The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models’ data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements.

Accurate quantity takeoff (QTO) is essential for cost estimation and project planning in the construction industry. However, current practices are often fragmented and rely on manual or semi-automated processes, leading to inefficiencies and errors. This study introduces a cloud-based framework that integrates automated QTO with a rule-based Quantity Precision Check (QPC) to ensure that quantities are derived only from validated and consistent BIM data. The framework is designed to be scalable and compatible with open data standards, supporting collaboration across teams and disciplines. A case study demonstrates the implementation of the system using structural and architectural models, where automated validation detected parameter inconsistencies and significantly improved the accuracy and reliability of takeoff results. To evaluate the system’s effectiveness, the study proposes five quantitative validation metrics, Inconsistency Detection Rate (IDR), Parameter Consistency Rate (PCR), Quantity Accuracy Improvement (QAI), Change Impact Tracking (CIT), and Automated Reporting Efficiency (ARE). These indicators are newly introduced in this study to address the absence of standardized metrics for automated QTO with pre-takeoff, rule-based validation. However, the current validation was limited to a single project and discipline-specific rule set, suggesting that broader testing across mechanical, electrical, and infrastructure models is needed to fully confirm scalability and generalizability. The proposed approach provides both researchers and practitioners with a replicable, transparent methodology for advancing digital construction practices and improving the quality and efficiency of BIM-based estimation processes.