## 4 Conclusions  
In this study, PUCM was modified with the level of NS particles and tested for mechanical properties tests. Response surface methodology and machine learning algorithms were employed to optimize and model the cement mortar mixtures. The fresh and mechanical properties were used to train the machine learning algorithms, which included artificial neural networks and Gaussian process regression. Four performance indicators assessed the prediction accuracy of the developed. The conclusions outlined in this study are stated below:  
1. The mechanical properties of PUCM remarkably decreased with increases in PU binder content, more pronounced in the compressive strength values. However, some part of the lost strength was mitigated due to the reinforcing effect of NS particles. The compressive strength of the PUCM25-0 specimen was 31.34 MPa, which appeared to be the lowest strength among all the mixes.
2. The RSM/CCD developed models evaluated the mechanical properties of PUCM, involving the PU binder and NS material as the independent variable with high accuracy, with R\({}^{2}\) values of 0.8760 and 0.9469 for the flexural and compressive strength, respectively. The optimized PUCM mixture can be achieved by introducing 3.5% PU binder and 2.93% NS particles by weight of cement.  
Figure 16: (**a**) Taylor diagram and (**b**) box plots.  
3. Artificial intelligence models were developed to predict the flexural strength of the PUCM. The performance of the machine learning algorithms was tested using performance indicators such as R\({}^{2}\), MAE, MSE, and RMSE. The GPR algorithm outperformed the ANN with higher R2 and lower MAE values in the training and testing phases. The GPR can predict flexural strength with 90% accuracy, while ANN can predict it with 75% accuracy. The Taylor diagram and box plots also confirmed that GPR outperforms the ANN model.
4. The macro properties of the polyurethane-cement mortar were explored extensively. However, the finding showed that polyurethane binder significantly affected the mechanical properties of the cementitious-based composite, particularly compressive strength. Therefore, the mechanism development behind these behaviors and how the PU binder affects the reaction kinetics are essential, and may likely change the microstructure of cement mortar, something which has not been explained. Thus, this requires future research.  
H.Z. and Y.E.I.: conceptualization, investigation, supervision, resources, project administration, funding acquisition M.S.M.A.-k. and S.I.H.: conceptualization, methodology, investigation, data curation, writing--original draft, writing--review and editing, visualization. formal analysis, S.S.M.A.-q.: data curation, writing--original draft, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of China (No. 51708314) and the APC was funded by the Structures and Materials Laboratory (S&M Lab) of the College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia.  
Not applicable.  
Data available on request due to privacy reasons.  
The authors declare no conflict of interest.