SAFENET: Image-Based Infrastructure Classification of Bridges and Viaducts for Seismic Risk Assessment

Abstract

Transportation networks are critical for emergency response after earthquakes, but national-scale bridge and viaducts inventories often lack vulnerability-related attributes such as material and structural system. This paper presents an image-based approach, developed within the SAFENET project, to automatically classify bridges/viaducts according to a practical Material-Structure (MS) labeling scheme that reduces sparsity compared to finer taxonomies that also include construction period. Using a Portuguese bridge image dataset, we compare three visual model families for Material-Structure classification: a ResNet-50 convolutional baseline, a self-supervised vision transformer (DINOv2-Large), and a contrastive vision encoder (CLIP). Models are evaluated with a strict 5-by-5 Nested CrossValidation (NCV) protocol with bridge-level splits to prevent information leakage across train and test sets. Results show that DINOv2 achieves the best overall performance, with a mean accuracy of 0.903, a macro-F1 of 0.773, and a weighted-F1 of 0.897, outperforming ResNet-50 and CLIP especially on minority classes. These findings support the use of self-supervised vision transformers to enrich bridge inventories from imagery and to provide scalable inputs for regional seismic risk assessment.