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.

利用机器学习算法对R/C建筑的地震响应进行预测的比较性研究，证明了LightGBM算法具有优异的性能和预测能力，对于技术系统和科学方法的采用，在促进地震评估和保护系统的决策方面具有宝贵意义。

机器学习算法对钢筋混凝土建筑地震损伤预测的比较评估