## 4 Conclusions and Discussion  
This research developed a framework to support efficient spatial visualization and comprehensive decision-making based on building performance analysis within the BIM context during the initial design phase. The developed framework automatically transforms clients' spatial requirements, expressed through sketches, into BIM-based building component objects and effectively calculates building performance analysis information such as direct material costs and energy usage charges. Through the verification phase, it was confirmed that the framework automatically translates sketch information into wall, floor, and roof objects in a BIM model, and it was validated that the framework automatically calculates direct material costs, as well as annual electricity and gas energy charges from the BIM model. The developed framework allows for the explicit visualization of spatial configurations from client-defined plan sketches through a BIM model, effectively providing clients with information on space size, form, and location during the initial design phase. Moreover, by efficiently calculating and providing direct material costs and annual electricity and gas energy charges from the generated BIM model, it serves as valuable evidence for budgetary decision-making at this stage. The generated BIM model can be utilized as a data model to efficiently convey explicit spatial requirements to professionals such as architects, serving as a foundational model for preliminary design. Furthermore, the research aims to refine the framework by conducting case studies that demonstrate the creation of BIM models for multi-story buildings. Additionally, the study will explore enhancing the understanding of spatial configurations through experiences in virtual 3D environments created with BIM models, incorporating elements such as furniture and openings, including windows and doors. This approach utilizes the framework developed in this research, facilitating the comprehension of space based on sketch information provided directly by clients. A more fundamental investigation into the potential errors within the BIM model creation system was conducted. The possibility of errors arising from translating sketched information into BIM components is noted. To investigate the mentioned error rate, six test \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CASE** & **CASE 1** & **CASE 2** & **CASE 3** & **CASE 4** \\ \hline Input & & & & \\ \hline Output & & & & \\ \hline \hline Direct material cost estimation (W) & 1,768,545 & 1,600,841 & 1,495,587 & 1,447,745 \\ \hline \hline
\begin{tabular}{c} Energy cost estimation \\ (W) \\ \end{tabular} & Electric costs (W) & 156,422 & 129,365 & 114,910 & 137,616 \\ \hline \hline Total costs (W) & 455,111 & 376,389 & 334,333 & 400,396 \\ \hline \hline Total costs (W) & 2,380,080 & 2,106,595 & 1,944,831 & 1,985,758 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation using test cases. cases were executed. It was confirmed that the average error occurring when translating sketched information into components of a BIM model is 1.36%. This error rate is attributed to several factors: the performance limitations of the LETR model, a line detection model on an object level; distortions in line thickness resulting from the line refinement algorithm developed in this study; and length discrepancies arising from the wall join function within the Revit application. Future research will aim to reduce the error rate and improve the accuracy of the framework by conducting additional training of the LETR model and refining the line refinement algorithm. While the framework has been implemented to calculate direct material costs and annual electricity and gas energy charges based on provided sketch information, further research is required to enhance the framework with accurate calculation and simulation techniques for more specific decision support in spatial detailing by providing additional building performance analysis information.