# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 6 Conclusions  
In this study, four individual (LR, SVR, BPNN, and CART) and three ensemble (bagging, AdaBoost, and stacking) ML models were proposed to predict the self-healing of ECC. All the models were trained, validated, and tested based on the experimental results from nine ECC mixtures. Their prediction performances were analyzed and compared in terms of the MAE, RMSE, and \(R^{2}\). Based on the comparison of the results, the following conclusions can be drawn:  
1. Among all individual ML models, the BPNN model performed the best in terms of the RMSE and \(R^{2}\), while the SVR model had the best performance in terms of the MAE;
2. All ensemble methods can generally improve the prediction accuracy of individual methods; however, the improvement varies. It was found that the bagging method mainly enhanced the performance of the BPNN and CART, whereas the AdaBoost method brought a considerable improvement for the LR and SVR models;
3. Among all the ML models studied, the Stack_LR model demonstrated great prediction on the self-healing of ECC and performed the best on the MAE, RMSE, and \(R^{2}\). The  
Figure 15: Ten-fold cross-validation of the RMSE by the proposed ML models in the prediction of the self-healing ability of ECC.  
assessment of the box plot also revealed that the stackLR model outperformed all other models because of its shortest IQR length and smallest RMSE values;
4. For the initial crack widths less than 60 \(\upmu\)m, the variations shown in the SVR model were smaller than those presented in other models. However, the CART model showed smaller variations for the crack widths between 60 \(\upmu\)m and 100 \(\upmu\)m compared to the SVR and BPNN models. For crack widths larger than 100 \(\upmu\)m, the SVR model performed the best, showing the smallest variations;
5. The computational results indicated that the individual and ensemble methods could be used to predict the self-healing ability of ECC. However, how to choose an appropriate base learner and ensemble method is critical. To improve the performance accuracy, researchers should employ different ensemble methods to compare their effectiveness with different ML models.  
Although the proposed ML models can be used to predict the self-healing of ECC, there are some challenges such as time consumption and the quality of the dataset that need to be addressed. Future investigation and experimentation should be carried out to extend the training dataset to include various crack width distributions and diverse influencing factors such as W/D cycles, healing time, etc. In addition, more research should be undertaken to optimize the parameters in ML models and develop hybrid ML models to improve the prediction accuracy.  
Not applicable.  
The data presented in this study are openly available in Github at [[https://github.com/davidnsw/Self-healing-of-ECC](https://github.com/davidnsw/Self-healing-of-ECC)]([https://github.com/davidnsw/Self-healing-of-ECC](https://github.com/davidnsw/Self-healing-of-ECC)) accessed on 1 March 2022, reference number [50].  
The authors declare no conflict of interest.