## 4 Conclusions  
In recent years, machine learning has emerged as a powerful tool for analyzing and predicting outcomes in various domains. One such application is the prediction of damage severity in materials, which holds great importance in structural health monitoring and maintenance. This paper presents a comprehensive study that harnesses machine learning techniques to predict crack depth in different materials, namely ABS, aluminum, and concrete.  
The study utilizes experimental data obtained from previous research and employs a range of regression models, including K Neighbors Regressor, Lasso, Ridge, Gradient Boosting Regressor, and SVR, to forecast crack depth. Model performance is evaluated using Mean Squared Error (MSE) on both training and test sets. The findings demonstrate that all models surpass the baseline model, indicating their ability to capture meaningful patterns within the data and make reasonably accurate predictions.  
Furthermore, the study identifies the most influential predictive features for each material. For instance, the impact of temperature on natural frequency exhibits a more pronounced effect in aluminum compared to concrete and ABS. This discrepancy can be attributed to the higher elastic modulus and greater consistency of isotropic sheet metal properties in aluminum relative to additive layer manufactured ABS and concrete. Moreover, the study reveals that all models outperform the baseline, implying their capability to learn significant patterns and facilitate accurate predictions.  
The study offers valuable insights into predictive features and model performance in crack depth prediction, which have practical implications for specialists involved in structural health monitoring and maintenance. By employing machine learning algorithms, it becomes possible to reduce the time and cost associated with traditional testing methods while enhancing the accuracy and reliability of damage severity predictions.  
It is worth noting that the training times for the discussed algorithms, such as Ridge, Lasso, kNN, SVR, Random Forest Regressor, and Gradient Boosting Regressor, are generally reasonable for small to moderate datasets. However, the actual time and complexity required for training may vary depending on dataset characteristics, implementation intricacies, and computational resources. It is essential to consider these factors when selecting and applying these algorithms to ensure efficient and effective model training.  
Future studies could explore the adoption of more advanced machine learning algorithms or ensemble techniques to further improve the accuracy of crack depth prediction. Additionally, investigating the influence of additional features, such as material composition or environmental factors, could provide valuable insights into the mechanisms of crack formation and propagation. Furthermore, evaluating the models on different datasets and in real-world scenarios would contribute to assessing the generalizability of these models beyond the specific materials and conditions used in this study.  
This study underscores the potential of machine learning techniques in predicting damage severity in materials. The results highlight the superiority of machine learning models in providing accurate and reliable predictions, which can significantly benefit structural health monitoring and maintenance practices. Further research is warranted to explore advanced techniques and the impact of additional features on predictive accuracy. Ultimately, the continued development and application of machine learning in this field could lead to remarkable advancements in structural engineering and maintenance.  
Writing--review & editing, I.O.; Supervision, M.K. and A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.