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Abstract:
Assessing or forecasting seismic damage to buildings
is an essential issue for earthquake disaster management.
In this study, we explore the efficacy of several machine
learning models for damage characterization, trained
and tested on the database of damage observed after Italian
earthquakes (the Database of Observed Damage – DaDO).
Six models were considered: regression- and classificationbased
machine learning models, each using random forest,
gradient boosting, and extreme gradient boosting. The structural
features considered were divided into two groups: all
structural features provided by DaDO or only those considered
to be the most reliable and easiest to collect (age,
number of storeys, floor area, building height). Macroseismic
intensity was also included as an input feature. The seismic
damage per building was determined according to the
EMS-98 scale observed after seven significant earthquakes
occurring in several Italian regions. The results showed that
extreme gradient boosting classification is statistically the
most efficient method, particularly when considering the basic
structural features and grouping the damage according to
the traffic-light-based system used; for example, during the
post-disaster period (green, yellow, and red), 68% of buildings
were correctly classified. The results obtained by the
machine-learning-based heuristic model for damage assessment
are of the same order of accuracy (error values were
less than 17 %) as those obtained by the traditional RISK-UE
method. Finally, the machine learning analysis found that the
importance of structural features with respect to damage was
conditioned by the level of damage considered.