# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 5 Machine Learning Analysis for Construction Cost Prediction with Properties  
### Dataset for Cost Prediction  
In a general estimate, construction costs were predicted only by construction cost influencing factors such as floor area, but this paper intends to present a more accurate construction cost prediction method using BIM properties as well as these construction cost influencing factors.  
Due to the limitation of the amount of data, we attempted to analyze the predictive performance of construction costs, focusing on the influencing factors that have the highest correlation with construction costs. \"Total floor area\" and \"Building area\" were derived as factors of building general properties, which are highly correlated with construction costs. Through the correlation analysis with construction cost, the General-1 model, which uses only the total area and building area as predictors of construction cost, and the General-2 case, which predicts construction cost by adding the number of floors, were defined. The details of the General-1 and General-2 cases for analyzing the predictive performance of construction costs are as shown in Table 2.  
Among the construction cost influencing factors extracted from BIM, the high correlation with construction cost was found for'sum of floors' and 'number of stairs', using them to define a 'BIM-1' case that can predict construction cost, and then'sum of wall area' with high construction cost impact was added to define 'BIM-2'. These details are as shown in Table 3.  
Finally, in the form of a combination of the two types, the Com-1 was defined using the sum of the total area and the length of the floor circumference as cost influencing factors, and the Com-2 model was defined by adding the number of stairs to it. Details of the Com-1 and Com2 datasets are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Influencing Factors** & **Source** \\ \hline \multirow{2}{*}{Com-1} & Total Floor Area & Building General Properties \\  & Sum of Floors’ Perimeter & BIM Properties \\ \hline \multirow{3}{*}{Com-2} & Total Floor Area & Building General Properties \\  & Sum of Floors’ Perimeter & BIM Properties \\ \cline{1-1}  & Number of Stairs’ Steps & BIM Properties \\ \hline \hline \end{tabular}
\end{table}
Table 4: Combination dataset of BIM properties and building general properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Influencing Factors** & **Pearson Correlation Coefficient** \\ \hline \multirow{2}{*}{General-1} & Total Floor Area & 0.965048 \\  & Building Area & 0.625655 \\ \hline \multirow{2}{*}{General-2} & Total Floor Area & 0.965048 \\  & Building Area & 0.625655 \\ \cline{1-1}  & Number of Floors & 0.509851 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Building general properties dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Influencing Factors** & **Pearson Correlation Coefficient** \\ \hline \multirow{2}{*}{BIM-1} & Sum of Floors’ Perimeter & 0.895415 \\  & Number of Stairs’ Steps & 0.875447 \\ \hline \multirow{3}{*}{BIM-2} & Sum of Floors’ Perimeter & 0.895415 \\  & Number of Stairs’ Steps & 0.875447 \\ \cline{1-1}  & Sum of Walls’ Area & 0.870781 \\ \hline \hline \end{tabular}
\end{table}
Table 3: BIM properties dataset.