Ramil' R. KHAKIMOVKazan State Power Engineering University (KSPEU), Kazan, Republic of Tatarstan, Russian Federation ramma19@mail.ru ORCID id: not available
Subject. In the context of the digital transformation of the construction industry, the key barrier to increasing economic efficiency is not the lack of data, but the inability of existing management systems to integrate and analyze heterogeneous information flows to build reliable forecasts. The separation of static information models (BIM) and dynamic equipment telemetry (IoT) generates an asymmetry of management decisions, leading to systematic budget overruns and delays in the implementation of investment and construction projects. Objectives. Development of an economic and mathematical methodology for predictive management of construction processes, which minimizes operational risks and increases the investment attractiveness of projects by integrating deep machine learning methods into the production planning contour. Methods. The research is based on the synthesis of methods of systems analysis, theory of economic risks and technologies of intellectual data processing. Using Monte Carlo stochastic modeling algorithms, a unique dataset has been generated that combines the attribute characteristics of BIM models (IFC classes, material volumes), telemetry flows of construction equipment, and external factors (weather conditions, logistical delays). Recurrent neural networks of the LSTM architecture with mathematical formalization of the optimization process of the objective function of economic losses are used to identify hidden nonlinear dependencies. Results. A methodology for integrating heterogeneous data has been developed and verified, including strict rules for time synchronization (?t = 1 hour) and semantic binding via globalId identifiers. A comparative analysis of four architectures (linear regression, Random Forest, XGBoost, LSTM) on a test sample demonstrated a statistically significant superiority of recurrent networks: the average absolute prediction error was reduced to 1.04 days (MAPE = 4.1%) with a coefficient of determination R2 = 0.94, which is 3 times more accurate than traditional methods and 38% more accurate than ensemble methods. algorithms of gradient boosting. Conclusions. The introduction of intelligent predictive management systems integrating BIM and IoT ensures the transition from reactive response to proactive risk management, creating an economic effect by reducing unproductive downtime, optimizing resource use and minimizing penalties for failure to comply with contractual obligations. The proposed methodology lays the foundation for the creation of self-learning digital counterparts of construction sites and can be scaled to enterprises of the investment and construction complex operating in a highly volatile environment.
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