Automation of geometric parameters control for reinforced concrete structures in building information modeling environment

Introduction. In current practice of reinforced concrete structures erection, traditional measurement control using manual tools followed by drafting schemes in CAD systems remains the most labor-intensive stage of technical supervision. The discreteness of such measurements and the lack of a direct link to the project digital environment complicate the rapid verification of completed work. The introduction of Building Information Modeling (BIM) technologies opens up opportunities for creating fundamentally new quality control mechanisms based on continuous reality scanning.

Aim. Substantiation of technological efficiency and development of a methodology for automated comparison of field measurement data (point clouds) with project BIM models to increase the speed of executive documentation formation.

Materials and methods. The study is based on the use of terrestrial laser scanning (TLS) as a tool for capturing the actual geometry of monolithic columns (a project in Kaliningrad). Data processing was carried out in the Revit environment. To compare “As-Built vs As-Designed” states, the method of aligning the point cloud and the information model using shared base coordinates was used. Mathematical estimation of the accuracy of the obtained point array relative to control measurements was carried out using the distance root mean squared (DRMS) indicator.

Results. The fundamental possibility of full automation of the process of identifying reinforced concrete structures geometric deviations in the BIM environment is proved. It has been established that automated comparison of the actual point cloud with project elements allows for the formation of executive schemes significantly faster than with the traditional approach. A comparative analysis showed a multiple reduction in labor costs for cameral data processing. It is noted that the direct transfer of deviation data to calculation complexes (LIRA-CAD allows for rapid verification calculations of the bearing capacity of elements whose parameters have exceeded the standard tolerances, which is technically unfeasible within traditional drafting in AutoCAD.

Conclusions. The use of BIM technologies in conjunction with laser scanning allows moving from selective manual inspection to systematic automated monitoring of reinforced concrete structures quality. This approach ensures acceptance transparency and updating of the building’s digital twin, minimizing time losses at the technical supervision stage.

1. Maalek R., Lichti D.D., Ruwanpura J.Y. Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction. Remote Sensing, 2019, vol. 11, no. 9, art. 1102. https://doi.org/10.3390/rs11091102.

2. Shu J., Li W., Zhang C., et al. Point cloud-based dimensional quality assessment of precast concrete components using deep learning. Journal of Building Engineering, 2023, vol. 70, no. 1, art. 106391. https://doi.org/10.1016/j.jobe.2023.106391.

3. Wang R., Zhang J., Qiu H., Sun J. Intelligent Inspection Method for Rebar Installation Quality of Reinforced Concrete Slab Based on Point Cloud Processing and Semantic Segmentation. Buildings, 2024, vol. 14, no. 11, art. 3693. https://doi.org/10.3390/buildings14113693.

4. Liu Z., Liu Z., Sun Z. Massive Point Cloud Processing for Efficient Construction Quality Inspection and Control. Sensors, 2024, vol. 24, no. 21, art. 6806. https://doi.org/10.3390/s24216806.

5. Choi Y., Park H.W., Mi Y., Song S. Crack Detection and Analysis of Concrete Structures Based on Neural Network and Clustering. Sensors, 2024, vol. 24, no. 6, art. 1725. https://doi.org/10.3390/s24061725.

6. Chen X., Li J., Huang S., et al. An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm. Sensors, 2021, vol. 21, no. 5, art. 1581. https://doi.org/10.3390/s21051581.

7. Постановление Правительства РФ от 05 марта 2021 г. № 331. Об утверждении Правил формирования и ведения информационной модели объекта капитального строительства // Собрание законодательства РФ. 2021. № 11. Ст. 1433. Режим доступа: http://government.ru/docs/all/133174/.

8. State Standard R 58944-2020. System of ensuring geometrical parameters accuracy in construction. Functional tolerances. Moscow: Standartinform Publ., 2020. (In Russian).

9. Kuzevanov, D.V. Reliability of out-of-center compressed reinforced concrete elements instrength calculation of normal sections [abstract of dissertation]. Moscow, 2012. (In Russian).

10. SP 63.13330.2018. Concrete and reinforced concrete structures. General provisions. Updated version of SNiP 52-01-2003. Moscow: The Ministry of Construction of Russia, 2018. (In Russian).

11. SP 70.13330.2012. Load-bearing and separating constructions. Updated version of SNiP 3.03.01-87. Moscow: The Ministry of Construction of Russia, 2012. (In Russian).