Combined use of the package-node method and artificial intelligence technologies in calendar-network planning of construction projects

Introduction. During a series of experiments on compressing the schedule of the calendar-network schedule for a construction project, a hypothesis was put forward and confirmed that the use of Advanced Work Packaging (AWP) construction method allows for a 25 % reduction in planned construction time without changing the duration of the operations performed or allocating additional resources. However, although this approach solves the problem of schedule compression on the one hand, on the other hand, it creates additional problems or tasks related to an increase in information flows and planning volumes. Current achievements in the field of digitalization allow us to put forward a new scientific hypothesis that the problem of high-level detail in planning data can be solved using artificial intelligence technology. The goal of the study is to reduce the time required for construction and installation work through detailed planning and high-quality project implementation according to a scenario referred to as “Scenario – AI”. To this end, it is proposed to move the start of detailed planning to the initial stage, known as “Business Planning and Life Cycle Modeling”. Achieving this goal involves the use of machine learning (ML) and artificial intelligence (AI) technologies, which are very relevant in modern construction management.

Materials and methods. The process model Planning & Scheduling was adopted as the initial methodology of calendar-network planning. The study examines different scenarios of planning and implementation of a construction project: one uses data with a level of detail increasing as it is received, and the other scenario uses the Batch-node construction method.

Results. The conducted research and experiments confirmed the relevance of integrating machine learning (ML) and artificial intelligence (AI) into the construction industry. This area is of significant scientific and practical interest, opening up new horizons for improving the productivity, safety and quality of construction projects. The authors analyzed neural network technologies for solving time management problems and other calendar and network planning problems. The final solution to the research problem was the initiation of the project “Development and implementation of the AI Planner and AI Assistant Project Manager system”.

Conclusions. The study concluded that the integration of ML and AI into the construction industry is an important step towards sustainable development of the industry, which is confirmed by the results of the study and requires further study to confirm and develop hypotheses.

1. Slepushkin D.V., Burlov D.Yu. Artificial intelligence and automation of design processes in construction: a bibliometric analysis. Vestnik MGSU [Monthly Journal on Construction and Architecture]. 2025; 20(3):440-455. DOI: 10.22227/1997-0935.2025.3.440-455. EDN MYHCAL. (rus.).

2. Suleimanova L.A., Obaidi A.A.H., Ryabchevsky I.S. Life cycle management of capital construction projects using neural network forecasting of building heat loss. Belgorod, Belgorod State Technological University named after V.G. Shukhov, 2024; 164. EDN UYZPHS. (rus.).

3. Petrukhin A.B., Shcherbakova N.A. Artificial intelligence in the construction industry. Vestnik of Volga State University of Technology. Series: Materials. Constructions. Technologies. 2024; 2:67-77. DOI: 10.25686/2542-114X.2024.2.67. EDN VJPORE.(rus.).

4. Gazarov A.R. Advantages of using artificial intelligence in the field of construction. Izvestiya Tula State University. 2020; 4:136-139. EDN DZQOPN. (rus.).

5. Kolchin V.N. The specifics of the use of “artificial intelligence” technology in construction. Innovation & Investment. 2022; 3:250-253. EDN JJLECU. (rus.).

6. Baduge S.K., Thilakarathna S., Perera J.S., Arashpour M., Sharafi P., Teodosio B. et al. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction. 2022; 141:104440. DOI: 10.1016/j.autcon.2022.104440

7. Karamanyants M.B. Changes in the construction industry with the active implementation of technology using artificial intelligence (AI). Construction Economics. 2023; 9:141-145. EDN SBRLCQ. (rus.).

8. Gorodnova N.V. Application of artificial intelligence and nanotechnology in the investment and construction sector in Russia. Vestnik NSUEM. 2021; 3:81-95. DOI: 10.34020/2073-6495-2021-3-081-095. EDN KWCGFR. (rus.)

9. Korol S.P., Korol R.A. Algorithmic approach in network modeling in construction: graphical solutions and optimization tasks. Russian Journal of Housing Research. 2023; 10(3):317-332. DOI: 10.18334/zhs.10.3.118842. EDN CTTTEW. (rus.).

10. Barkalov S.A., Moiseev S.I., Serebryakova E.A. Application of Markov random processes for risk assessment in construction projects. Modern problems of project management in the investment and construction sphere and nature management : proceedings of the XV International scientific and practical conference. 2025; 99-104. EDN IZUOGT. (rus.).

11. Yarkova O.N., Sidorenko N.A. Modelling construction time by discrete Markov chains. Engineering journal of Don. 2024; 2(110):506-519. EDN QUFHYZ. (rus.).

12. Khristoforova K.A., Demidova V.S., Krivogina D.N. Managing calendar and network schedules of construction in an unstable world. Engineering journal of Don. 2022; 12(96):707-720. EDN ODYQZZ. (rus.).

13. Petrochenko M.V., Nedviga P.N., Kukina A.A., Sherstyuk V.V. Classification of information models in BIM using artificial intelligence algorithms. Vestnik MGSU [Monthly Journal on Construction and Architecture]. 2022; 17(11):1537-1550. DOI: 10.22227/1997-0935.2022.11.1537-1550. EDN JFYSSO. (rus.).

14. Ginzburg A.V.E., Ryzhkova A.I. Artificial intelligence capabilities for increasing organizational-technological reliability of construction. Vestnik MGSU [Proceedings of the Moscow State University of Civil Engineering]. 2018; 13(1):(112):7-13. DOI: 10.22227/1997-0935.2018.1.7-13. EDN XCIOMJ. (rus.).

15. Telichenko V.I., Lapidus A.A., Slesarev M.Yu. Analysis and synthesis of images of environmentally oriented innovative technologies of construction production. Vestnik MGSU [Monthly Journal on Construction and Architecture]. 2023; 18(8):1298-1305. DOI: 10.22227/1997-0935.2023.8.1298-1305. EDN RNDOCL. (rus.).

16. Petrukhin A.B., Shcherbakova N.A. Artificial intelligence in the construction industry. Vestnik of Volga State University of Technology Series. 2024; 2:67-77. DOI: 10.25686/2542-114X.2024.2.67. EDN VJPORE. (rus.).

17. Oluleye B.I., Chan D.W., Antwi-Afari P. Adopting Artificial Intelligence for enhancing the implementation of systemic circularity in the construction industry : a critical review. Sustainable Production and Consumption. 2023; 35:509-524. DOI: 10.1016/j.spc.2022.12.002

18. Ghosh A., Sufian A., Sultana F., Chakrabarti A., De D. Fundamental Concepts of Convolutional Neural Network. Intelligent Systems Reference Library. 2019; 519-567. DOI: 10.1007/978-3-030-32644-9_36

19. Joshi K. Study of Tesseract OCR. GLS KALP: Journal of Multidisciplinary Studies. 2021; 1(2):41-50. DOI: 10.69974/glskalp.01.02.54

20. Le C.C. BERT (Bidirectional Encoder Representations from Transformers) Architecture. 2025. DOI: 10.13140/RG.2.2.22192.67845

21. Ahmadi E., Muley S., Wang C. Automatic construction accident report analysis using large language models (LLMs). Journal of Intelligent Construction. 2025; 3(1):1-10. DOI: 10.26599/JIC.2024.918-0039

22. Zhang C., Deng Y., Lin X., Wang B., Ng D., Ye H. et al. 100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models. ArXiv. 2025; 1-37. DOI: 10.48550/arXiv.2505.00551

23. Wang H., Zhang D., Li J., Feng Z., Zhang F. Entropy-Optimized Dynamic Text Segmentation and RAG-Enhanced LLMs for Construction Engineering Knowledge Base. Applied Sciences. 2025; 15(6):3134. DOI: 10.3390/app15063134

24. Pan X., Yang T.T. Y., Liu R., Xiao Y., Xie F. A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes. Journal of Intelligent Construction. 2025; 3(2):1-11. DOI: 10.26599/jic.2025.9180086

25. Belaroussi R. Subjective Assessment of a Built Environment by ChatGPT, Gemini and Grok: Comparison with Architecture, Engineering and Construction Expert Perception. Big Data and Cognitive Computing. 2025; 9(4):100. DOI: 10.3390/bdcc9040100

26. Langford A., Shah A., Gupta A., Bhatter A., Goyal A., Mathur A. et al. The Amazon Nova Family of Models: Technical Report and Model Card. ArXiv. 2025. DOI: 10.48550/arXiv.2506.12103

27. Pujari N.K., Miriyala S.S., Mitra K. Generative Adversarial Networks for Modelling Uncertainties in Wind Farm Design. Engineering Optimization: Methods and Applications. 2025; 173-192. DOI: 10.1007/978-981-97-7909-3_10

28. Williams A.S. EVM-based Risk Management in Construction Projects: A Case Study. 2025.

29. Garudasu S., Byri A., Nadukuru S., Goel O., Singh N. Building interactive dashboards for improved decision-making: a guide to power bi and dax. International Journal of Worldwide Engineering Research. 2025; 188-209.