# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
###### Abstract
The results showed that the GA-ANN model was capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance of cement-based materials. [PERSON] et al. [38] used an ANN model to predict the self-healing property of concrete containing ground granulated blast furnace slag in terms of compressive strength recovery based on 51 samples collected from their experimental studies. Generally, the predicted results by the ANN model were in good agreement with the experimental values. [PERSON] and [PERSON] [39] conducted a comparative study on six ML algorithms including SVR, decision tree regression (DTR), gradient boosting regression (GBR), ANN, Bayesian ridge regression (BRR), and kernel ridge regression (KRR) for the crack-repairing ability of the bacteria-based self-healing concrete. The results showed that GBR performed much better than other models with \(R^{2}\) values of 0.93 and 0.74 for the training set and testing set, respectively. However, the \(R^{2}\) values of most models were less than 0.7 on both the training and testing sets. Although extensive experiments with different combinations of influencing variables were utilized to generate the empirical dataset, their study only selected three variables including the number of bacteria, the healing time, and the initial crack width to predict the crack closure percentage as the output. [PERSON] et al. [40] used six types of machine learning algorithms to predict the healing performance of self-healing concrete. The data were taken from the open literature; however, different studies used different self-healing indicators (e.g., crack width, permeability, or mechanical properties) to assess self-healing; therefore, their study lacks a specific analysis and discussion on the efficiency of the models for predicting a specific indicator. [PERSON] et al. [41] explored several ML algorithms, including support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) for predicting the shear strength of rockfill materials. The results demonstrated that the SVM achieved the best prediction performance. To the best of our knowledge, there has been no study to date to predict the self-healing of ECC using the ML approach. It is worthwhile to understand and evaluate the prediction capability of various ML models for the self-healing of ECC. Furthermore, conducting experiments is usually expensive and time consuming; therefore, it is necessary to develop accurate and reliable prediction models for the self-healing ability of ECC. In addition, it is critical to choose an appropriate ML method as each algorithm has a significant effect on the accuracy of the results [42]. Therefore, this study aims to provide a comparative analysis on the performance of various ML models in predicting the self-healing capability of ECC. The ML model with the best performance can be used as a baseline prediction model for developing advanced models in the future. In this paper, four ML individual methods including linear regression (LR), SVR, backpropagation neural network (BPNN), and classification and regression tree (CART) were proposed to predict the self-healing capability of ECC. To improve the prediction accuracy, three ensemble methods, namely bagging, AdaBoost, and stacking, were used to construct ensemble models using the individual models as the base learners. A series of experimental works on the self-healing performance of ECC samples was conducted, and the results were used to develop and compare the accuracy among the ML models. Experimental data collected from the experiments were first preprocessed and then divided into a 10-foldcross-validation algorithm (for the details, refer to Section 4.1) to avoid overfitting. Figure 1 summarizes the steps that were performed when predicting the self-healing of ECC. This paper is organized as follows.