## 3 Results
### Test of MLA Trained with the Same Number of Data in Each \(f_{c,\text{mass}}\) Range (Case-2)  
When all the training datasets were used, the data of the 30-60 MPa range accounted for 51% of the total and were considerably concentrated. The research was performed to determine whether having a large amount of training dataset in a specific compressive-strength range affected the accuracy of the MLA. To compare with Case-1, the compressive-strength range affected the MLA. The error rate of the MLA was investigated when the number of training datasets for each \(f^{\prime}_{\text{\emph{cross}}}\) range was the same (Case-2). For this, 230 data points were randomly selected for each range of \(f^{\prime}_{\text{\emph{cross}}}\); a total of 1380 (=230 \(\times\) 6) data were selected. Among them, 1080 (=180 \(\times\) 6) data were used for training, and 300 (=50 \(\times\) 6) data were used for validating accuracy. The verification results of Case-2 exhibit in Table 3 and Figure 5. The CV, RMSE, MAE, and MAPE of Case-2 were 0.34, 14.41 MPa, 11.42 MPa, and 26.85%, respectively, and the error indices were slightly increased compared with Case-1. Regarding the results for each\(f^{\prime}_{\text{\emph{cross}}}\) range, the CV of Case-2 was larger than that of Case-1 for all the ranges; i.e., the error was larger. The other indices of different \(f^{\prime}_{\text{\emph{cross}}}\) ranges were also larger compared with Case-1 in most cases. The \(\gamma\)-value of the 95% confidence interval was 0.35-1.80, and the error rate was approximately 72%. The \(\gamma\)-value of the 90% confidence interval was 0.41-1.59. The error rate was approximately 59%. The \(\gamma\)-value of the 80% confidence interval was 0.52-1.48, and the error rate was approximately 48%. Although the range of the concrete compressive strength data was wide, the number of data was relatively small; hence, it is assumed that the error rate of Case-2 was higher than that of Case-1.  
Figure 4: The normal distribution curve of \(\gamma\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**All Data**} & \multicolumn{5}{c}{**Range of \(f^{\prime}_{\text{\emph{c,meas}}}\)**} \\ \hline  & **1380** & **0–20 MPa** & **20–30 MPa** & **30–40 MPa** & **40–60 MPa** & **60–80 MPa** & **80–100 MPa** \\ \hline Training & \multirow{2}{*}{\(1080\)} & \multirow{2}{*}{\(180\)} & \multirow{2}{*}{\(180\)} & \multirow{2}{*}{\(180\)} & \multirow{2}{*}{\(180\)} & \multirow{2}{*}{\(180\)} & \multirow{2}{*}{\(180\)} & \multirow{2}{*}{\(180\)} \\ dataset & & & & & & & \\ \hline Testing & \multirow{2}{*}{\(300\)} & \multirow{2}{*}{\(50\)} & \multirow{2}{*}{\(50\)} & \multirow{2}{*}{\(50\)} & \multirow{2}{*}{\(50\)} & \multirow{2}{*}{\(50\)} & \multirow{2}{*}{\(50\)} \\ dataset & & & & & & & \\ \hline Mean (\(m\)) & \(1.08\) & \(0.78\) & \(1.07\) & \(0.99\) & \(1.08\) & \(1.28\) & \(1.26\) \\ \hline \(\sigma\) & \(0.36\) & \(0.2\) & \(0.48\) & \(0.39\) & \(0.3\) & \(0.28\) & \(0.20\) \\ \hline CV & \(0.34\) & \(0.26\) & \(0.45\) & \(0.39\) & \(0.27\) & \(0.22\) & \(0.16\) \\ \hline RMSE (MPa) & \(14.41\) & \(10.24\) & \(7.54\) & \(12.34\) & \(14.43\) & \(17.51\) & \(20.30\) \\ \hline MAE (MPa) & \(11.42\) & \(7.06\) & \(5.86\) & \(10.32\) & \(12.23\) & \(14.98\) & \(17.83\) \\ \hline MAPE (\%) & \(26.85\) & \(40.68\) & \(22.88\) & \(29.98\) & \(25.75\) & \(21.53\) & \(20.13\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis accuracy of ML trained with 1080 data points.