  Seismic assessment of buildings and determination of their structural damage
is at the forefront of modern scientific research. Since now, several
researchers have proposed a number of procedures, in an attempt to estimate the
damage response of the buildings subjected to strong ground motions, without
conducting time-consuming analyses. These procedures, e.g. construction of
fragility curves, usually utilize methods based on the application of
statistical theory. In the last decades, the increase of the computers' power
has led to the development of modern soft computing methods based on the
adoption of Machine Learning algorithms. The present paper attempts an
extensive comparative evaluation of the capability of various Machine Learning
methods to adequately predict the seismic response of R/C buildings. The
training dataset is created by means of Nonlinear Time History Analyses of 90
3D R/C buildings with three different masonry infills' distributions, which are
subjected to 65 earthquakes. The seismic damage is expressed in terms of the
Maximum Interstory Drift Ratio. A large-scale comparison study is utilized by
the most efficient Machine Learning algorithms. The experimentation shows that
the LightGBM approach produces training stability, high overall performance and
a remarkable coefficient of determination to estimate the ability to predict
the buildings' damage response. Due to the extremely urgent issue, civil
protection mechanisms need to incorporate in their technological systems
scientific methodologies and appropriate technical or modeling tools such as
the proposed one, which can offer valuable assistance in making optimal
decisions.

Machine Learning;Artificial Intelligence