Testing machine learning models for heuristic building damage assessment applied to the Italian Database of Observed Damage (DaDO)

In: Natural hazards and earth system sciences: NHESS, Band 23, Heft 10, S. 3199-3218

Abstract

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 classification-based 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.