## 5 Validation Using a Case Study
Figure 10: The algorithm used in the automation script to identify concrete elements within the steel and wood worksets in the structural BIM model. According to Figure 13, four structural foundation elements lacked volume parameter values. Additionally, 21 structural elements were identified as not having assigned structural materials, potentially impacting the model's integrity. Another notable observation was the absence of nominal weight values for four steel elements, underscoring a gap in the model's data completeness. Furthermore, the model exhibited 62 instances of \"Overlap\" warning messages, indicating potential geometric conflicts within the model's structure. It is important to note that columns in the report displaying \"No Value\", such as in \"Area for Concrete Elements\", \"Phase\", and \"Duplicate Warnings\", signified the absence of issues within these specific QC categories in the BIM model. This indicated a level of completeness and accuracy in these aspects of the model. The Excel-based QC report files were uploaded to the ProjectWise platform to enhance communication and foster collaboration among project stakeholders. This integration facilitates a connection with the Data Visualization Module. As previously mentioned, the Data Visualization Module comprises six key components: Home_Checklist, STR, ARCH, ELEC, MECH, and PIP. Figure 14 shows the Home_Checklist section of the module for the case study BIM models. This section provides a comprehensive overview of each discipline, listing the total counts of failed and passed BIM elements. The data presented in the figure reveal that the number of elements failing QC checks in the STR, ARCH, ELEC, MECH, and PIP models are 14, 165, 125, 51, 46, and 109, respectively. This breakdown clearly and concisely represents the QC status across different disciplines within the BIM models. Figure 11: The algorithm used in the automation script to identify concrete elements within the steel workset in the structural BIM model. Figure 12: The result of the summary statistics section for one of the architectural BIM models. Figure 13 presents a segment of the report for the structural BIM model. This section explicitly lists failing elements, accompanied by their unique IDs. This format was particularly designed to aid engineers in efficiently identifying and modifying problematic elements within the BIM model. Figure 14: The Home Checklist section of the Data Visualization Module. Figure 13: List of failing elements’ names and their corresponding unique IDs. In Figures 15-17, the results from the case study's architectural (ARCH), structural (STR), and electrical (ELEC) BIM models are presented, describing the outcomes based on specific checklists for each discipline. The section consists of different components: Percentage Fail Organized by Name, Category (Name, Quality Score, Sum of Fails, and Sum of Passes), Select Category, and TRACKING FAILS graph. The Percentage Fail Organized by Name component provides a comparative analysis of each QC category's failure rate relative to the total failure rate. For instance, Figure 15 reveals that within the ARCH checklist, the category \"Rooms with Enclosed Area\" exhibited the highest failure rate at 58.2%. This was followed by \"Door Element Code (started with specified code)\" at 22.4%, and \"Door Element Code\" at 14.9%. Such detailed insights enable engineers to identify which specific cases are lagging in terms of quality based on the number of BIM elements involved. Further, the \"Category\" table provides additional data, including the quality score and the number of failing and passing elements for each QC category. According to the Category table, categories such as \"Element Phase\", \"Element Surface\", \"Element Workset\", \"Family Name\", and \"Workset Name\" achieved a quality score of 100%, indicating no failures in these areas. Conversely, the \"Door Element Code (started with specified code)\" category recorded a relatively lower quality score of 50%.