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What is Seismic Damage Classification with ML about?

This paper evaluates the effectiveness of various machine learning (ML) methods in classifying the seismic damage potential of reinforced concrete (R/C) buildings using a dataset of 5850 samples generated from nonlinear time history analyses. It highlights the importance of hyperparameter tuning through Bayesian Optimization to enhance the reliability of ML models, with the Support Vector Machine - Gaussian Kernel algorithm yielding the most accurate results. The study aims to improve the classification of

Author

Aftab Ahmad

Language

EN