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Implementation of Emerging Technologies in Seismic Risk Estimation

Vertaalde titel van de bijdrage: Toepassing van opkomende technologieën bij het schatten van seismische risico's

    Onderzoeksoutput: Contribution to conference proceedingAcademicpeer review

    Samenvatting

    ''Ever increasing population in seismically active urban areas, aging building stock, and expansion of urbanization to previously agricultural lands with soft soil deposits render the protection of human lives against earthquake disasters extremely more difficult by the time. Although much effort is put in further improving the current seismic design practices for new buildings, recent earthquakes show us, again and again, that life losses occur in older and much more vulnerable structures. Finding those substandard, collapse-vulnerable buildings before a destructive earthquake is like finding a needle in a haystack. It is clear that the problem in hand cannot be addressed with the existing, and mostly old-fashioned tools anymore.

    This manuscript focuses on how the emerging technologies, such as Artificial Intelligence, image processing, and data sciences in general, can be implemented as useful tools for conducting an urban scale seismic risk assessment while estimating the risk for every individual building. A review of the available technologies is given for the exposure component. Furthermore, a novel method of estimating the vulnerability of individual buildings, based on autoregressive machine learning algorithms, is presented. The manuscript discusses that the technological advancement is mature enough to radically alter how the earthquake risk is estimated.''
    Vertaalde titel van de bijdrageToepassing van opkomende technologieën bij het schatten van seismische risico's
    Originele taal-2English
    TitelProgresses in European Earthquake Engineering and Seismology
    SubtitelThird European Conference on Earthquake Engineering and Seismology - Bucharest, 2022
    UitgeverijSpringer Nature Switzerland AG
    Pagina's279-294
    Aantal pagina's16
    ISBN van elektronische versie2524-3438
    ISBN van geprinte versie2524-342X
    DOI's
    StatusPublished - 25 aug. 2022

    Publicatie series

    ReeksSpringer Proceedings in Earth and Environmental Sciences (SPEES)
    ISSN2524-3438

    Keywords

    • kunstmatige intelligentie
    • machinaal leren
    • seismisch risico

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