## 2 Literature Review
They analyzed the output information and work conducted during the design phase within the design process, subsequently formulating detailed QC objectives for a series of cases. The method developed by [PERSON] et al. [34] was integrated into a general BIM methodology and its typical methods and strategies. They used customized checklists and executed queries on a database management system to assess the quality of BIM models. To confirm the accurate configuration of parameters within BIM models, they used the data list extracted from the IFC file. [PERSON] et al. [35] proposed integrated solutions to enhance existing quality management procedures through the support of an IFC-based working environment. They tried to connect IFC data and neural network algorithms to construction quality evaluations to improve the efficiency and accuracy of the assessment. [PERSON] and [PERSON] [36] used the IFC file format to automate checking compliance. Despite the widespread use of the IFC standard, its application is not without challenges. [PERSON] et al. [31] pointed out that while IFC files can represent BIM information effectively, they often do not fully adhere to the necessary standards, leading to data losses and model interpretation issues. This transformation from original proprietary formats to IFC often results in the loss of critical information, parametric data, and element connections, impacting the overall integrity of the model. [PERSON] and [PERSON] [37] observed changes in materials when models were converted to IFC, highlighting the format's limitations in preserving original data. Furthermore, despite its evolution, inconsistencies like data transfer losses persist in the IFC format [38; 39; 40; 41; 42; 43]. The IFC standards do not adequately support entities with unstructured quality-related information or relationships in the quality database. This gap becomes apparent with different user-defined QC parameters required for specific programming applications. While the IFC standard is extensible for using property sets, the need for unique and standard-compliant naming conventions renders the process less user-friendly. Additionally, practitioners often face challenges in controlling the quality and content of IFC models. This is partly due to limited specific knowledge and reliance on commercial systems that perform conversions, allowing for minimal customization [31]. In summary, while the IFC format provides a foundational framework for BIM-based quality control, it has limitations. The loss of information during format conversion, challenges in maintaining data integrity, and the format's inability to quickly provide user-defined QC parameters highlight the need for more robust and flexible solutions in BIM quality control. These challenges underscore the importance of developing a comprehensive and adaptable QC system that can effectively address these issues, enhancing the overall quality of BIM models. Table 1 describes some previous studies on BIM-based QC solutions and their limitations. Table 1 clearly shows that despite the significance of 3D model data QCs during the design phase in the AEC projects, studying and automating the QC process of BIM models during the preliminary and detailed design phases have not received adequate attention and the challenges in using IFC models in the process of BIM QCs still exist. To address these issues, the primary aim of this study is to develop an automated system explicitly designed to evaluate the quality of 3D BIM model data. This system will utilize a project-specific quality control checklist and established procedures. This innovative approach promises to enhance BIM models' accuracy, reliability, and overall quality during the preliminary and detailed design phases, thereby facilitating more efficient and effective project outcomes in the AEC industry.