## 4 Interpretable Analysis of Prediction Model Based on SHAP
### Global Interpretation Based on SHAP  
The global interpretation aims to provide a comprehensive representation of SHAP values for the input features of all samples. Figure 6 shows the distribution of SHAP values for individual features throughout the dataset, where the vertical axis represents the input features ranked by importance, and the horizontal axis represents the SHAP values of specific features. Positive and negative values represent the positive and negative effects on the output results, respectively. The larger the absolute value, the higher the influence. The color represents the magnitude of the feature parameter values, with blue indicating lower values and red indicating higher values. Therefore, Figure 6 not only reflects the importance of characteristic parameters, but also reveals how changes in characteristic values affect the compressive strength of SFRC. It can be seen from Figure 6 that the cement content is the most important characteristic parameter affecting the compressive strength, which is consistent with the conclusion obtained in the local interpretation. The positive effect of cement content on the compressive strength of SFRC increases with the increase in characteristic parameter values. Similarly, as values of age, steel fiber content, silica powder content, coarse aggregate content, and dosage of high-range water reducer water reducer increase, their corresponding positive SHAP values also increase, indicating that these parameters have a positive effect on the compressive strength of SFRC. In contrast, water content and steel fiber length exhibit increasingly negative SHAP values as their values increase, suggesting a more significant negative impact on compressive strength. The influence of slag content on compressive strength shows a trend of first increasing and then decreasing. When the substitution rate of slag admixture for cement is too high, it will inevitably lead to a decrease in the amount of cement, a decrease in the content of Ca(OH)\({}_{2}\) that plays an activating role, and a deterioration of the hydration conditions of slag. Therefore, a higher slag substitution rate does not necessarily yield better results. Figure 7 presents the dependence diagram of eight important characteristic parameters. Through the dependence diagram, the influence of parameter value change on compressive strength can be understood more deeply. The DNN model captures the overall relationship between these eight parameters on the compressive strength: (1) increases in age, steel fiber content, silica powder content, coarse aggregate content, and dosage of high-range water reducer lead to higher predicted compressive strength; (2) increases in water content, sand content, and steel fiber length result in a reduction in the predicted compressive strength. Figure 6: SHAP summary plot. Figure 7: _Cont._  
From Figure 7a, it can also be clearly seen that the cement content is positively correlated with the compressive strength of SFRC. After the amount of cement reaches 600 kg/m\({}^{3}\), the SHAP value becomes positive, and the positive impact on the compressive strength increases. It can be seen from Figure 7b that the SHAP value of the age increases with increasing curing time, with a rapid rise before 28 days and a slower increase thereafter, which is consistent with the hydration reaction process of the concrete. As shown in Figure 7c, when the steel fiber content reaches 2.5%, the SHAP value is positive and at its maximum, which has the greatest positive impact on the compressive strength. Steel fibers primarily enhance the toughness and failure behavior of concrete by inhibiting crack propagation. An increase in steel fiber content can improve the compressive strength of SFRC to a certain extent. However, since the matrix strength remains the dominant factor, this improvement is limited. As shown in Figure 7h, steel fiber length exhibits a nonlinear relationship with compressive strength.