# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 2 Experimental Program
### Data Collection  
Experimental data for prediction were gathered with four features, including the crack width before self-healing (representing the influencing factor of self-healing) and the mineral contents of FA, SF, and LP. It is noteworthy that the factors such as GPC, sand, W/CM, and healing time were kept constant, and hence, they were excluded in the prediction modeling. For each ECC mixture, there were 6 identical test specimens. After pre-loading, the crack widths of the specimens were measured using the digital microscope before and after the self-healing. Four horizontal lines were drawn on the surface of each specimen along the direction of vertical force, which divided the specimen into five observation areas, as shown in Figure 5. With the newly developed splitting tensile test, the crack propagation for all samples was generally consistent and visually straight, as shown in Figure 4. The cracks observed in the observation zone were used to manifest the self-healing capability of individual samples in this study. The schematic diagram of the measurement is shown in Figure 5. In each observation area, only one crack datum was recorded if the crack width showed little or no change along the vertical force; otherwise, multiple crack data would be collected. For example, more crack data were collected for  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Mix** & **Water/cm** & **Sand** & **Water** & **Fiber (V)** & **GPC** & **Hy Ash** & **SF** & **LP** & **HRWR** \\ \hline FA70 & 0.29 & 419.67 & 338.07 & 26 & 349.73 & 816.03 & 0.00 & - & 5.13 \\ FA65-SF5 & 0.29 & 419.67 & 338.07 & 26 & 349.73 & 757.74 & 58.29 & - & 5.13 \\ FA60-SF10  
FA65-LP5 as more cracks were recorded in the observation zone compared to the other samples, as indicated in Table 4. In total, 617 crack data samples were collected from 9 mixtures to construct the ML training-testing dataset [44]. In previous ML studies on predicting the mechanical [35; 45; 46; 47; 48] and permeability [49] properties of concrete through machine learning methods, the number of samples is usually less than 600, so the number of samples in the study was considered as sufficient. To assist researchers and engineers who are interested in re-implementing or improving the algorithms and exploring the application of ML in ECC self-healing prediction, the codes developed in this study are published as open-source codes. The raw crack data and full code [44] are available from our open-source project GitHub repository [50]. Table 4 shows the number of collected samples and range of cracks on each sample before and after self-healing.  
Figure 3: Splitting tensile test apparatus and microscope used in the experiment for creating and measuring ECC cracks. (**a**) Splitting tensile test apparatus; (**b**) schematic diagram of the apparatus; (**c**) crack width measurement.  
Figure 4: Surface images of cracked ECC specimens: (**a**) before and (**b**) after self-healing.  
Figure 5: Schematic diagram of measuring observation areas on the surface of ECC mixture specimens.