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Machine Learning for Seismic Fragility Curves by hamid ghorbani is a document available to read on EtoBox.

What is Machine Learning for Seismic Fragility Curves about?

This document discusses applying machine learning techniques to derive seismic fragility curves. It explores using 10 classification methods like logistic regression, support vector machines, random forests, and neural networks on structural response data from multiple strip analyses. It finds random forests and quadratic discriminant analysis performed best at predicting responses from imbalanced and balanced datasets. The paper also details how to derive fragility curves from classification models and exa

Author

hamid ghorbani

Language

EN