## 4 Interpretable Analysis of Prediction Model Based on SHAP
### Partial Interpretation Based on SHAP
Since the volume of steel fiber content in this sample is small (0.5%), the influencing factors of the final compressive strength are basically the same as those of ordinary concrete, including the cement content that determines the strength of the matrix, the coarse aggregate content, the superplasticizer content, and the water content, which significantly affect the mechanical properties of the composite material, which is consistent with the basic theoretical knowledge of concrete formed by long-term practice. For sample 2, the predicted compressive strength of the DNN model is 80.65 MPa, which is very close to the experimental result of 81.0 MPa. As shown in the figure, cement  
Figure 5: Force plot of samples by SHAP: (**a**) sample 1; (**b**) sample 2; (**c**) sample3. content and coarse aggregate content, located in the blue region, are the main features exerting negative influence. Among them, cement content has the longest band, indicating the strongest negative impact. In contrast, the dosage of high-range water reducer and water content, located in the red region, are the primary features contributing positively, with the dosage of high-range water reducer having the longest band and thus the most significant positive effect. Compared with sample 1, the steel fiber content (1.5%) in sample 2 increases more. Therefore, it can be seen from Figure 5b that the content of steel fiber has become a major feature affecting the compressive strength of concrete and plays a positive role, which is consistent with the results of many experimental studies [17,19]. From the perspective of mechanism analysis, the appropriate amount of steel fiber incorporation can hinder the expansion of micro-cracks in concrete and block the occurrence and development of macro-cracks. This enhances the overall internal bonding performance of the concrete and improves its lateral confinement under compression. The confinement effect has improved the compressive strength of concrete to a certain extent. For sample 3, the predicted compressive strength of the DNN model is 86.38 MPa, which is not much different from the test result of 89.3 MPa. As shown in the figure, cement content and coarse aggregate content, located in the blue region, are the main features exerting negative influence. In contrast, curing age, located in the red region, is the dominant feature with a positive effect, as indicated by its longest band length, representing the most significant positive contribution. The main difference between sample 3 and sample 2 is that the curing age is different (90 days and 28 days). Comparing Figure 5b,c, it can be seen that the curing age becomes a main feature affecting the compressive strength of concrete and plays a positive role, which means that under the same other conditions, prolonging the curing age allows the SFRC to achieve higher compressive strength. From the mechanism analysis, since the reaction of fine aggregate is relatively slow, the hydration product Ca(OH)\({}_{2}\) of cement is needed as the activator, and the fine aggregate will show an obvious enhancement effect in the later stage of cement hydration (generally after 28 days). The SHAP analysis results of the above three samples show that appropriate steel fiber content and longer age contribute to the development of higher compressive strength of concrete.