Version 1
: Received: 13 September 2024 / Approved: 15 September 2024 / Online: 16 September 2024 (09:51:14 CEST)
How to cite:
Estêvão, J. M. C. Effectiveness of Generative AI for Post-Earthquake Damage Assessment. Preprints2024, 2024091155. https://doi.org/10.20944/preprints202409.1155.v1
Estêvão, J. M. C. Effectiveness of Generative AI for Post-Earthquake Damage Assessment. Preprints 2024, 2024091155. https://doi.org/10.20944/preprints202409.1155.v1
Estêvão, J. M. C. Effectiveness of Generative AI for Post-Earthquake Damage Assessment. Preprints2024, 2024091155. https://doi.org/10.20944/preprints202409.1155.v1
APA Style
Estêvão, J. M. C. (2024). Effectiveness of Generative AI for Post-Earthquake Damage Assessment. Preprints. https://doi.org/10.20944/preprints202409.1155.v1
Chicago/Turabian Style
Estêvão, J. M. C. 2024 "Effectiveness of Generative AI for Post-Earthquake Damage Assessment" Preprints. https://doi.org/10.20944/preprints202409.1155.v1
Abstract
After an earthquake, assessing the seismic vulnerability of buildings is essential for prioritizing emergency response, guiding reconstruction, and ensuring public safety. Accurate and timely evaluation of structural damage plays a vital role in mitigating further risks and facilitating in-formed decision-making during disaster recovery. This study investigates the performance of various Generative Artificial Intelligence (GAI) models, developed by different companies with diverse model sizes and context windows, in analysing post-earthquake images. The primary objective was to evaluate the models' effectiveness in classifying structural damage according to the EMS-98 scale (with 5 levels of damage), which ranges from minor damage to total destruction. For masonry buildings, the correct classification rates varied widely across models, from 28.6% to 64.3%, with mean damage grade errors ranging from 0.50 to 0.79. In the case of reinforced concrete buildings, correct classification rates ranged from 37.5% to 75.0%, with mean damage grade errors between 0.50 and 0.88. The use of fine-tuning could probably improve the results substantially. Improving the accuracy of GAI models could significantly reduce the time and resources needed to assess post-earthquake damage, namely when comparing with more traditional approaches. The results achieved thus far demonstrate the promise of GAI models for rapid, automated, and ac-curate damage evaluation, which is critical for expediting decision-making in disaster response scenarios.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
