## 5 Conclusions  
In this research, we assessed the performance of advanced convolutional neural network (CNN) architectures, including ResNet-18/50/101, ResNeXt-50/101 and EfficientNet-B3, for the classification of bridge damage using the CODEBRIM dataset. The use of the full fine-tuning approach, where all layers of the models, including the convolutional layers, were trained, ensured that the models could fully learn the specific characteristics of bridge defects without being limited to pre-learned features from IMAGENET. From the Results and Discussion chapter (Section 4), the following conclusions can be derived:  
During the training phase, ResNeXt-50, ResNeXt-101 and EfficientNet-B3 showed faster convergence and reduced final error rates. The ResNeXt models, which used grouped convolutions, outperformed their ResNet counterparts in classification, particularly for visually identical faults. The final test results showed high classification accuracy in single-label defect scenarios, but overlapping flaws remained an issue, with models occasionally identifying only one fault while missing others. Figure 11: Random images of the testing results with real and predicted values. ResNeXt-101 and EfficientNet-B3 outperformed the other models examined, with ResNeXt-101 achieving the best Precision and Recall, showing its ability to detect flaws accurately. The top-performing models were EfficientNet-B3 and ResNeXt-101, which demonstrated higher accuracy and feature extraction capabilities. EfficientNet-B3 had the best-Balanced Accuracy (0.935), beating the ResNet-based models, particularly in difficult defect categories such as corrosion and spalling. These findings were verified by an AUC-ROC analysis, which revealed that EfficientNet-B3 consistently maintained an AUC greater than 0.96 across all the fault categories. Notably, EfficientNet-B3's background classification was nearly faultless, with 1.000 Recall and a 0.943 F1-Score, proving its ability to distinguish between areas with structural damage and those without defects. The exceptional performance of ResNeXt-101 and EfficientNet-B3 indicates their potential for practical use in the evaluation of civil infrastructure. Bridge maintenance can be made more accurate and efficient by incorporating these models into automated monitoring systems. With its excellent accuracy and computational economy, EfficientNet-B3 is the most effective model and a good choice for real-world defect classifications. ResNeXt-101 is a good substitute in the meantime, especially for flaws that are apparent to the naked eye, including corrosion stains and efflorescence. Even with excellent Precision, addressing overlapping flaws and uncommon defect classes still presents difficulties. There is a need for more improvements in feature extraction and multi-label classification, as the models occasionally had trouble distinguishing between complicated fault categories. Future research should explore multi-label classification techniques by using the best-performing models identified in this study and applying various ensemble learning methods such as bagging, boosting and stacking. These strategies can help enhance individual model accuracy, leverage the strengths of multiple architectures and reduce classification errors, particularly in complex or overlapping defect scenarios. Additionally, a critical direction for future work is to evaluate the model performance across multiple benchmark datasets to assess the portability and generalizability of the trained models. This will help determine how robust the models are when exposed to different data distributions, environmental conditions and defect types. Conceptualization, B.P., S.N.D., J.C.M. and V.P. ; methodology, B.P., S.N.D., J.C.M. and V.P. ; software, B.P. ; validation, S.N.D., J.C.M. and V.P. ; formal analysis, S.N.D., J.C.M. and V.P. ; investigation, B.P. ; resources, B.P. ; data curation, B.P. ; writing--original draft preparation, B.P., S.N.D., J.C.M. and V.P. ; writing--review and editing, B.P., S.N.D., J.C.M. and V.P.