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Abstract
This thesis presents a novel deep learning approach for autonomously detecting structural vulnerabilities in urban infrastructure using two-dimensional visual data. By integrating spatial reconstruction algorithms with defect-recognition neural networks, the proposed method transitions flat imagery into semantically analyzed three-dimensional models. The framework effectively identifies topological anomalies, surface deformations, and micro-fissures. The conceptual findings demonstrate enhanced diagnostic precision and computational efficiency, offering a scalable tool for preemptive architectural maintenance and smart city hazard mitigation.
References
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