The use of digital twin technology to optimize the operation of the construction machinery fleet in real time
How to cite
Abstract
This study is devoted to the analysis of the possibilities of using digital twin technology to optimize the management of a fleet of construction equipment in real time. The paper considers an integrated approach to the creation of virtual models that ensure monitoring, forecasting and improving the efficiency of the use of construction machinery and mechanisms. In the course of the research, a methodology was developed for integrating telemetry, geo-positioning and technical condition data of construction equipment into a single information system using machine learning algorithms. The empirical testing of the proposed model was carried out on the basis of a construction company implementing large infrastructure projects. The results demonstrate a significant increase in fleet utilization by 27.5%, a 24.3% reduction in operating costs, and a 31.8% reduction in equipment downtime. Practical recommendations are formulated for the introduction of digital twin technology in educational institutions that train construction specialists, which ensures the formation of digital competencies necessary for effective management of construction processes. The model proposed in the study is of theoretical and practical value for improving the management system of material resources of educational organizations implementing infrastructure projects.
Keywords
References
Абрамов В.И., Гордеев В.В., Столяров А.Д. Цифровые двойники: характеристики, типология, практики развития // Вопросы инновационной экономики. 2024. № 14(3). С. 691-716.
Травушкина А.А., Щелокова А.Н., Стеценко Е.П. Обзор перспектив развития технологии цифровых двойников продуктов, услуг и сервисов в секторе материального производства // Вопросы инновационной экономики. 2022. № 12(3). рр. 1593-1612.
Ahmed S., Hossain M.M., Hoque I. Digital twins in the construction industry: A comprehensive review of current implementations, enabling technologies and future directions // Sustainability. 2023. № 15(14).рр. 80-109.
Al-Sehrawy R., Kumar B., Watson R. Technologies for digital twin applications in construction // Automation in construction. 2023. № 147.рр. 104-705.
Ashtarout A., Nassereddine H., AbdulBaky N., AbouKansour A., Tannoury J., Urban H., Schranz C. Digital twins in the construction industry: A perspective of practitioners and building authority // Frontiers in built environment. 2022. № 8. pp. 671-834.
Boje C., Guerriero A., Kubicki S., Rezgui Y. Towards a semantic construction digital twin: directions for future research // Automation in construction. 2020. № 114.рр. 103-179.
Chen K., Lu W., Peng Y., Rowlinson S., Huang G.Q. Bridging BIM and building: From a literature review to an integrated conceptual framework // International journal of project management. 2015. № 33(6). pp. 1405-1416.
Darabseh M., Enshaeifar S. Digital twin used in real-time monitoring of operations performed on CNC technological equipment // Applied sciences. 2024. № 14(22). рр. 88-100.
Farsi M., Daneshkhah A., Hosseinian-Far A., Jahankhani H. Digital twin technologies and Smart Cities. Berlin, Hoboken: Springer International Publishing, 2020.
Liu Z., Xie X., Jia S., Chen J. The Role of BIM in Integrating Digital Twin in Building Construction: A literature review // Sustainability. 2023. № 15(13). рр. 62-104.
Mittal S., Khan M.A., Romero D., Wuest T. Smart manufacturing: Characteristics, technologies and enabling factors // Proceedings of the Institution of Mechanical Engineers. Vol. B: Journal of engineering manufacture. 2019. № 233(5). рр. 1342-1361.
Opoku D.G.J., Perera S., Osei-Kyei R., Rashidi M. Digital twin application in the construction industry: A literature review // Journal of building engineering. 2021. № 40. рр. 102-726.
Pan Y., Zhang L. A BIM-data mining integrated digital twin framework for advanced project management // Automation in construction. 2021. № 124. рр. 103-564.
Rodrigues F., Sousa M., Cardoso C. Digital twin and industry 4.0 enablers in building and construction: A survey // Buildings. 2022. 12(11). pp. 20-44.
Tao F., Zhang H., Liu A., Nee A.Y. Digital twin in Industry: State-of-the-Art // IEEE Transactions on Industrial Informatics. 2019. № 15(4). рр. 2405-2415.