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

Aug 3, 2026

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Study develops machine learning models to predict post-seismic building usability across earthquake-prone regions, using field data from the 2016–2017 Central Italy earthquakes. Multiple algorithms are evaluated, including k-nearest neighbors, support vector machines with different kernels, decision trees, random forests, neural networks, boosting, probabilistic classifiers, and discriminant and regression-based methods. Building attributes and seismic intensity measures are used as inputs, while class imbalance is addressed through principal component analysis and synthetic minority oversampling. Model performance is assessed using multiple metrics, with robustness under uncertainty, feature importance, and the effect of usability-class clustering analyzed, and the full workflow explained to guide similar applications.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages52

Reading Time2 h 11 min

AccessFree to download

Abstract

Introduction

Territorial-scale damage and usability assessment

Mechanics-based vs data-driven approaches

Prior work on post-seismic usability prediction

POLITECNICO DI TORINO Repository ISTITUZIONALE Machine-learning-aided regional post-seismic usability prediction of buildings: 2016–2017 Central Italy earthquakes Original Machine-learning-aided regional post-seismic usability prediction of buildings: 2016–2017 Central Italy earthquakes / Aloisio, A. ; Rosso, M. M. ; Di Battista, L. ; Quaranta, G.. -In: JOURNAL OF BUILDING ENGINEERING. -ISSN 2352- 7102. -91:(2024), pp. 1-33. [10 . 1016/j.jobe.2024. 109526] Availability: This version is available at: 11583/2991633 since: 2024-08-09T17:14:37Z Publisher: Elsevier Published DOI:10.1016/j.jobe.2024.109526 Terms of use: This article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository Publisher copyright Elsevier postprint/Author's Accepted Manuscript © 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by-nc-nd/4 .0/ .The final authenticated version is available online at: [http://dx.doi.org/10.1016/j.jobe.2024.109526](http://dx.doi.org/10.1016/j.jobe.2024.109526) (Article begins on next page) 21 February 2026 Machine-learning-aided regional post-seismic usability prediction of buildings: 2016-2017 Central Italy earthquakes Angelo Aloisio∗a , Marco Martino Rossob , Luca Di Battistac , Giuseppe Quarantad a Department of Civil, Construction-Architectural and Environmental Engineering, University of L’Aquila, L’Aquila, Italy b Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Turin, Italy c Ministry of Infrastructure and Transport, Public Works Department of Lazio, Abruzzo, and Sardinia, L’Aquila, Italy d Department of Structural and Geotechnical Engineering, Sapienza University of Rome, Rome, Italy Abstract This study addresses the development of machine learning models for predicting the post-seismic buildings usability within regions prone to frequent earthquakes. The analysis leverages on field data from 2016- 2017 Central Italy earthquakes. Several machine learning techniques are employed for this task, namely K-nearest neighbors, linear support vector machine, radial basis function support vector machine, decision tree, random forest, neural network, adaptive boosting, naive Bayes, quadratic discriminant analysis, logistic regression, and linear discriminant analysis. The input variables include both building attributes and seismic intensity measures. Since the database turns out to be strongly imbalanced, the potential influence of two preprocessing techniques is examined, namely principal component analysis and synthetic minority oversampling technique. Several metrics are considered to evaluate the performance of the resulting predictive machine learning models. Moreover, this study investigates the optimal machine learning model’s robustness against uncertainties, quantifies the importance of its features, and investigates how usability classes clustering can impact its performance. Every step of the implemented procedure is deeply explained and discussed to provide useful guidelines for similar applications. Keywords: Building, Classification, Earthquake, Feature importance, Machine learning, Usability 1. Introduction The goals of damage and usability assessment at territorial scale of buildings exposed to seismic hazard are twofold. In fact, they are essential for a rational elaboration of regional or national seismic risk mitigation ∗ Corresponding author Email address: [angelo.aloisio1@univaq.it](angelo.aloisio1@univaq.it) (Angelo Aloisio∗ ) Preprint submitted to Journal of Building Engineering March 30, 2024 plans through scenario simulations and cost-benefit evaluations. Furthermore, damage and usability assess- 5 ment are also among the priority tasks in the aftermath of an earthquake. In such case, they support the rapid losses quantification as well as t

FAQs

What is the document’s main objective?

To develop and evaluate machine learning models that predict post-seismic building usability at regional scale using earthquake-related field data.

Which data and input variables are used for the predictions?

The models use building attributes together with seismic intensity measures derived from the 2016–2017 Central Italy earthquakes.

How does the study address class imbalance in the dataset?

It examines two preprocessing techniques—principal component analysis and synthetic minority oversampling—to mitigate imbalance effects before training and evaluating models.

Aug 3, 2026

Upload by Levi