Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake

Assessing post-seismic damage on an urban/regional scale remains relatively difficult
owing to the significant amount of time and resources required to acquire informa-
tion and conduct a building-by-building seismic damage assessment. However, the
application of new methods based on artificial intelligence, combined with the
increasingly systematic availability of field surveys of post-seismic damage, has pro-
vided new perspectives for urban/regional seismic damage assessment. This study
analyzes the effectiveness and relevance of a number of machine learning techniques
for analyzing spatially distributed seismic damage after an earthquake at the regional
scale. The basic structural parameters of a portfolio of buildings and the post-
earthquake damage surveyed after the Nepal 2015 earthquake are analyzed and com-
bined with macro-seismic intensity values provided by the United States Geological
Survey ShakeMap tool. Among the methods considered, the random forest regres-
sion model provides the best damage predictions for specified ground motion inten-
sity values and structural parameters. For traffic-light-based damage classification
(three classes: green-, amber-, and red-tagged buildings based on post-earthquake
damage grade), a mean accuracy of 0.68 is obtained. This study shows that restricting
learning to basic features of buildings (i.e. number of stories, height, plinth area, and
age), which could be readily available from authoritative databases (e.g. national cen-
sus) or field-surveyed databases, yields a reliable prediction of building damage (4 fea-
tures/3 damage grade accuracy: 0.64).