Machine learning model for building seismic peak roof drift ratio assessment

The peak roof drift ratio is one of the most important engineering
parameters to describe the expected seismic damage in a building. A predictive model of the drift ratio was developed using a
machine learning approach (Gaussian process regression model)
on a dataset of approximately 11,800 records from 34 monitored
buildings in Japan. Four predictors for ground motion and three
predictors for building vulnerability are used in the machinelearning modelling. The residual analysis shows a reduction of
50% compared to the state of the art. The Gaussian process
regression model is applied in a second analysis on an original
dataset of approximately 4,500 records for 127 monitored buildings in Italy. A satisfactory comparison emerges by comparing the
drift ratio prediction map with the observed damage pattern produced by satellite imagery for a test site in central Italy after the
2009 earthquake. The drift ratio map plays an important role in
the simulation of an earthquake scenario at regional scale, which
is needed by Civil Protection for emergency planning and management activities.