## 5 Conclusions  
In this study, a deep learning model is proposed to predict the compressive strength of SFRC. Based on a massive number of experimental data, the model is trained and tested, and the prediction results are explained and analyzed by the SHAP interpretability method to overcome the \"black-box\" problem of conventional machine learning. The main conclusions are as follows:  
* **Model performance verification:** The DNN model established in this study is compared and analyzed against three commonly used algorithmic models and three widely used empirical formulas. On the testing set, the model achieves an R\({}^{2}\) of 0.94 and a MAPE of 8.16%, indicating that the predicted compressive strength of SFRC is highly consistent with the experimental results. This demonstrates that the DL model proposed in this paper possesses high prediction accuracy and generalization ability, and effectively captures the complex nonlinear relationships among the characteristic parameters. * **Feature Importance Analysis:** The SHAP value analysis results based on a wide range of experimental samples are consistent with the conclusions obtained from the experiments. Among the 14 input features, cement content, coarse aggregate content, and curing age are the most important factors affecting the compressive strength of concrete. For steel fiber reinforced concrete, in addition to the above three, an increase in steel fiber content, silicon powder content, dosage of high-range water reducer, and other parameter values has a positive impact on the compressive strength, providing useful guidance for the engineering design of concrete materials. * **The influence mechanism of steel fiber parameters:** For steel fiber reinforced concrete, both local and global interpretation analysis based on SHAP shows that within an appropriate range, an increase in steel fiber content leads to an enhancement in the compressive strength of concrete, although the improvement is relatively small. The overall impact of steel fiber length on compressive strength is limited, but excessively long fiber has a negative effect. This is consistent with engineering practice, as overly long fibers can impair the workability of the mixture and the quality of construction. Although this study confirms the effectiveness of a DNN framework integrated with the SHAP interpretability method in predicting the compressive strength of SFRC, several limitations remain. As a typical multiphase composite material, concrete has been extensively studied with respect to its strength properties. While this work investigates the influence of multiple features on the compressive strength of SFRC, the synergistic effects among these features have not yet been quantified. A deeper exploration of feature interdependencies may lead to the development of a more comprehensive and accurate strength prediction model. In addition, given the wide variety of deep learning architectures available today, adopting more advanced and powerful models may further improve prediction accuracy and interpretability, thereby offering more reliable guidance for engineering applications. H.W. : Conceptualization, Methodology, Investigation, Resources, Writing--review and editing, Supervision, Project administration, Funding acquisition. J.L. : Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing--original draft, Visualization. S.G.: Methodology, Software, Validation, Data curation, Writing--original draft and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors acknowledge financial support from the National Natural Science Foundation of China [51568062] and also from the Open Research Fund Project of Xin-jiang Key Laboratory of Building Structure and Earthquake Resistance [600120004]. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data will be made available on request.