Leveraging transformer models for seismic fragility assessment of non-engineered masonry structures in Malawi

Harirchian, Ehsan and Novelli, Viviana Iris ORCID: https://orcid.org/0000-0003-3809-7170 2025. Leveraging transformer models for seismic fragility assessment of non-engineered masonry structures in Malawi. Infrastructures 10 (11) , 279. 10.3390/infrastructures10110279

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Abstract

Assessing seismic vulnerability is a critical step in evaluating the resilience of existing buildings, and fragility curves are widely used to quantify the probability of damage under varying levels of seismic intensity. However, traditional methods for generating these curves often rely on generalized assumptions that may not accurately capture the seismic behavior of diverse building types within a region. This limitation is particularly evident for non-engineered masonry buildings, which typically lack standardized designs. Their irregular and informal construction makes them difficult to assess using conventional approaches. Transformer-based models, a type of machine learning (ML) technique, offer a promising alternative. These models can identify patterns and relationships in available data, making them well suited for developing seismic fragility curves with improved efficiency and accuracy. While transformers are relatively new to civil engineering, their application to seismic fragility assessment has been largely unexplored. This study presents a pioneering effort to apply transformer models for deriving fragility curves for non-engineered masonry buildings. A comprehensive dataset of 646 masonry buildings observed in Malawi is used to train the models. The transformers are trained to predict the probability of four damage states: Light Damage, Severe Damage, Near Collapse, and Collapse based on Peak Ground Acceleration (PGA). The performance of the transformer-based approach is compared with other ML methods, demonstrating its strong potential for more efficient and accurate seismic fragility assessment. Future work could adopt the proposed methodology and extend the approach by incorporating larger datasets, additional regional contexts, and alternative ML techniques to further enhance predictive performance.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Publisher:	MDPI
ISSN:	2412-3811
Date of First Compliant Deposit:	30 October 2025
Date of Acceptance:	16 October 2025
Last Modified:	03 Nov 2025 15:00
URI:	https://orca.cardiff.ac.uk/id/eprint/182011

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