Triplet Loss-Based Concrete Crack Verification for Structural Health Monitoring and Digital Twin Applications

This paper proposes a novel deep-learning approach for verifying concrete cracks, leveraging a Siamese network with triplet loss. Inspired by FaceNet, the model is trained on an augmented “Crack dataset” containing rotations and shearing to enhance its ability to distinguish subtle crack variations. This method facilitates the evolution-centric monitoring of cracks, enabling early detection of potential failures and contributing to the development of digital twins for structural health moni-toring. The model achieves high accuracy (97.36 %) and precision (95.77 %) in verifying crack patterns, demonstrating its potential for improving infrastructure maintenance and safety.