## 3 Results and Analysis
### Predicted and Measured Compressive Strength Relationships
#### 3.4.2 M5P-Tree Model  
In this study, the M5P-tree model tree is utilized to forecast the compressive strength of conventional concrete using 483 mix-design data. The coefficient of determination (R2), root mean square error (RMSE), and goal were all employed to assess the suggested performance of the model in this research. The M5P-tree technique, as seen in Figure 6b, divides the input space (independent variables) into linear tree regression functions (marked LM1 through LM8). \(Y=b_{o}+b_{1}\times X_{1}+b_{2}\times X_{2}\), where b\({}_{o}\), b\({}_{1}\), and b\({}_{2}\) are linear regression constants representing the model parameters. The model parameters are listed in Table 6. The study dataset has a 25% and 35% error line, indicating that all measured values fall within the 20% and \(-15\%\) error line. The coefficient of determination R2 for this model indicates that the model performance is better than the NLR.  
Therefore, from the result of slump retention and compressive strength, SP62 (FM) can be used to produce a precast concrete member. The admixture should maintain a liquid consistency and good workability when used with concrete that has a low w/c and a high quantity of mineral additives. High early strength developments are made possible by the PCE-based superplasticizer even at low ambient temperatures and without additional external heat. This might make it possible to shorten the stripping periods, which could lead to a more effective production process. The compaction energy used to compact concrete may be lessened with concrete admixture. Therefore, concrete producers, builders, and installers may profit economically and technically. The three types of water-reducer additives can be used to produce the precast concrete member.  
A similar study was also conducted on the effect of two water-reducer polymers with smooth and rough surfaces on the workability and the compression strength of concrete from an early age (1 day) up to 28 days of curing. The polymer contents used in this study varied from 0 to 0.25% (%wt.). The initial ratio between water and cement was 60%, and it slowly reduced to 0.46 by increasing the polymer contents. The compression strength of concrete was increased significantly by increasing the polymer contents by 24% to 95% depending on the polymer type, polymer content, w/c, and curing age. Because of a fiber net (netting) in the concrete when the polymers were added, which led to a decreased void between the particles, binding the cement particles increased the viscosity of the fresh concrete and the compression strength of the hardened concrete rapidly. This study also aims to establish systematic multiscale models to predict the compression strength of concrete containing polymers and to be used by construction projects with no theoretical restrictions. For that purpose, 88 concrete samples modified with two types of polymer (44 samples for each modification) have been tested, analyzed, and modeled. Linear and nonlinear regression, M5P-tree, and Artificial Neural Network (ANN) approaches were used for the qualifications. In the modeling process, the most relevant parameters affecting the strength of concrete were polymer incorporation ratio (0-0.25% of cement's mass), water-to-cement ratio (0.46-0.6), and curing ages (1 to 28 days). Among the used approaches and based on the training data set, the model made based on the nonlinear regression, ANN, and M5P-tree models seem to be the most reliable. The sensitivity investigation concludes that the curing time is the most dominating parameter for predicting concrete's maximum stress (compression strength) with this dataset [12].