The use of machine-learning methods for post-earthquake building usability assessment: A predictive model for seismic-risk impact analyses

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dc.citation.articleNumber104033en_US
dc.citation.journalTitleInternational Journal of Disaster Risk Reductionen_US
dc.citation.volumeNumber97en_US
dc.contributor.authorTocchi, Gabriellaen_US
dc.contributor.authorMisra, Sushreyoen_US
dc.contributor.authorPadgett, Jamie E.en_US
dc.contributor.authorPolese, Mariaen_US
dc.contributor.authorDi Ludovico, Marcoen_US
dc.date.accessioned2024-05-08T18:56:09Zen_US
dc.date.available2024-05-08T18:56:09Zen_US
dc.date.issued2023en_US
dc.description.abstractThe assessment of building usability in the aftermath of an earthquake is mostly aimed at post-event emergency management, but it is also valuable for the planning of risk-reduction policies. In the seismic risk assessment field, the development of suitable consequence functions that correlate physical damage to usability and serviceability of structures is crucial to evaluate the expected social and economic losses in a region of interest. Predictive models for usability classification generally are calibrated on empirical data and provide the probability of loss of usability as function of the intensity measure, the building type and the severity of damage attained by the structure. Exploiting the large amount of data available in Italy, a decision tree-based approach is proposed in this study to assess post-earthquake usability of ordinary buildings. Thanks to its high interpretability coupled with reasonable predictive capability _, the selected machine learning algorithm allows investigation of the structural parameters that have a significant impact on building usability, while also accounting for the traditionally neglected uncertainty of subjective decisions. Finally, to show the potential of the proposed usability consequence models, a large-scale risk analysis is carried out to evaluate the spatial distribution of expected building-usability losses over time.en_US
dc.identifier.citationTocchi, G., Misra, S., Padgett, J. E., Polese, M., & Di Ludovico, M. (2023). The use of machine-learning methods for post-earthquake building usability assessment: A predictive model for seismic-risk impact analyses. International Journal of Disaster Risk Reduction, 97, 104033. https://doi.org/10.1016/j.ijdrr.2023.104033en_US
dc.identifier.digital1-s20-S2212420923005137-mainen_US
dc.identifier.doihttps://doi.org/10.1016/j.ijdrr.2023.104033en_US
dc.identifier.urihttps://hdl.handle.net/1911/115655en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleThe use of machine-learning methods for post-earthquake building usability assessment: A predictive model for seismic-risk impact analysesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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