## 7 Conclusions  
Periodic QCs of BIM models (e.g., weekly or monthly) are essential because BIM models are used to make decisions about buildings' design, construction, operation, and maintenance. If the models are not of high quality, these decisions will not be based on accurate or complete information. The primary emphasis of this study was on the development of an automated QC system for assessing the quality and consistency of designed BIM models. This system incorporates project-specific QC procedures and checklists, offering applicability to similar construction projects. The system comprises four principal components, including a user-friendly _Data Visualization Module_ and automation scripts designed for various disciplines to detect inconsistencies, errors, and problems in BIM models. The capability of the developed system was explored through a real case study.  
The key findings from this study are as follows:  
1. The automation workflows developed in this study were capable of reading, selecting, sorting, filtering, extracting, and transferring all QC data from STR, ARCH, MECH, PIP, and ELEC BIM models.
2. The generated QC reports enabled design teams to rapidly identify and correct failing elements within the BIM models. In the case study, 17, 67, 42, 29, and 55 failing elements were initially detected and listed from the project's STR, ARCH, ELEC, MECH, and PIP BIM models.
3. The Data Visualization Module significantly aided in prioritizing corrections by highlighting QC categories with the highest failure rates, both in terms of the number of failing elements and their quality scores.
4. The Data Visualization Module calculated the AQS values of 87.6%, 83.2%, and 56.2% for the case study's architectural, structural, and electrical BIM models, providing a comprehensive quality overview and alerting engineers and QC teams to potential issues.
5. The application of the QC system in a case study demonstrated a 30% reduction in failing elements for the architectural design team over four months, indicating the system's efficiency.
6. The system proved beneficial in identifying missing data or information within the BIM models. For example, in the case study STR BIM model, 21 structural elements did not have an assigned structural material parameter value, and 4 steel elements were identified as not having an assigned nominal weight parameter value.
7. Additionally, the Data Visualization Module facilitated the design teams' tracking of correction progress in the BIM models, which provided a clear overview of each discipline's improvements over time.  
This study underscores the importance of periodic QCs in BIM models, which are vital for informed decision making in building design, construction, operation, and maintenance. By enabling timely identification and correction of failing elements, the developed system can prevent problems leading to increased costs and delays, thereby enhancing the overall efficiency of BIM-based project management.  
Conceptualization, M.V.; Methodology, M.V.; Software, M.V.; Validation, M.V.; Formal analysis, M.V.; Investigation, M.V.; Resources, M.V.; Data curation, M.V.; Writing--original draft, M.V.; Writing--review & editing, M.V., I.I., F.V. and A.B.; Visualization, A.S.; Supervision, O.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality and privacy.  
The authors acknowledge [PERSON], the senior VDC coordinator at Pomerleau Inc., for his assistance in the preliminary development and testing of the Data Visualization Module.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Pomerleau Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.