Development of algorithms for optimizing technological processes for the installation of monolithic reinforced concrete structures using robotic systems
How to cite
Abstract
This article presents the results of a study on the development and implementation of optimization algorithms for automating technological processes for the installation of monolithic reinforced concrete structures using robotic complexes. The research is aimed at improving the efficiency of construction processes by minimizing labor costs, reducing construction time and reducing material consumption while improving the quality of structures. As part of the work, multiparametric optimization algorithms for controlling robotic systems were developed and tested, including genetic algorithms, swarm intelligence and topological optimization methods. Experimental testing of the proposed solutions on real construction sites has shown a significant increase in economic efficiency: reduction of installation time by 28-35%, reduction of labor costs by 45-52% and reduction of material consumption by 18-22%. The integrated digitalization of the design and installation processes has made it possible to create an integrated automated control system for construction processes, adaptive to changing construction site conditions and technological requirements. The results obtained demonstrate the prospects for the implementation of the proposed algorithms and technologies for the transformation of the construction industry in the direction of Construction 4.0.
Keywords
Funding
References
Anane W., Iordanova I., Ouellet-Plamondon C. BIM-driven computational design for robotic manufacturing in off-site construction: an integrated design-to-manufacturing (DtM) approach // Automation in construction. 2023. 150. pp. 104-782.
Daleyev D. Potential of structural multi-objective optimization of reinforced concrete slabs in the context of sustainable development // Proceedings of the Creative construction conf. 2023. 2023.
Gamil Y. Machine learning in concrete technology: A review of current researches, trends, and applications // Frontiers in built environment. 2023. № 9. pp. 114-591.
Gappmaier P., Reichenbach S., Kromoser B. Automated production process for structure-optimised concrete elements // Proceedings of the IASS Annual symposium 2023. 2023. pp. 1577-1585.
Jeong J.H., Lee J., Lee H. Deep reinforcement learning for automated design of reinforced concrete structures // Computer-aided civil and infrastructure engineering. 2021. № 36(7). pp. 912-930.
Johns R.L., Wermelinger M., Mascaro R., Jud D., Hurkxkens I., Vasey L., Chli M., Gramazio F., Kohler M., Hutter M. A framework for robotic excavation and dry stone construction using on-site materials. Science Robotics. 2023. № 8(84). eabp9758.
Kim S., Peavy M., Huang P.C. Development of BIM-integrated construction robot task planning and simulation system // Automation in construction. 2021. № 127. pp 103-720.
Mechtcherine V., Nerella V.N., Will F., Nather M., Otto J., Krause M. Large-scale digital concrete construction – CONPrint3D concept for on-site, monolithic 3D-printing // Automation in construction. 2019. № 107. pp. 102-933.
Pan Y., Zhang L. Roles of artificial intelligence in construction engineering and management: A critical review and future trends // Automation in construction. 2021. № 122. pp. 103-517.
Sherif M., Nassar K., Hosny O. Automated BIM-based structural design and cost optimization model for reinforced concrete buildings. Scientific Reports. 2022. № 12. pp. 16-216.
Velichki V. Production process automation for construction of monolithic buildings and structures // Journal of civil, construction and environmental engineering. 2021. № 6(3). pp. 87-91.
Wang D., Ren B., Cui B., Wang J., Wang X., Guan T. Real-time monitoring for vibration quality of fresh concrete using convolutional neural networks and IoT technology // Automation in construction. 2021. № 123. pp. 103-510.
Wang X., Jia L., Jia Z., Zhang C., Chen Y., Ma L., Wang Z., Deng Z., Banthia N., Zhang Y. Optimization of 3D printing concrete with coarse aggregate via proper mix design and printing process. // Journal of building engineering. 2022. № 56. pp. 104-745.
Xu Z., Song T., Guo S., Peng J., Zeng L., Zhu M. Robotics technologies aided for 3D printing in construction: a review // International journal of advanced manufacturing technology. 2023. № 118(11). pp. 3559-3574.
Zhao S., Wang Q., Fang X., Wei Liang Y., Cao C.Z., Li L., Liu C., Wang K. Application and development of autonomous robots in concrete construction: challenges and opportunities // Drones, 2022. № 6(12). P. 424.
Published
Issue
Section
Metrics
Rights and terms of use
Copyright holder: STROITEL'NYE I DOROZHNYE MASHINY, 2025
Terms of use of the full text: All rights reserved