Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence

Neda Asgarkhani; Farzin Kazemi; Robert Jankowski

doi:10.1007/978-3-031-63759-9_47

Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence

Abstrakt

This research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story steel structures considering four soil type effects. To prepare the dataset, 3584 incremental dynamic analysis (IDA) were performed on 64 structures. The research employs 6-, and 8-story structures to validate the AL-Ensemble ML model's effectiveness, showing it achieves the highest accuracy among conventional ML models, with an R2 of 98.4%. Specifically, it accurately predicts the RID of floor levels in a 6-story structure with an accuracy exceeding 96.6%. Additionally, the programming code identifies the specific damaged floor level in a building, facilitating targeted local retrofitting instead of retrofitting the entire structure promising a reduction in retrofitting costs while enhancing prediction accuracy.

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Słowa kluczowe

COMPUTATIONAL METHOD, ACTIVE LEARNING, ENSEMBLE MACHINE-LEARNING MOD-EL, RETROFITTING STRUCTURES, MAINSHOCK-AFTERSHOCK SEQUENCE.

Informacje szczegółowe

Kategoria:

Aktywność konferencyjna

Typ:

publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)

Język:

angielski

Rok wydania:

2024

Opis bibliograficzny:

Asgarkhani N., Kazemi F., Jankowski R.: Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence// / : , 2024,

DOI: