Organizational and technological solutions for managing the life cycle of construction projects in integrated territorial development projects

Introduction. The efficiency of implementation of integrated territorial development projects (ITDP) directly depends on the applied management methods. In this regard, the issue of transition from traditional object-oriented management to integrated management of the life cycle of the entire project becomes relevant. This leads to the need to develop new system criteria for classification and selection of organizational and technological solutions (OTS) for each stage of the life cycle of construction projects. The purpose of the study was to develop a system classification of OTS for life cycle management of construction projects in ITD projects based on the system property introduced by the authors — autonomy.

Materials and methods. The methodological basis of the study is the general theory of systems, principles and tools of system engineering of construction, systems analysis, modeling and risk management. Construction objects in ITD projects are considered as complex systems interacting with the external environment and decomposed into subsystems, components and elements.

Results. As a result, the authors formulated the essence of the concept of autonomy of a construction object as an emergent property characterizing its ability to function in conditions of uncertainty of the external environment. A four-level classification of OTS for ITD objects is proposed, a multi-criteria structure for selecting and forming OTS is developed, integrating such parameters as the scale of the system, the stage of the life cycle, the level of management and the level of autonomy.

Conclusions. The proposed classification provides a comprehensive toolkit for making management decisions, allowing for a targeted impact on the parameters of the construction object to achieve the required level of autonomy throughout the life cycle. The results obtained provide the basis for developing methods for predictive and monitoring management of ITD objects. Further research by the authors will be devoted to issues of managing the parameters of construction objects to ensure their autonomy within the specified time frame, considering the existing restrictions and determining the boundaries of autonomy.

1. Adamtsevich L.A. Life cycle management of residential buildings and infrastructure facilities in projects of integrated development of territories. Vestnik MGSU [Monthly Journal on Construction and Architecture]. 2025; 20(6):957-966. DOI: 10.22227/1997-0935.2025.6.957-966 (rus.).

2. Osipenkova I.G. Theoretical and practical aspects of the development of organizational and technological solutions in modern conditions. Engineering journal of Don. 2023; 3(99):436-444. EDN PBHJNQ. (rus.).

3. Bidov T.Kh., Kotlyarov M.A., Akhverdashvili G.G., Baychorov R.H., Urusov A.A. Organizational and technological solutions affecting the effectiveness of the construction of monolithic structures in the organization of construction of residential buildings and structures. Izvestiya Tula State University. 2020; 6:175-182. EDN LETPIH. (rus.).

4. Demidenko O.V. Innovative organizational and technological solutions for energy-efficient construction. Bulletin of the Omsk Regional Institute. 2018; 2:65-70. EDN EIUFZU. (rus.).

5. Batal’tsev A.V. Problems of organizational and technological solutions in the construction and operation of buildings. Fotinskie Readings. 2014; 1(1):261-263. EDN XXZRYH. (rus.).

6. Yeskaliyev M.Zh., Mukhametzyanov Z.R. Study of the current state of development of organizational and technological solutions in the construction of facilities. Construction Economics. 2022; 2(74):52-60. EDN PSUNYE. (rus.).

7. Dement’eva M., Mazurin D. Organization of construction waste processing processes using “smart demolition” technology. Construction and Architecture. 2023; 11(4):10. DOI: 10.29039/2308-0191-2023-11-4-10-10. EDN SJYPDO. (rus.).

8. Gong H., Su D., Zeng S., Chen X. Parallel simulation and prediction techniques for digital twins in urban underground spaces. Automation in Construction. 2025; 175:106212. DOI: 10.1016/j.autcon.2025.106212

9. El-Shorbagy M.A., Bouaouda A., Abualigah L., Hashim F.A. Stochastic Fractal Search: A Decade Comprehensive Review on Its Theory, Variants, and Applications. Computer Modeling in Engineering & Sciences. 2025; 142(3):2339-2404. DOI: 10.32604/cmes.2025.061028

10. Fan C., Ding Y., Liu X., Yang K. A review of crack research in concrete structures based on data-driven and intelligent algorithms. Structures. 2025; 75:108800. DOI: 10.1016/j.istruc.2025.108800

11. Peivaste I., Belouettar S., Mercuri F., Fantuzzi N., Dehghani H., Izadi R. et al. Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories. Composite Structures. 2025; 372:119419. DOI: 10.1016/j.compstruct.2025.119419

12. Kookalani S., Parn E., Brilakis I., Dirar S., Theofanous M., Faramarzi A. et al. Trajectory of building and structural design automation from generative design towards the integration of deep generative models and optimization : a review. Journal of Building Engineering. 2024; 97:110972. DOI: 10.1016/j.jobe.2024.110972

13. Zhang X., Zheng J., Li P., Yang Y., Ye Y., Causone F. et al. A review of building digital twins: Framework and enabling technologies. Journal of Building Engineering. 2025; 111:113117. DOI: 10.1016/j.jobe.2025.113117

14. Thomsen A.M.S., Bjørnskov J., Jradi M. Ontology-based Digital Twins for informed decision-making: Scenario testing and optimization in hospital building operations. Journal of Building Engineering. 2025; 112:113594. DOI: 10.1016/j.jobe.2025.113594

15. Yoon S., Song J., Li J. Ontology-enabled AI agent-driven intelligent digital twins for building operations and maintenance. Journal of Building Engineering. 2025; 108:112802. DOI: 10.1016/j.jobe.2025.112802

16. Xue J., Xun X., Wang H., Luo Q. Dynamic generation method for structural components to achieve digital twin displays of building construction progress. Automation in Construction. 2025; 178:106406. DOI: 10.1016/j.autcon.2025.106406

17. Elshaboury N., AlMetwaly W.M., Hesham A., Abbas A. Integrated BIM-GIS framework for holistic building stock assessment using 5D geo-modeling and digital twin concepts. Journal of Building Engineering. 2025; 111:113391. DOI: 10.1016/j.jobe.2025.113391

18. Ghansah F.A. Digital twins for smart building at the facility management stage: a systematic review of enablers, applications and challenges. Smart and Sustainable Built Environment. 2025; 14(4):1194-1229. DOI: 10.1108/SASBE-10-2023-0298

19. Yan J., Lu Q., Li N., Chen L., Pitt M. Common data environment for digital twins from building to city levels. Automation in Construction. 2025; 174:106131. DOI: 10.1016/j.autcon.2025.106131

20. Xu Y., Zhang J., Qin H., Zhou H., Yang Z. Digital twin-enabled hybrid deep evolutionary framework for smart building sustainable infrastructure management. Sustainable Energy Technologies and Assessments. 2024; 65:103773. DOI: 10.1016/j.seta.2024.103773