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An integrated BIM–AI model for event-driven construction management

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Parent Category: 2026

Category: Content №2 2026

Created on 25 April 2026

Last Updated on 25 April 2026

Published on 30 November -0001

Written by M. O. Borodin, I. M. Iliev, T. V. Tkach, O. V. Nesterova

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Authors:

M. O. Borodin*, orcid.org/0000-0003-0513-3876, Ukrainian State University of Science and Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I. M. Iliev, orcid.org/0000-0002-9515-7734, Ukrainian State University of Science and Technology, Dnipro, Ukraine

T. V. Tkach, orcid.org/0000-0002-9433-7514, Ukrainian State University of Science and Technology, Dnipro, Ukraine

O. V. Nesterova, orcid.org/0000-0003-1035-6572, Ukrainian State University of Science and Technology, Dnipro, Ukraine

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2026, (2): 112 - 120

https://doi.org/10.33271/nvngu/2026-2/112

Abstract:

Purpose. The study aims to develop an integrated event-driven system for managing construction processes that combines BIM technologies, digital twins, and artificial intelligence algorithms to enhance the flexibility and reliability of scheduling under uncertainty.

Methodology. The research applies a combination of Building Information Modelling (BIM), event formalization based on the “event–condition–action” principle, as well as the mathematical framework of finite-state machines and task-scheduling optimization algorithms. Machine learning methods were used to forecast task durations, while integration with a digital twin enabled synchronization between planned and actual data.

Findings. The system architecture was developed to respond to events in real time, enabling dynamic adaptation of the construction schedule. A formalized management model was created, enabling automated transitions between task states and optimization of resource allocation. Case studies have demonstrated a reduction in overall project duration of up to 5 % compared to traditional planning methods, as well as increased transparency of management decisions.

Originality. An integrated BIM–AI event-driven management model is proposed, which combines finite-state modelling, machine learning algorithms, and digital twin principles for adaptive scheduling. For the first time, an architecture has been developed that can operate in a hybrid mode with the use of edge devices to ensure autonomy in case of central system failures.

Practical value. The results can be applied to improve construction project organization under conditions of high uncertainty, particularly in the context of infrastructure recovery in Ukraine. The proposed system helps mitigate risks of delays, optimize resource allocation, and extend the capabilities of construction professionals by automating routine control and planning tasks.

Keywords: construction, event-driven management, digital twin, artificial intelligence, scheduling

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