Implementation of Emerging Technologies in Seismic Risk Estimation

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    Abstract

    ''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.''
    Translated title of the contributionToepassing van opkomende technologieën bij het schatten van seismische risico's
    Original languageEnglish
    Title of host publicationProgresses in European Earthquake Engineering and Seismology
    Subtitle of host publicationThird European Conference on Earthquake Engineering and Seismology - Bucharest, 2022
    PublisherSpringer Nature Switzerland AG
    Pages279-294
    Number of pages16
    ISBN (Electronic)2524-3438
    ISBN (Print)2524-342X
    DOIs
    Publication statusPublished - 25 Aug 2022

    Publication series

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

    Keywords

    • artificial intelligence
    • machine learning
    • seismic risk

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