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A Machine Learning Approach for Seismic Vulnerability Ranking

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Engineering Applications of Neural Networks (EANN 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1826))

Abstract

Structures often suffer damages as a result of earthquakes, potentially threatening human lives, disrupting the economy and requiring large amounts of monetary reparations. Thus, it is essential for governments to be able to rank a given population of structures according to their expected degree of damage in an earthquake, in order for them to properly allocate the available resources for prevention. In this paper, the authors present a ranking approach, based on Machine Learning (ML) algorithms for pairwise comparisons, coupled with ad hoc ranking rules. The degree of damage of several structures from the Athens 1999 earthquake, along with collected attributes of the building, were used as input. The performance of the ML classification algorithms was evaluated using the respective metrics of Precision, Recall, F1-score, Accuracy and Area Under Curve (AUC). The overall performance was evaluated using Kendall’s tau distance and by viewing the problem as a classification into bins. The obtained results were promising, outperforming currently employed engineering practices. They have shown the capabilities and potential of these models in mitigating the effects of earthquakes on society.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering-Lab of Mathematics and Informatics (ISCE), Democritus University of Thrace, 67100, Xanthi, Greece

    Ioannis Karampinis & Lazaros Iliadis

Authors
  1. Ioannis Karampinis
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  2. Lazaros Iliadis
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Correspondence to Ioannis Karampinis .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  3. University of Leon, León, Spain

    Serafin Alonso

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

  5. University of the West of England, Bristol, UK

    Elias Pimenidis

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Karampinis, I., Iliadis, L. (2023). A Machine Learning Approach for Seismic Vulnerability Ranking. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-34204-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34203-5

  • Online ISBN: 978-3-031-34204-2

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