Despite the emergence of recent advancements, machine learning (ML) based methods for estimating the fragility curves of structures through probabilistic ground motion selection techniques pose a challenge due to the computational cost associated with data preparation. The primary aim of this research is to reduce the data preparation time involved in estimating the fragility curves of structures using a ground motion selection approach that considers earthquake magnitude, distance from the seismic source, and shear wave velocity of soil as essential parameters. To achieve this objective, ML algorithms are employed to calculate the fragility curves of various reinforced concrete moment resisting (RC/MR) frames with different periods, utilizing codebased and generalized conditional intensity measure (GCIM) ground motion selection methods. The SMOTE-ENN technique, a data resampling method, is used to balance the training data for the ML algorithms to address potential bias resulting from imbalanced training data. To validate the fragility curves obtained through ML, analytical fragility curves are derived for a specific structure at three damage levels and compared with the ML curves. The results demonstrate that the percentage of the enclosed area between the analytical and ML curves, relative to the area under the analytical curve, is below 10 % and 5 % for the GCIM and code-based methods, respectively. Fragility curves were generated for various structures, including regular and irregular buildings, to investigate the generalizability. Results indicate that, for the specific structures analyzed in this study, excluding torsional ones, the structure's period is a sufficient structural feature for generating fragility curves.

来源平台：JOURNAL OF BUILDING ENGINEERING