Evaluating the likelihood of damage in buildings undergoing earthquake actions is a difficult and timeconsuming task. In the context of Performance-Based Earthquake Engineering (PBEE), an intensity measure (IM) provides a link between the probabilistic seismic hazard analysis and the probabilistic structural response analysis [1-2]. The purpose of this study is to develop a structural damage classifier and improve current prediction on the basis of a given intensity measure and different supervised machine learning algorithms [3]: Support-Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF).
A machine learning approach to the seismic fragility assessment of buildings
Rocchi A.;Chiozzi A.;Nale M.;Benvenuti E.
2021
Abstract
Evaluating the likelihood of damage in buildings undergoing earthquake actions is a difficult and timeconsuming task. In the context of Performance-Based Earthquake Engineering (PBEE), an intensity measure (IM) provides a link between the probabilistic seismic hazard analysis and the probabilistic structural response analysis [1-2]. The purpose of this study is to develop a structural damage classifier and improve current prediction on the basis of a given intensity measure and different supervised machine learning algorithms [3]: Support-Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF).File in questo prodotto:
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