## 3 Research Methodology
Categories like \"element phase\" and \"element workset\" are reviewed to ensure they are consistent with the project timeline and segmentation. Categories like \"door element code\" and \"MEP QC\" significantly impact multiple project aspects. Figure 1 shows how much these QC categories influence various project scopes. The \"QTO\" category is significantly affected (73%), emphasizing its importance in cost estimation and budgeting. \"BIM Modeling Accuracy\" (35%), \"Coordination\" (19%), and \"Naming Convention\" (19%) are also significantly impacted, showing that these QC categories play a vital role in different project aspects. This detailed analysis of Table 2 and Figure 1 helps us understand the QC categories and their broad impact on the BIM project. This comprehensive QC approach aims to improve the accuracy, efficiency, and reliability of BIM models, ensuring they meet the high standards required for a successful project in all disciplines involved. As shown in Figure 2, the proposed framework is a multi-component system designed to enhance the QC of BIM models across various engineering disciplines. This framework comprises four main components: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. Engineering teams supply the BIM model for each discipline, forming this framework's foundation. The primary function of this system is to check the compliance of the design BIM models with the customized QC checklists proposed in this study. To achieve this, a specialized module for each discipline has been developed to automatically extract and analyze the designated QC information from BIM models. In the Data Extraction and Analysis Module, QTO, coordination, naming convention, modeling errors, and identity-related parameter data, as specified in the QC checklist, are extracted from architectural, structural, and MEP BIM models to assess their quality and compliance status automatically. The module, developed in Dynamo, operates by reading, sorting, and filtering the extracted data. It then verifies their compliance against the QC checklist, identifying which elements pass or fail, calculating the quality score for each QC category, and subsequently transferring this information to the Data Storage Module. The Data Storage Module serves as a repository  
Figure 1: The percentage contribution of specified scopes affected by the QC categories. for the processed information. It stores the number of failed and passing elements, the quality score for each QC category, and a list of failed elements, including their unique ID and any critical warning messages that could impact QTO and coordination outputs. While the data can be seamlessly moved to an external database like MySQL, this study utilizes Microsoft Excel for its simplicity and accessibility. The QC Excel files are subsequently uploaded to ProjectWise in the cloud, facilitating smooth integration with the Data Visualization Module in Power BI. A user-friendly dashboard was designed in Power BI to display the QC process outputs effectively and track changes over time. This component provides a clear visual representation of the QC status, facilitating more straightforward interpretation and decision making. The Data Extraction and Analysis Module, developed for each discipline, is distinct due to the different parameter names and filtering techniques required. Figure 3 details the workflow for the structural BIM model's QC process. This automated workflow includes multiple steps and filtering methods, beginning with the structural BIM model, listing all failing elements with their unique IDs, and calculating the quality score for each QC category. Because the main 3D view of the BIM model might contain elements from other disciplines for coordination purposes, the export 3D view, provided by the BIM modeler to be used in the coordination tools such as Navisworks or BIM 360 Glue, is used for the QC process.