Jul 2026· International Conference on Smart Communications and Networking· pp. 1-7· 0 citations· 15 references
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.
Automated damage inspection of railway viaducts requires pixel-level identification of structural components and surface damage such as cracking and rebar exposure. A common assumption in bridge inspection is that damage segmentation improves when component information is provided alongside the image. This study tests that assumption on the Tokaido synthetic viaduct dataset using controlled comparisons between segmentation models with and without component information. Both damage and structural component segmentation are evaluated across multiple architectures, and the trained component model is assessed on real viaduct photographs against a baseline model requiring no task-specific training. Under the original random split, explicit component conditioning does not produce a measurable improvement in damage segmentation: all tested strategies remain within 0.008 mean Intersection-over-Union (mIoU) of a baseline without component input, and this null result persists even when component predictions are reliable. Under a leakage-controlled scene-disjoint split, however, the same component-aware variants show a small positive trend (up to +0.019 mIoU over three seeds), so the effect of component conditioning depends on the evaluation protocol. The best unconditioned model reaches 0.569 mIoU for damage segmentation; for real-photo component segmentation, the trained model reaches 0.424 mIoU compared with 0.250 mIoU for the training-free baseline. These results show that multitask benefits reported in bridge inspection do not automatically translate into gains from explicit use of component information on synthetic viaduct data, where damage occurs almost exclusively on columns yet is too sparse for structural element identity to yield more than a marginal localisation gain. The multi-architecture benchmark and the measured real-photo structural transfer gap provide reference baselines for subsequent work on component-aware and transfer-robust inspection.
P. Tauzowski, P. Hołobut, B. Błachowski· Applied Sciences· 0 citations
Earthquakes remain a critical threat to global infrastructure. Recent catastrophic events, such as the 2023 Kahramanmaraş earthquakes in Türkiye and Syria, underscore the vital necessity of rapid, accurate post-disaster building damage evaluations. Structural collapse under seismic loading leads to substantial loss of life and severe economic disruption, particularly in regions dominated by aging building stocks that predate modern seismic design codes. To address the limitations of conventional manual inspections, this study introduces a comprehensive artificial intelligence (AI) framework designed to automate and enhance post-earthquake structural assessments. Leveraging a heterogeneous dataset from the 2021 Haiti earthquake, which includes both categorical building attributes and post-disaster imagery, the proposed approach employs rigorous data preprocessing and exploratory analysis to identify key vulnerability indicators and resolve data inconsistencies. Independent predictive pipelines were developed utilizing state-of-the-art machine learning algorithms for tabular data and deep learning architectures for image analysis. Subsequently, a novel hybrid meta-classifier was implemented to fuse these distinct modalities. By integrating spatial and structural context with direct visual evidence of damage, the hybrid model is successful in estimating structural damage severity. Among all evaluated approaches, this multimodal framework significantly improved predictive reliability. The hybrid model achieved a classification accuracy of 89%, consistently outperforming isolated tabular and image-based models. These findings highlight the efficacy of multimodal data fusion in disaster analytics and suggest that AI-driven hybrid architectures can serve as robust, scalable decision support tools for structural engineers and emergency response agencies.
Abdulrahman Bazbouz, N. Bektaş, Samuel Alexandro Silitonga· Applied System Innovation· 0 citations
This study explores AI-driven image classification to expedite damage evaluation by identifying damaged buildings from post-disaster photos much faster than conventional methods, providing a more detailed understanding of structural integrity across affected areas.
M. Kovačević, F. Đorđević, Đorđe Nedeljković et al.· Bulletin of Earthquake Engin...· 0 citations
Experimental results demonstrate that, compared with mainstream semantic segmentation models, MDeepLab significantly reduces the number of parameters while maintaining high segmentation accuracy, exhibiting promising engineering application value for automated road crack detection.
Guangling Sun, Dongdong Wang, Yanqiu Li et al.· Journal of Real-Time Image P...· 0 citations
A deep transfer learning-based framework is proposed for automatic damage detection and classification in historical buildings and contributes to the development of data-driven decision-support tools for digital documentation and condition assessment in heritage conservation.
A novel lightweight Local-Global Interaction Network (LGINet) for efficient BDD, which achieves the best balance between accuracy and efficiency, outperforming existing methods.
Wei Li, Guorui Ma, Lunjun Fan et al.· The International Archives o...· 0 citations
