  Developing a rapid, but also reliable and efficient, method for classifying
the seismic damage potential of buildings constructed in countries with regions
of high seismicity is always at the forefront of modern scientific research.
Such a technique would be essential for estimating the pre-seismic
vulnerability of the buildings, so that the authorities will be able to develop
earthquake safety plans for seismic rehabilitation of the highly
earthquake-susceptible structures. In the last decades, several researchers
have proposed such procedures, some of which were adopted by seismic code
guidelines. These procedures usually utilize methods based either on simple
calculations or on the application of statistics theory. Recently, the increase
of the computers' power has led to the development of modern statistical
methods based on the adoption of Machine Learning algorithms. These methods
have been shown to be useful for predicting seismic performance and classifying
structural damage level by means of extracting patterns from data collected via
various sources. A large training dataset is used for the implementation of the
classification algorithms. To this end, 90 3D R/C buildings with three
different masonry infills' distributions are analysed utilizing Nonlinear Time
History Analysis method for 65 real seismic records. The level of the seismic
damage is expressed in terms of the Maximum Interstory Drift Ratio. A large
number of Machine Learning algorithms is utilized in order to estimate the
buildings' damage response. The most significant conclusion which is extracted
is that the Machine Learning methods that are mathematically well-established
and their operations that are clearly interpretable step by step can be used to
solve some of the most sophisticated real-world problems in consideration with
high accuracy.
