# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 7 Conclusions  
This study introduced a novel approach that applies CNN deep learning to both time-history and SVD responses for identifying damage in submerged structure-foundation systems. Firstly, various foundation damage levels were simulated in a lab-scale caisson-foundation system, and corresponding time-history responses were recorded using accelerometers. Secondly, the 1-D CNN deep learning model was trained using time-history responses. Thirdly, the 1-D CNN model was trained using the SVD responses derived from time-history responses. Finally, the trained CNN models were implemented to evaluate the damage within the system's foundation under noise contamination and partially untrained data. Four main conclusions of the study are summarized as follows:  
1. The CNN deep learning model trained using time-history and SVD responses successfully classified the foundation damage of the caisson system. 2. The t-test evaluation on performance metrics indicated that the CNN model trained using SVD responses outperformed the CNN model trained using time-history responses, particularly when dealing with untrained noise levels. The findings under-score the effectiveness of using additional vibration features such as SVD data. 3. The performance of the CNN model was maintained with partially untrained cases. The CNN models trained using time-history and SVD responses successfully classified the untrained damage levels as the most similar trained damage levels. This outcome demonstrates the robustness and potential effectiveness of the CNN model under in situ conditions. 4. Integrating the time-history and SVD responses strengthened the damage classification results and provided additional insights for investigators. The proposed approach can be directly integrated into existing monitoring systems as a post-processing and decision-making phase. The CNN deep learning technique requires no additional hardware and only a one-time setup, with the ability to be automatically updated as new data become available. By reducing reliance on manual decision-making, this approach mitigates risks associated with human error and offers long-term benefits by saving expert labor costs. By collaborating with industrial partners, vibration data for training can be collected directly from in situ damaged caissons. The CNN model is continuously updated with variations in the shape, location, and severity of foundation  
Figure 29: Accuracy of CNN models with untrained cases. damage. The ongoing refinement and expansion of the dataset enhances the reliability of the CNN model, improving its accuracy and robustness in real-world conditions. Despite the effectiveness of the developed method for damage detection and monitoring, some aspects need to be resolved. Firstly, the feasibility of the method was proven with experiments on a lab-scaled caisson system. It now needs to be verified with the in situ caisson-foundation systems. Secondly, future investigations should consider different types of noise and the influence of environmental factors. Several other data augmentation techniques such as shifting, amplitude scaling, and window slicing could be applied besides Gaussian noise contamination. Thirdly, the verification of the method should be extended to other machine learning models. Although integrating various vibration features has proven effective for the CNN model, further evaluation is necessary to assess its compatibility with alternative machine learning models. Finally, the investigation showed the dependence of CNN models on the availability of vibration data (i.e., time-history and SVD responses). For real caisson systems, simulating damage to submerged foundations is challenging. Additional research on generating training data through indirect methods such as numerical models and pseudo-damage simulation is needed. Conceptualization, J.-T.K. and N.-L.P; methodology, analysis, writing--original draft, N.-L.P.; data recording, N.-L.P. and Q.-B.T. ; investigation and data curation, N.-L.P.; writing--review and editing, J.-T.K.