Machine-learning-aided regional post-seismic usability prediction of buildings: 2016–2017 Central Italy earthquakes

Aloisio, Angelo; Rosso, Marco Martino; Di Battista, Luca; Quaranta, Giuseppe

doi:10.1016/j.jobe.2024.109526

Journal of Building Engineering

2024

DOI: 10.1016/j.jobe.2024.109526

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Machine-learning-aided regional post-seismic usability prediction of buildings: 2016–2017 Central Italy earthquakes

Angelo Aloisio,

Marco Martino Rosso,

Luca Di Battista

et al.

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2024

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Cited by 2 publications

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Assessment of the vulnerability of regional buildings considering low-temperature fields and seismic intensity measures

2024

Case Studies in Construction Materials

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Assessment of the vulnerability of regional buildings considering low-temperature fields and seismic intensity measures

2024

Case Studies in Construction Materials

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Machine Learning Ensemble Methodologies for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints

Karabini,

Karampinis,

Rousakis

et al. 2024

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One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle shear failure of the joint, which can lead to sudden collapse and loss of human lives. To this end, it is imperative to be able to predict the failure mode of RC joints for a large number of structures in a building stock. In this research effort, various ensemble machine learning algorithms were employed to develop novel, robust classification models. A dataset comprising 486 measurements from real experiments was utilized. The performance of the employed classifiers was assessed using Precision, Recall, F1-Score, and overall Accuracy indices. N-fold cross-validation was employed to enhance generalization. Moreover, the obtained models were compared to the available engineering ones currently adopted by many international organizations and researchers. The novel ensemble models introduced in this research were proven to perform much better by improving the obtained accuracy by 12–18%. The obtained metrics also presented small variability among the examined failure modes, indicating unbiased models. Overall, the results indicate that the proposed methodologies can be confidently employed for the prediction of the failure mode of RC joints.

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Machine-learning-aided regional post-seismic usability prediction of buildings: 2016–2017 Central Italy earthquakes

Cited by 2 publications

References 53 publications

Assessment of the vulnerability of regional buildings considering low-temperature fields and seismic intensity measures

Assessment of the vulnerability of regional buildings considering low-temperature fields and seismic intensity measures

Machine Learning Ensemble Methodologies for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints

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