A Defect-Informed Smart Prioritisation Framework for Landslide Risk Assessment of Masonry Arch Bridges

Multi-risk assessment frameworks for existing bridges commonly evaluate landslide hazard primarily through geomorphological evidence and inventory-based data, while the actual structural response of the bridge is typically addressed only within the structural safety domain. This separation may lead to a systematic underestimation of risk in the presence of slow-moving landslides, which impose cumulative horizontal actions over long timescales and often lack clear or persistent surface indicators. Masonry arch bridges, owing to their hyperstatic configuration, tend to develop progressive and observable deformation and cracking patterns before reaching critical conditions. When correctly interpreted, these patterns can provide valuable diagnostic evidence of ongoing slope–structure interaction and support early-stage risk reduction strategies. This study proposes a defect-informed prioritisation framework in which structural damage patterns observed during inspections are explicitly integrated with conventional landslide hazard descriptors to refine landslide risk classification and intervention ranking by urgency. A systematic diagnostic scheme is introduced, classifying landslide-related defects into macroscopic, intermediate and contextual indicators. The workflow combines structural observations with territorial, geological, and inventory-based datasets to derive priority indices reflecting both environmental hazard conditions and the observed structural response. The approach is applied to selected case studies of masonry arch bridges in landslide-prone contexts. Numerical modelling is finally employed as a scenario-consistency tool to verify whether the observed damage configurations are compatible with plausible landslide-induced mechanisms and alternative causes such as local scour. The results demonstrate that defect-based assessment can reduce subjectivity and improve the reliability of landslide risk prioritisation, supporting risk-informed decision-making within multi-risk management frameworks for existing infrastructure.