Intelligent Inspection System for Concrete Strength and Surface Quality of Pumping Station

The importance of quality and safety control in concrete structures (such as pumping stations) has become increasingly prominent. To address issues in traditional concrete strength and surface quality inspection methods—such as low efficiency, high subjectivity, and lack of standardized processes—this study develops an intelligent concrete quality inspection system based on secondary Building Information Modeling (BIM) development. By integrating deep learning and 3D point cloud technology, the system achieves (1) strength prediction with an error margin of less than 10% and (2) sub-millimeter-level detection precision. The system utilizes deep neural networks to automatically assess surface quality from point cloud data and is implemented as Autodesk Revit plugin for practical applications. Experimental results demonstrate a 65% increase in inspection efficiency compared to traditional methods. This research provides a standardized and intelligent solution for concrete quality control and presents significant practical value for advancing the digital transformation of the construction industry.