Automating 4D/5D Project Control via a BIM-to-BI Data Pipeline: Framework for Construction Data Workflows

On-site construction data management often relies on manual data entry, two-dimensional drawings, paper-based systems, and fragmented scheduling and costing workflows, leading to transcription errors, data duplication, and information latency. This paper presents SmartAEC, an automated data pipeline implemented as a custom C# plugin that extracts geometric and parametric data (volumes, material types, levels, dimensions) from 48 Building Information Modeling (BIM) element types in Autodesk Revit. The framework employs extract-transform-load logic to synchronize data with a Microsoft structured query language Server database via ADO.NET, in which BIM-derived quantities are transformed into four-dimensional (4D) (schedule) and five-dimensional (5D) (cost) analytics by linking them to user-defined labor productivity rates and material costs through automated C# calculations. The framework was validated through a retrospective paired-comparison case study of the concrete construction phase of a fast-track three-story building in Egypt, against audited as-built records. SmartAEC estimated the concrete phase duration at 55 days versus the 60-day actual (accuracy: 91.7%), compared with the traditional contractor estimate of 40 days (accuracy: 66.7%). For quantity takeoff, SmartAEC estimated 745.1 m 3 versus 748.2 m 3 actual (accuracy: 99.6%), compared with 735 m 3 traditional (accuracy: 98.2%). For total concrete cost, SmartAEC estimated 3,092,006 EGP versus 3,107,377 EGP actual (accuracy: 99.5%), compared with 3,057,519 EGP traditional (accuracy: 98.4%). The overall mean absolute percentage error was 3.1% for SmartAEC versus 12.2% for traditional methods, representing a 74.6% reduction in average estimation error. This study contributes a reproducible, validated BIM-to-Business Intelligence (BI) pipeline to automated 4D/5D project control, supported by preliminary single-case evidence. Limitations include the retrospective single-project design and dependence on user-defined productivity inputs.