Uncertainty quantification in fragility methods for masonry arch bridges: A review

Masonry arch bridges (MABs) represent a critical component of transportation infrastructure worldwide due to their historical value and continued service in modern networks. Despite their relevance, most structural assessments remain deterministic, often neglecting the inherent uncertainties that govern their performance under diverse hazards. This review provides a comprehensive overview of stochastic fragility assessment methods for MABs. The review highlights that current research is largely centered on sensitivity and fragility assessments using simulation-based approaches, particularly Monte Carlo methods, which impose significant computational demands. Surrogate models (SMs), widely used in other structural domains to reduce computational cost, have been applied only in a limited manner to MABs, with kriging-based approaches dominating, while the broader adoption of machine learning methods, such as artificial neural networks, remains scarce. Moreover, fragility analyses commonly rely on the assumption of log-normality, with limited exploration of alternative probabilistic frameworks or stochastic-based surrogate techniques. The findings reveal a clear research gap in the probabilistic treatment of exposure and consequences, as well as in the direct application of advanced SMs for multi-hazard assessments of MABs. This review highlights the need for expanded stochastic-based approaches to better capture uncertainties, support risk-informed decision-making, and improve the management and preservation of MABs. • MABs remain underrepresented in probabilistic studies despite widespread use. • A review of 71 studies shows a focus on seismic analysis with limited multi-hazard. • A framework for MAB fragility integrates UQ, surrogates, and modal calibration. • Stochastic SMs can improve the representation of non-log-normal fragility functions.