Inspecting Bridges and Critical Infrastructure: An AI and Blockchain Approach
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56776-6_12
Reference11 articles.
1. Arafin, P., Billah, A.M., Issa, A.: Deep learning-based concrete defects classification and detection using semantic segmentation. Struct. Health Monit. 23(1), 383–409 (2024). https://doi.org/10.1177/14759217231168212
2. Chhetri, B., Gopali, S., Olapojoye, R., Dehbash, S., Namin, A.S.: A survey on blockchain-based federated learning and data privacy (2023)
3. Sales da Cunha, B., das Chagas Moura, M., Souto Maior, C., Cláudia Negreiros, A., Didier Lins, I.: A comparison between computer vision- and deep learning-based models for automated concrete crack detection. Proc. Inst. Mech. Eng. Part O: J. Risk and Reliabil. 237(5), 994–1010 (2023). https://doi.org/10.1177/1748006X221140966
4. Das, A., Ichi, E., Dorafshan, S.: Image-based corrosion detection in ancillary structures. Infrastructures 8(4) (2023). https://doi.org/10.3390/infrastructures8040066
5. Galdelli, A., et al.: A novel remote visual inspection system for bridge predictive maintenance. Remote Sens. 14(9) (2022). https://doi.org/10.3390/rs14092248
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