# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 5 Machine Learning Analysis for Construction Cost Prediction with Properties
### Error Rates Analysis of Test Cases
By comparing the derived error rates, it was possible to determine which data combination had the lowest error rate. The building general properties dataset, when the General-2 dataset was trained as Case A, yielded the minimum relative error rate value. The average of the training error rate and testing error rate at this point, which was 33.04%, was determined as the error rate. This information can be found in Table 6.  
The BIM properties dataset, when the BIM-1 dataset was trained as Case B, yielded the minimum relative error rate value. The average of the training error rate and testing error rate at this point, which was 37.33%, was determined as the error rate. This information can be found in Table 7.  
The building general + BIM properties dataset, when the Com-2 dataset was trained as Case C, yielded the minimum relative error rate value. The average of the training error rate and testing error rate at this point, which was 20.82%, was determined as the error rate. This information can be found in Table 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Case** & **Training Error Rate** & **Testing Error Rate** & **Relative Error Rate** \\ \hline \multirow{6}{*}{General-1} & Case A & 34.34\% & 37.22\% & 8.38\% \\  & Case B & 21.33\% & 8.24\% & 61.37\% \\  & Case C & 18.77\% & 20.02\% & 6.67\% \\  & Case D & 47.79\% & 76.64\% & 60.39\% \\  & Case E & 24.75\% & 4.19\% & 83.06\% \\  & Case F & 31.67\% & 37.01\% & 16.85\% \\ \hline \multirow{6}{*}{General-2} & Case A & 33.62\% & 32.46\% & 3.45\% \\  & Case B & 24.13\% & 11.12\% & 53.90\% \\ \cline{1-1}  & Case C & 21.18\% & 17.45\% & 17.60\% \\ \cline{1-1}  & Case D & 36.24\% & 39.34\% & 8.55\% \\ \cline{1-1}  & Case E & 25.81\% & 7.48\% & 71.02\% \\ \cline{1-1}  & Case F & 32.14\% & 33.96\% & 5.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Error rates from building general properties. In terms of the minimum error rate of test data, Com-2 (20.82%), using a combination of building general properties and BIM properties had the smallest error rate, followed by the building general properties dataset (33.04%), and BIM properties (37.33%). Upon investigation of the causes, among the factors within the building general properties dataset, the 'total floor area\" showed a very high Pearson correlation coefficient of 0.9 or above with the total construction cost. However, the remaining factors exhibited low correlation coefficients, at 0.63 or below. On the other hand, within the BIM properties dataset, there was no factor with a correlation coefficient as high as the floor area of the building general properties dataset (0.965048). However, all the factors within the BIM properties dataset showed correlation coefficients of 0.87 or higher. Therefore, it can be inferred that the combination of three factors based on the highest Pearson correlation coefficients, as observed in the Com-2 dataset, resulted in the lowest error rate when trained. As a result, compared to using general building properties or BIM properties independently to predict construction costs, it is expected that predicting construction costs by combining them can increase accuracy. The results are shown in Table 9. Additionally, in the case of Com-2, which had the lowest error rate, the training results of Model C are shown in Figure 6.