# 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  
This study classified data that can impact construction costs into three categories: the building general properties dataset, the BIM properties dataset, and the combination dataset of BIM properties and building general properties. The purpose of this study was to evaluate how much construction cost prediction errors can be relatively reduced when estimating construction costs using BIM properties compared to using only general building properties. In construction cost prediction, there are two types of error rates: the \"training error rate\" derived during the training phase and the \"testing error rate\" obtained after the completion of training. If there is a significant difference between the values of the training error rate and the testing error rate, the reliability of the predicted values can be questionable. Therefore, in addition to the training error rate and the testing error rate, a measure \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Hidden Layer Configurations** & **Learning Rate** & **Epochs** & **Test Data Ratio** & **Loss Function** \\ \hline \multirow{3}{*}{Case A} & [PERSON](100)/ & \multirow{3}{*}{0.0005} & \multirow{3}{*}{1500} & \multirow{3}{*}{0.5} & \multirow{3}{*}{[PERSON]} \\  & [PERSON](64)/ & & & & \\  & [PERSON](32) & & & & \\ \hline \multirow{3}{*}{Case B} & [PERSON](100)/ & \multirow{3}{*}{0.001} & \multirow{3}{*}{1500} & \multirow{3}{*}{0.5} & \multirow{3}{*}{[PERSON]} \\  & [PERSON](64)/ & & & & \\  & [PERSON](32) & & & & \\ \hline \multirow{3}{*}{Case C} & [PERSON](100)/ & \multirow{3}{*}{0.001} & \multirow{3}{*}{1500} & \multirow{3}{*}{0.5} & \multirow{3}{*}{[PERSON]} \\  & [PERSON](50) & & & & \\ \hline \multirow{3}{*}{Case D} & [PERSON](100)/ & \multirow{3}{*}{0.0005} & \multirow{3}{*}{1500} & \multirow{3}{*}{0.5} & \multirow{3}{*}{[PERSON]} \\  & [PERSON](64)/ & & & & \\  & [PERSON](32) & & & & \\ \hline \multirow{3}{*}{Case E} & [PERSON](100)/ & \multirow{3}{*}{0.01} & \multirow{3}{*}{1500} & \multirow{3}{*}{0.5} & \multirow{3}{*}{[PERSON]} \\  & [PERSON](64)/ & & & & \\ \hline \multirow{3}{*}{Case F} & [PERSON](100)/ & \multirow{3}{*}{0.0001} & \multirow{3}{*}{2500} & \multirow{3}{*}{0.5} & \multirow{3}{*}{[PERSON]} \\  & [PERSON](64)/ & & & & \\ \cline{1-1}  & [PERSON](32) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Configurations of test cases for machine learning. indicating the difference between these two values, called the \"relative error rate\", was calculated. The formula for calculating the relative error rate is as Equation (1). \[\text{Relative Error Rate}=\frac{\text{Training Error Rate}}{\text{ Testing Error Rate}}\times 100 \tag{1}\]  
In other words, a larger magnitude of the relative error rate indicates lower confidence in the predicted values, while a smaller magnitude of the relative error rate suggests higher confidence in the predicted values. This allows us to assess the reliability of the predicted values based on the size of the relative error rate. Considering that the optimal artificial intelligence model varies for each type of data (building general properties dataset, BIM properties dataset, building general + BIM properties dataset), a uniform artificial intelligence model was not applied to all data types. Instead, diverse combinations (Case A-Case F) were applied to each individual dataset, the building general properties dataset, BIM properties dataset, and building general + BIM properties dataset. As a result, the training error rate, testing error rate, and relative error rate were calculated for all cases. Finally, by comparing the average values of the training error rate and testing error rate when the relative error rate was the lowest for each case of building general properties dataset (General-1, General-2), BIM properties dataset (BIM-1, BIM-2), and building general + BIM properties dataset (Com-1, Com-2), the \"error rate\" was determined.