# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 6 Conclusions and Limitation  
### Conclusions  
An accurate estimate of the total construction cost of a building is possible only after the \"Detailed Design\" phase. In addition, estimating construction costs requires calculating the quantity of all components in the drawing, which takes considerable time. If the cost exceeds the budget, a comprehensive plan revision may be required, and in some cases, a complete overhaul of the initial plan may be required.  
Although the building components are not yet accurately determined, it is necessary to accurately predict the total construction cost in the schematic design phase in which the main structural system is determined. This helps to prevent changes to the overall plan due to budget shortages. However, so far, low-accuracy unit area-based cost estimation methods are mainly used. BIM has major property values that determine the characteristics of buildings, and these values are highly correlated with construction costs. In addition, it is expected that accurate prediction of construction costs will be possible in the early stages by utilizing factors that affect construction costs.  
This study attempted to verify whether the predictive performance of total construction costs can be improved when BIM object characteristics are used as an influencing factor compared to general characteristics of buildings.  
For this study, a total of 78 buildings with construction costs of less than 20 billion were analyzed, and data extracted from each building's 'Building Register' or 'Building Overview Data' were defined as 'Building General Properties', and data extracted from BIM objects as 'BIM Properties'.  
As a result of testing construction cost prediction performance on more than 30 AI learning models, six of the lowest error rates, Case A, Case B, Case C, Case D, Case E, and Case F, were defined as final analysis targets, and relative construction cost prediction performance was analyzed in these six cases. All datasets were trained and tested using all six models. As a result, the building general properties dataset showed an error rate of 33.04% and the BIM properties showed an error rate of 37.33%. When using the building general properties and BIM properties datasets together, the error rate was reduced to 20.82%. Therefore, it is expected that accurate construction cost prediction is possible by combining the building general properties and BIM properties datasets.  
Figure 6: Training result of model C (Com-2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Building General** & **BIM Properties** & **Building General +** \\  & **Properties** & **BIM Properties** \\ \hline Mean Error Rate & 33.04\% & 37.33\% & 20.82\% \\ ((Training + Test)/2) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Error rate comparison.  
As a result of analyzing the prediction performance of construction costs through Deep learning, using BIM properties together can increase the accuracy of predicting total construction costs rather than using general building properties alone. Using this, it is expected that accurate construction costs due to design changes can be predicted at the basic design stage.