## 3 Research Methodology
In this stage, all BIM categories are selected after inputting the coordination 3D view's name into the automated module. The module then filters out all unnecessary categories, such as views, lines, STR beam systems, section boxes, and shaft openings, and removes them from the list of extracted categories. This methodology illustrates a comprehensive and detailed approach to BIM QCs, ensuring that each discipline's specific requirements are met while maintaining a high data accuracy and integrity standard. Figure 2: A comprehensive structure of the developed 3D BIM QC system framework. Figure 3: Overall architecture of the detailed _Data Extraction and Analysis Module_ for the structural BM model’s QC process. Quality control in the BIM models involves detailed extraction and analysis of essential parameter values relevant to each category. This systematic approach ensures comprehensive coverage of all necessary parameters for QCs. In the structural BIM model's QC process, for example, extracting \"Family Type\" parameter values is essential to verify their consistency with the project's codification procedures. Similarly, _Volume_ parameter values for steel and concrete components are extracted to confirm that each element has an assigned volume, a critical factor for accurate QTO. For concrete components, _Area_ parameter values are also extracted to ensure that all such elements have an area value, reinforcing the precision of the QTO process. Phase and Workset parameters play a crucial role in the classification of elements. The Phase parameter, in particular, is instrumental in distinguishing new and existing structural components within the 3D model, which is vital for filtering elements during the QTO process. Similarly, the extraction of Phase and Workset parameter values for all 3D elements is conducted to confirm their presence in the model. The Workset parameter values are further analyzed for naming convention consistency. An incorrect Workset name can lead to erroneous QTO results if these parameters are used for classifying steel and concrete quantities. Therefore, the automated workflow identifies concrete and steel components with inappropriate Workset names. Parameter values are extracted for structural materials to ensure that all elements have a material name aligned with the naming convention procedure, which is essential for QTO accuracy. Due to possible modeling errors, the geometry of steel elements might not align with the steel section type, potentially leading to incorrect steel quantity calculations. Consequently, steel elements' Nominal Weight and Length parameters are considered the most reliable metrics for quantity calculations. The automated workflow developed in this study extracts nominal weight parameter values to identify steel elements lacking these critical data. A comprehensive list of all warnings is automatically extracted and filtered to address critical warning messages in the BIM model. This step identifies warnings directly impacting QTO results, such as overlapping or duplicated elements. Subsequently, the automated system identifies the number of failing and passing elements to calculate the quality score for each part using the following formula:  
\[\text{Quality Score}=\text{P}\left(1-\frac{\text{Failing Elements}}{\text{Total Elements}}\right)*100 \tag{1}\]  
where:  
Failing = total number of failed elements;  
Total = total number of elements. This formula offers a clear and objective metric to assess the quality of each QC category within the BIM model. Figures 4 and 5 show the Data Extraction and Analysis Module's detailed workflow for the architectural and MEP BIM models' QC process, respectively. Each discipline's workflow includes general sections to be checked and discipline-specific considerations. For architectural BIM models, the module checks additional categories like doors and rooms, ensuring the accuracy of room element geometry and the appropriate codification of door elements.