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ORIGINAL RESEARCH article

Front. Built Environ.
Sec. Wind Engineering and Science
Volume 10 - 2024 | doi: 10.3389/fbuil.2024.1485388
This article is part of the Research Topic NHERI 2015-2025: A Decade of Discovery in Natural Hazards Engineering View all articles

Automated Extraction and Summarization of Wind Disaster Data Using Deep Learning Models, with Extended Applications to Seismic Events

Provisionally accepted
  • Virginia Tech, Blacksburg, United States

The final, formatted version of the article will be published soon.

    The United States experiences more extreme wind events than any other country due to its diverse climate and geographical features. While these events pose significant threats to society, they generate substantial data that can support researchers and disaster managers in resilience planning. This research leverages such data to develop a framework that automates the extraction and summarization of structural and community damage information from reconnaissance reports.The framework utilizes the large Bidirectional and Auto-Regressive Transformers model (BARTlarge), a deep learning model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) and Cable News Network (CNN) Daily Mail datasets for these tasks. Specifically, the BARTlarge MNLI model employs zero-shot text classification to identify sentences containing relevant impact information based on user-defined keywords, minimizing the need for fine-tuning the model on wind damage-related datasets. Subsequently, the BART-large CNN model generates comprehensive summaries from these sentences, detailing structural and community damage.The performance of the framework is assessed using reconnaissance reports published by the Structural Extreme Events Reconnaissance (StEER), part of the Natural Hazards Engineering Research Infrastructure (NHERI) network. Particularly, the initial evaluation is conducted with the 2022 Hurricane Ian report. This is followed by a verification of the BART-large MNLI model's capability to extract impact sentences, utilizing the 2023 Hurricane Otis report. Finally, the versatility of the framework is illustrated through an extended application to the 2023 T ürkiye earthquake sequences report, highlighting its adaptability across diverse disaster contexts.

    Keywords: Wind disaster and resilience, Structural damage, Community damage, Zero-shot text classification, text mining

    Received: 23 Aug 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Pham and Arul. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Huy Pham, Virginia Tech, Blacksburg, United States
    Monica Arul, Virginia Tech, Blacksburg, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.