Digital transformation of life cycle ­management processes for residential and engineering ­infrastructure facilities in complex development of territories

Main Article Content

Azariy Abramovich Lapidus
Liubov Andreevna Adamtsevich

Abstract

Modern projects of complex development of territories are characterized by technological, organizational and informational complexity. Technological challenges stem from the need to integrate heterogeneous systems while ensuring safety, reliability and sustainability of residential buildings and infrastructure facilities. Organizational complexity arises from the increasing number of participants in complex territory development projects, while information complexity results from exponentially growing volumes of heterogeneous data and requirements for rapid decision-making.


In this regard, the issue of ensuring end-to-end life cycle management of objects and consistency of decisions for the efficient use of resources is relevance.


The purpose of the research work is to analyze digital technologies for optimizing the life cycle management of residential buildings and infrastructure facilities in projects of complex development of territories.


As part of the presented study, an analysis of publications was conducted, according to keywords defined by the authors in the context of the topic under consideration. The international Scopus database served as the basis for forming specimens, according to keywords.


The analysis demonstrates that the introduction of digital twins, IoT, Big Data, BIM and machine learning allows achieving significant optimization in managing the life cycle of residential and engineering infrastructure facilities in projects of complex development of territories. Digital transformation fundamentally changes the approach to life cycle management, moving it from reactive to predictive and adaptive, which ensures the sustainability, reliability and safety of objects, resource savings and improved quality of the urban environment.

Article Details

Section

Official section

How to Cite

Digital transformation of life cycle ­management processes for residential and engineering ­infrastructure facilities in complex development of territories. (2025). Real Estate: Economics, Management, 2, 6-12. https://doi.org/10.22337/2073-8412-2025-2-6-12

References

Röck M., Hollberg A., Habert G., Passer A. LCA and BIM: Visualization of environmental potentials in building construction at early design stages // Building and Environment. 2018. No. 140. Рр. 153–161. DOI: 10.1016/j.buildenv.2018.05.006 DOI: https://doi.org/10.1016/j.buildenv.2018.05.006

Hollberg A., Genova G., Habert G. Evaluation of BIM-based LCA results for building design // Automation in Construction. 2019. Nо. 109. DOI: 10.1016/j.autcon.2019.102972 DOI: https://doi.org/10.1016/j.autcon.2019.102972

Najjar M., Figueiredo K., Palumbo M., Haddad A. Integration of BIM and LCA: Evaluating the environmental impacts of building materials at an early stage of designing a typical office building // Journal of Building Engineering. 2017. No. 14. Рр. 115–126. DOI: 10.1016/j.jobe.2017.10.005 DOI: https://doi.org/10.1016/j.jobe.2017.10.005

Zhuang D., Zhang X., Lu Y., Wang C., Jin X., Zhou X. et al. A performance data integrated BIM framework for building life-cycle energy efficiency and environmental optimization design // Automation in Construction. 2021. No. 127. DOI: 10.1016/j.autcon.2021.103712 DOI: https://doi.org/10.1016/j.autcon.2021.103712

Bovteev S.V., Petrochenko M.V., Zavodnova E.B. Applying of 4D modeling at preparation and construction stages // BIO Web of Conferences. 2024. No. 107. DOI: 10.1051/bioconf/202410706013 DOI: https://doi.org/10.1051/bioconf/202410706013

Bayat H., Ramezanianpour A.M. Utilizing building information modeling (BIM) in the operation phase of civil infrastructure to analyze reinforcement corrosion induced by carbonation // Innovative Infrastructure Solutions. 2024. No. 9. DOI: 10.1007/s41062-024-01707-y DOI: https://doi.org/10.1007/s41062-024-01707-y

Gao X., Pishdad-Bozorgi P. BIM-enabled facilities operation and maintenance:а review // Advanced Engineering Informatics. 2019. No. 39. Рр. 227–247. DOI: 10.1016/j.aei.2019.01.005 DOI: https://doi.org/10.1016/j.aei.2019.01.005

Moradabadi B., Noorzai E., Abbasi S. BIM-based optimization approach to reduce life cycle costs by focusing on the integration of construction and operation phases in office-commercial buildings // Journal of Building Engineering. 2024. No. 98. DOI: 10.1016/j.jobe. 2024.111126 DOI: https://doi.org/10.1016/j.jobe.2024.111126

Akbarieh A., Teferle F.N., O’donnell J. Semantic Material Bank: A web-based linked data approach for building decommissioning and material reuse, eWork and eBusiness in Architecture, Engineering and Construction — Proceedings of the 14th European Conference on Product and Process Modelling, ECPPM-2022. 2023. Рр. 69–76. DOI: 10.1201/9781003354222-9 DOI: https://doi.org/10.1201/9781003354222-9

Daniska D., Vrban B. Decommissioning planning: Empowering efficiency through BIM modelling and a single-source-of-truth framework // Nuclear Engineering and Design. 2023. No. 414. DOI: 10.1016/j.nucengdes.2023.112617 DOI: https://doi.org/10.1016/j.nucengdes.2023.112617

Sun J., Yi Man Li R., Deeprasert J. The Impact of BIM Technology on the Lifecycle Cost Control of Prefabricated Buildings: Evidence from China // Buildings. 2024. No. 14. DOI: 10.3390/buildings14123709 DOI: https://doi.org/10.3390/buildings14123709

