# Analysis of BIM-Based Quantity Take-Off in Simplification of the Length of Processed Rebar
## 4 Analysis & Discussion  
### Correlation Analysis  
In this section, an analysis was performed to confirm a linear correlation between the total floor area of the building and (1) the change in rebar material amounts and (2) the number of LPRS calculated through QTO. In the construction industry, design changes are often inevitable due to industrial characteristics [24], which means that QTO must be performed and revised repeatedly according to design changes. If the correlation analysis verifies that the linear correlation between each factor and the total floor area of the building is high, the results can contribute to predicting the change of these factors through the total floor area of the building in construction projects where frequent QTO works are challenging. Therefore, the correlation analysis between these factors and the floor area \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Amount of Rebar Material (Ton)**} \\ \cline{3-7}  & \multicolumn{2}{c}{**Case A**} & \multicolumn{2}{c}{**Case B**} & \multicolumn{2}{c}{**Case C**} \\ \cline{2-7}  & **Unrefined** & **Val.** & **Diff.** & **Val.** & **Diff.** & **Val.** & **Diff.** \\ \hline
**Project A** & 799.2 & 801.4 & +0.28\% & 805.2 & +0.75\% & 810.4 & +1.41\% \\
**Project B** & 515.5 & 517.0 & +0.29\% & 519.6 & +0.79\% & 522.6 & +1.37\% \\
**Project C** & 726.8 & 729.5 & +0.36\% & 734.2 & +1.01\% & 742.5 & +2.16\% \\
**Project G** & 757.4 & 760.2 & +0.38\% & 764.9 & +0.99\% & 773.4 & +2.12\% \\
**Project H** & 1613.5 & 1617.6 & +0.25\% & 1621.7 & +0.51\% & 1633.5 & +1.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Amounts of Rebar Materials for Slab Members and Differentials by Cases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Types of Rebar Lengths (Units)**} \\ \cline{3-7}  & \multirow{2}{*}{**Unrefined**} & \multicolumn{2}{c}{**Case A**} & \multicolumn{2}{c}{**Case B**} & \multicolumn{2}{c}{**Case C**} \\ \cline{3-7}  & & **Val.** & **Diff.** & **Val.** & **Diff.** & **Val.** & **Diff.** \\ \hline
**Project A** & 10,787 & 8233 & \(-\)23.68\% & 6554 & \(-\)39.24\% & 5477 & \(-\)49.23\% \\
**Project B** & 19,445 & 12,819 & \(-\)34.08\% & 10,820 & \(-\)44.36\% & 9477 & \(-\)51.26\% \\
**Project C** & 12,860 & 10,573 & \(-\)17.78\% & 9023 & \(-\)29.84\% & 7955 & \(-\)38.14\% \\
**Project C** & 30,854 & 19,204 & \(-\)37.76\% & 16,020 & \(-\)48.08\% & 14,477 & \(-\)53.08\% \\
**Project H** & 23,689 & 16,113 & \(-\)31.98\% & 13,103 & \(-\)44.69\% & 11,264 & \(-\)52.45\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Numbers of LPRS for Slab Members and Differentials by Cases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Basements**} & \multicolumn{2}{c}{**Stairwells**} \\ \hline  & **Amounts of** & & & **Amounts of** & \\  & **Rebar** & & **Rebar** & & **Differential** \\ \hline
**Unrefined** & 2351.13 & - & 295.80 & - \\
**Case A** & 2354.91 & +0.16\% & 297.47 & +0.56\% \\
**Case B** & 2361.38 & +0.44\% & 300.35 & +1.54\% \\
**Case C** & 2370.74 & +0.83\% & 302.19 & +2.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Total amounts of rebar materials calculated and differentials from unrefined cases. is important. Correlation analysis measures the strength of an association between two (or more) quantitative variables [25]. The correlation analysis was conducted according to Equation (1). The correlation coefficient values computed by the correlation analysis range from \(-1\) to \(1\), which indicates that the two variables are strongly related in a linear manner if the absolute value of the correlation coefficient is closer to \(1\).