## 5 Limitations and Future Work  
This study considered the vast variety of ML approaches and their comparative performance in terms of predicting the strength property of HPC. However, it does have  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approaches Use** & **ML Methods** & **MAE** & **MSE** & **RMSE** & **MSLE** \\ \hline Individual learner & Decision tree & 5.40 & 55.70 & 7.46 & 0.052 \\  & Multilayer perceptron & 4.57 & 37.34 & 6.11 & 0.049 \\ \hline Ensemble & Decision tree-bagging & 4.19 & 34.51 & 5.87 & 0.034 \\ learning bagging & \
\begin{tabular}{c} neuron \\ network- bagging \\ \end{tabular} & 4.41 & 33.49 & 5.78 & 0.043 \\ \hline  & Decision & 3.53 & 24.28 & 4.92 & 0.029 \\ Ensemble &
\begin{tabular}{c} Multilayer perceptron \\ neuron network- \\ \end{tabular} & 4.39 & 39.29 & 6.26 & 0.045 \\ \hline Modified & Random forest & 3.26 & 22.26 & 4.71 & 0.026 \\ \hline Boosting ensemble & Gradient boosting & 4.11 & 33.60 & 5.79 & 0.042 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Statistical evaluation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Folds** & \multicolumn{3}{c}{**MLPNN-Bagging**} & \multicolumn{3}{c}{**MLPNN-Boosting**} \\ \hline  & MAE & RMSLE & RMSE & MAE & RMSLE & RMSE \\ \hline
1 & 6.136 & 0.151 & 8.197 & 7.628 & 0.126 & 9.229 \\
2 & 7.274 & 0.117 & 8.158 & 8.614 & 0.137 & 10.723 \\
3 & 1.511 & 0.010 & 2.032 & 6.008 & 0.072 & 9.055 \\
4 & 4.492 & 0.038 & 5.196 & 7.217 & 0.142 & 14.321 \\
5 & 4.483 & 0.056 & 5.807 & 5.500 & 0.067 & 6.347 \\
6 & 4.769 & 0.054 & 5.555 & 5.497 & 0.056 & 6.783 \\
7 & 2.688 & 0.031 & 4.175 & 5.799 & 0.081 & 7.901 \\
8 & 6.420 & 0.051 & 8.628 & 5.774 & 0.054 & 10.006 \\
9 & 6.059 & 0.094 & 9.990 & 7.432 & 0.131 & 9.404 \\
10 & 6.016 & 0.053 & 7.719 & 5.215 & 0.044 & 6.912 \\ \hline \hline \end{tabular}
\end{table}
Table 12: K-Fold cross-validation result of ensemble models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Folds** & \multicolumn{3}{c}{**RF-Bagging**} & \multicolumn{3}{c}{**GBR-Boosting**} \\ \hline  & MAE & RMSLE & RMSE & MAE & RMSLE & RMSE \\ \hline
1 & 6.136 & 0.151 & 8.197 & 7.628 & 0.126 & 9.229 \\
2 & 7.274 & 0.117 & 8.158 & 8.614 & 0.137 & 10.723 \\
3 & 1.511 & 0.010 & 2.032 & 6.008 & 0.072 & 9.055 \\
4 & 4.492 & 0.038 & 5.196  
certain limitations. The data included is indispensable for the efficiency of the forecasted models. The range of the database employed in this study was limited to 471. In addition, this research was also limited to compressive strength prediction and did not consider the flexural and corrosive behavior of concrete at high temperatures. Indeed, proper database and testing must be applied as they are vital elements for engineering applications. This study was based on a wide range of data sets with eight input variables; however, the database and input parameters can be increased to generate a better response of the employed models.  
It is recommended that additional variables, such as elevated temperature, humidity, and other environmental conditions, are added as input variables to evaluate the performance of the model for the prediction of required outcomes. As concrete properties are severely affected by environmental conditions, the impact under different conditions should be explored with other deep ML algorithms, such as the convolutional neural network (CNN) and the recurrent neural network, and using a restricted Boltzmann machine (RBM).