This study presents a comprehensive multimodal deep learning framework for predicting lifespan degradation in concrete bridges caused by iron oxidation. The proposed system integrates YOLOv8 for surface-level crack detection and ResNet50 for deep image feature extraction, combined with structurally significant tabular data such as crack geometry, material composition, environmental factors, and corrosion indicators. Addressing limitations in current approaches-including dataset scarcity, lack of multimodal integration, and high cost of sensor-based inspection-the framework employs a hybrid architecture to estimate three critical outputs: degradation score, condition class, and remaining life of the bridge. To overcome data limitations, synthetic tabular features were generated using AI-based simulations aligned with visual inputs. The system was trained with extensive resources: 200 epochs for YOLOv8 and 50+ epochs for the tabular model, followed by k-fold cross-validation (MAE: 3.48, R²: 0.89) to validate generalization. Despite challenges in detection accuracy (mAP@0.5: 0.0101), the classification component achieved an AUC of 0.98, confirming robustness in condition prediction. Comparative evaluations demonstrate that YOLOv8 and ResNet50 provide the best trade-off between accuracy, efficiency, and deployment readiness. The proposed model, further enhanced with attention mechanisms and future transformer-based extensions, offers a scalable, low-cost alternative to traditional sensor-driven monitoring and contributes to more proactive, data-driven maintenance of aging bridge infrastructure.
Patel et al. (2025) studied this question.