## 6 Conclusions  
BIM technology was applied in this research with the intention of aiding building facility managers in decision-making processes and budget management by using a CBR algorithm. Facility managers often rely on national averages or their own personal experience when estimating installation costs for building components; however, the integration of various data such as structured and graphical data in IFC with the CBR model resulted in an accurate cost estimation based on the existing best matching cases. Previous cases were used for obtaining a better match for new problems that the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **CBR** & **MRA** & **Difference** \\ \hline
1 & 23.20\% & 20.21\% & 2.99\% \\
2 & 31.00\% & 26.26\% & 4.74\% \\
3 & 17.63\% & 25.32\% & \(-\)7.69\% \\
4 & 31.49\% & 20.06\% & 11.43\% \\
5 & 36.85\% & 67.50\% & \(-\)30.65\% \\
6 & 15.03\% & 25.46\% & \(-\)10.43\% \\
7 & 17.74\% & 20.97\% & \(-\)3.23\% \\
8 & 18.68\% & 24.23\% & \(-\)5.55\% \\
9 & 41.07\% & 24.04\% & 17.03\% \\
10 & 31.01\% & 16.60\% & 14.41\% \\ Average & 26.37\% & 27.06\% & \(-\)0.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average error rate of each test. MRA: multiple regression analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Case32** & **Case29** \\ \hline Similarity & 0.96 & 0.9 \\ Accessibility & 3 & 3 \\ Case Code & 33 & 30 \\ Component Type & Window & Window \\ Cost (\$) & 162 & 82 \\ Duration (min) & 43 & 40 \\ Material & Wood & Wood \\ Position & Exterior & Interior \\ Size (in.) & \(15\times 25\) & \(15\times 25\) \\ Story & 4 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Best matched cases from CBR model. facility managers may have to deal with. This research showed that, with the CBR model, the facility managers can select a particular object from the BIM model, obtain the properties related to that object, input that data into the CBR model, and retrieve the closest case match. The illustrative example presented in this research showed how beneficial the proposed model could be with window installation cost estimation. Window installation is an example of how replacement costs can vary depending on the location of the installation and other factors related to the window. An illustrative example that took into consideration 180 cases of a real building was presented. While it is uncommon to obtain such a high number of cases in the practice of facility management, recently, the number of BIM projects in the industry is increasing, which will aid in collecting a sufficient number of cases in the near future. Moreover, it should be noted that CBR can be used for conducting data analysis even with a few cases. Therefore, it is desirable but not necessary to make use of a large number of cases in the proposed approach. In the proposed approach, two main advantages were identified. First, this study makes a theoretical contribution to the body of knowledge by developing a CBR model based on data obtained from IFC entities. Next, the model and the study results will be valuable to practitioners when estimating the replacement cost of windows with the help of the CBR algorithm. The model presented in this research is a prototype that demonstrates the process of retrieving replacement costs from BIM. Thus, the model needs to improve itself. First, owing to the complexity in the IFC structure, this research was focused on predicting cost estimates for window replacement projects with six factors. However, there would be more factors affecting the replacement cost. Second, the CBR in this study was constructed based on only 180 windows installation cases as an illustrative example. Third, the model did not take account of the time value of money. Therefore, further studies to overcome these limitations and improve the accuracy of the presented model are being carried out with additional factors and cases. Conceptualization, H.K. ; methodology, H.K. and J.K.; software, H.K. and J.K.; validation, H.K. and J.K.; formal analysis, H.K.