levels of severity, and the time-varying actual capacity curves were analysed considering earthquakes before and after 2011. Result showed that the initial slope of the capacity curves reflects the amount of damage. The between-building and within-building components of the variability are discussed by comparing a single building and several buildings in the same class for several earthquakes. Finally, the epistemic uncertainty of seismic risk assessment studies is discussed in relation to the selection of a generic capacity model for all buildings in a single class. [ABSTRACT FROM AUTHOR]

Assessing or predicting seismic damage in buildings is an essential and challenging component of seismic risk studies. Machine learning methods offer new perspectives for damage characterization, taking advantage of available data on the characteristics of built environments. In this study, we aim (1) to characterize seismic damage using a classification model trained and tested on damage survey data from earthquakes in Nepal, Haiti, Serbia and Italy and (2) to test how well a model trained on a given region (host) can predict damage in another region (target). The strategy adopted considers only simple data characterizing the building (number of stories and building age), seismic ground motion (macroseismic intensity) and a traffic-light-based damage classification model (green, yellow, red categories). The study confirms that the extreme gradient boosting classification model (XGBC) with oversampling predicts damage with 60% accuracy. However, the quality of the survey is a key issue for model performance. Furthermore, the host-to-target test suggests that the model's applicability may be limited to regions with similar contextual environments (e.g., socio-economic conditions). Our results show that a model from one region can only be applied to another region under certain conditions. We expect our model to serve as a starting point for further analysis in host-to-target region adjustment and confirm the need for additional post-earthquake surveys in other regions with different tectonic, urban fabric and socio-economic contexts. [ABSTRACT FROM AUTHOR]

Over the last two decades, seismic ground motion prediction has been significantly improved thanks to the development of shared, open, worldwide databases (waveform and parametric values). Unlike seismic ground motion, earthquake data recorded in buildings are rarely shared. However, their contribution could be essential for evaluating the performance of structures. Increasing interest in deploying instrumentation in buildings gives hope for new observations, leading to better understanding of behavior. This manuscript presents a flat-file containing information on earthquake responses of instrumented buildings. Herein, we present the structure of the NDE1.0 flat-file containing structural response parameters (i.e. drift ratio, peak top values of acceleration, velocity and displacement, pre- and co-seismic fundamental frequencies) computed for several intensity measures characterizing ground motion (i.e. peak ground values of acceleration, velocity and displacement, spectral values, Arias intensity, strong motion duration, cumulative absolute velocity, destructive potential). The data are from real earthquake recordings collected in buildings over the years. Some building, site and earthquake characteristics are also included (i.e. structural design, shear wave velocity, magnitude, epicentral distance, etc.). This 1.0 version contains 8520 strong motion recordings that correspond to 118 buildings and 2737 events, providing useful information for analyses related to seismic hazard, variability of building responses, structural health monitoring, nonlinear studies, damage prediction, etc. [ABSTRACT FROM AUTHOR]