# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 4 Data for Machine Learning
### Data Collection  
The primary goal of this study was to develop a more accurate cost prediction model for building construction utilizing machine learning techniques in the 'Schematic Design' stage. To accomplish this, various data that can be extracted during the 'Schematic Design' phase, where architecture-related components are planned, were collected and analyzed, including total floor area, site area, building area, land area, landscape area, general floor height, total floor height, number of basement floors, number of ground floors, and number of parking spaces.  
In addition to this data, LOD 200 level BIM models were newly built and utilized to collect additional BIM properties, such as the number of rooms, sum of room heights, sum of room perimeter, sum of room area, sum of wall length, sum of wall area, sum of floor area, number of steps, amount of floor space, sum of floor space, number of stairs, and sum of steps. Table 1 provides a comprehensive summary of the types of data collected through this study. The factors determined by \"Building General Properties\" include \"Total area\", \"Site area\", \"Building area\", \"Landscape area\", \"Floor height\", \"Total height\", \"Ground floor level\", \"Number of floors\", \"Number of parking lots\", etc., and the correlation between these factors and construction costs is as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Building General Properties**} & \multicolumn{2}{c}{**BIM Properties**} \\ \cline{2-3}  & **Object** & **Data** \\ \hline Total Floor Area (m\({}^{2}\)) & & Number (ea) \\ Site Area (m\({}^{2}\)) & & Sum of Height (m) \\ Building Area (m\({}^{2}\)) & & Sum of Perimeter (m) \\ Landscape Area (m\({}^{2}\)) & & Sum of Area (m\({}^{2}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Collected data for building cost prediction using machine learning.  
Figure 2: Construction cost per total floor area according to total floor area (**left**), total construction cost according to total floor area (**right**).  
The relationship between each building general property and total construction cost is shown in Figure 3.  
In order to extract BIM properties for predicting construction costs, 78 BIM models including objects such as room walls, floors, columns, stairs, windows, and doors were modeled using 2D drawings. The newly created BIM models are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Building General Properties**} & \multicolumn{2}{c}{**BIM Properties**} \\ \cline{2-3}  & **Object** & **Data** \\ \hline Typical Floor Height (m) & & Number (ea) \\ Total Height (m) & & Sum of Height (m) \\ Number of Basement Floors (ea) & & Sum of Length (m) \\ Number of Floors (ea) & & Sum of Area (m\({}^{2}\)) \\ \hline Number of Parking lots (ea) & & Sum of Perimeter (m) \\  & & Sum of Area (m\({}^{2}\)) \\ \cline{2-3}  & \multirow{2}{*}{Column} & Number (ea) \\  & & Sum of Height (m) \\ \cline{2-3}  & \multirow{2}{*}{Stair} & Number (ea) \\  & & Sum of Steps (ea) \\ \cline{2-3}  & & Opening \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: Construction cost distribution relation with building general properties.  
From the created BIM model, properties such as the number and height of walls and rooms, circumference, and area sum were extracted. These data determine the size and complexity of the building, and data such as length, area, and circumference are expected to affect construction costs such as structure construction and finishing work, and were extracted as BIM attribute values for predicting construction costs. Figure 5 shows the results of analyzing the correlation between these BIM attributes and construction costs. Figure 5 shows the correlation between these BIM attributes and construction costs, which shows that the extracted BIM attributes correlate with construction cost.  
Figure 4: BIM model cases for cost prediction.  
Figure 5: Total construction cost according to BIM properties (room, wall).