Rostamiasl V., Jrade A. Integrating Building Information Modeling (BIM) and Life Cycle Cost Analysis (LCCA) to Evaluate the Economic Benefits of Designing Aging-In-Place Homes at the Conceptual Stage // Sustainability (Switzerland). 2024. No. 16. DOI: 10.3390/su16135743 DOI: https://doi.org/10.20944/preprints202405.0681.v1

Pan Y., Zhang L. A BIM-data mining integrated digital twin framework for advanced project management // Automation in Construction. 2021. No. 124. Р. 103564. DOI: 10.1016/j.autcon. 2021.103564 DOI: https://doi.org/10.1016/j.autcon.2021.103564

Deng M., Menassa C.C., Kamat V.R. From BIM to digital twins:а systematic review of the evolution of intelligent building representations in the AEC-FM industry // Journal of Information Technology in Construction. 2021. No. 26. Рр. 58–83. DOI: 10.36680/J.ITCON.2021.005 DOI: https://doi.org/10.36680/j.itcon.2021.005

Pan Y., Zhang L. Integrating BIM and AI for Smart Construction Management: Current Status and Future Directions // Archives of Computational Methods in Engineering. 2023. No. 30 (2). Рр. 1081–1110. DOI: 10.1007/s11831-022-09830-8 DOI: https://doi.org/10.1007/s11831-022-09830-8

He R., Li M., Gan V.J.L., Ma J. BIM-enabled computerized design and digital fabrication of industrialized buildings:а case study // Journal of Cleaner Production. 2021. No. 278. Р. 123505. DOI: 10.1016/j.jclepro.2020.123505 DOI: https://doi.org/10.1016/j.jclepro.2020.123505

Xia H., Liu Z., Efremochkina M., Liu X., Lin C. Study on city digital twin technologies for sustainable smart city design : а review and bibliometric analysis of geographic information system and building information modeling integration // Sustainable Cities and Society. 2022. Vol. 84. No. 19. DOI: 10.1016/j.scs.2022.104009 DOI: https://doi.org/10.1016/j.scs.2022.104009

Boje C., Guerriero A., Kubicki S., Rezgui Y. Towards a semantic Construction Digital Twin: Directions for future research // Automation in Construction. 2020. No. 114. Р. 103179. DOI: 10.1016/j.autcon.2020.103179 DOI: https://doi.org/10.1016/j.autcon.2020.103179

He X., Ai Q., Wang J., Pan B., Qiu R. Situation Awareness of Energy Internet of Things in Smart City Based on Digital Twin: From Digitization to Informatization // IEEE Internet of Things Journal. 2023. No. 10 (9). Рр. 7439–7458. DOI: 10.1109/JIOT.2022.3203823 DOI: https://doi.org/10.1109/JIOT.2022.3203823

Arsiwala A., Elghaish F., Zoher M. Digital twin with Machine learning for predictive monitoring of CO2 equivalent from existing buildings // Energy and Buildings. 2023. No. 284. Р. 112851. DOI: 10.1016/j.enbuild.2023.112851 DOI: https://doi.org/10.1016/j.enbuild.2023.112851

Wang W., Li X., Tang S., Guo H., Lv Z. Deep learning for assessment of environmental satisfaction using BIM big data in energy efficient building digital twins // Sustainable Energy Technologies and Assessments. 2022. No. 50. Р. 101897. DOI: 10.1016/j.seta.2021.101897 DOI: https://doi.org/10.1016/j.seta.2021.101897

Li C., Lu P., Zhu H., Zhang X., Zhu W. Intelligent Monitoring Platform and Application for Building Energy Using Information Based on Digital Twin // Energies. 2023. No. 16 (19). Р. 6839. DOI: 10.3390/en16196839 DOI: https://doi.org/10.3390/en16196839

Zhang X., Hua S., Qi J., Ruan Y. Progress and Prospects of New Smart City Construction: AI-based Big Data, Big Models and Big Computing Power // Journal of Geo Information Science. 2024.No. 26 (4). Рр. 779–789. DOI: 10.12082/dqxxkx.2024.240065

Liu P., Wang J., Sangaiah A.K., Xie Y., Yin X. Analysis and prediction of water quality using LSTM deep neural networks in IoT environment // Sustainability (Switzerland). 2019. No. 11 (7). Р. 2058. DOI: 10.3390/su1102058 DOI: https://doi.org/10.3390/su11072058

Zhdaneev O.V., Frolov K.N., Petrakov Y.A. Predictive Systems for the Well Drilling Operations, Studies in Systems // Decision and Control. 2021. No. 342. Рр. 347–368. DOI: 10.1007/978-3-030-66081-9_28 DOI: https://doi.org/10.1007/978-3-030-66081-9_28

Lige X., Hua S.Z., Feng S.Z. Road Machinery Fault Prediction Based on Big Data and Machine Learning : 2019 5th International Conference on Control // Automation and Robotics, ICCAR 2019. 2019. Рр. 536–540. DOI: 10.1109/ICCAR.2019.8813333 DOI: https://doi.org/10.1109/ICCAR.2019.8813333

Park J.-S., Ham H.-M., Ahn Y.-H. Expansion Joints Risk Prediction System Based on IoT Displacement Device // Electronics (Switzerland). 2023. No. 12 (12). Р. 2713. DOI: 10.3390/electronics12122713 DOI: https://doi.org/10.3390/electronics12122713

Similar Articles

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)