INTELLIGENT SYSTEM FOR ADAPTIVE CONTROL OF THE TECHNOLOGICAL PROCESS OF LAYING ASPHALT CONCRETE BASED ON NEURAL NETWORK MODELS
DOI:
https://doi.org/10.20535/kpisn.2026.1.350095Keywords:
automated systems, adaptive control, artificial intelligence, neural networks, asphalt concrete, closed system, quality predictionAbstract
Background. The quality and durability of asphalt concrete pavement directly depends on compliance with technological parameters during its laying process. Any violation of such parameters inevitably leads to the appearance of hidden defects. Traditional automated technological process control systems are often based on rigid algorithms that are unable to fully take into account the complex nonlinear effects of air humidity, material cooling rate and dynamic base stiffness. The implementation of closed-loop control loops based on feedback ensures the stability of the laying parameters regardless of external disturbances. However, the effectiveness of such systems is significantly limited without the integration of predictive models that can detect potentially defective areas even before the final cooling of the mixture.
Objective. The purpose of the study is to develop and analyze the effectiveness of an intelligent adaptive control system (IACS) for the asphalt concrete paving process, which integrates a predictive neural network model into a closed-loop control loop.
Methods. To implement the system, object-oriented programming methods (Python language) and machine learning libraries (XGBoost, TensorFlow) were used. The methodology is based on comparative computer modeling of 1000 technological cycles. The results were verified by comparing the predicted density values with reference physical and mathematical models of compaction.
Results. The implementation of the proposed system allowed to increase the coating density by 4 times compared to standard systems. The reduction of the reaction time to temperature disturbances from 18.5s to 5.2s confirms the ability of the system to act proactively and reduce the probability of hidden damage to the level of 1.8%. The value of the process stability coefficient.
Conclusions. The proposed intelligent adaptive control system allows solving the problem of delayed response of traditional automated complexes to stochastic changes in external factors during asphalt concrete laying. The proposed approach eliminates the negative impact of temperature instability and humidity fluctuations, which usually lead to the appearance of hidden defects and heterogeneity of the coating structure. Thanks to the integration of predictive neural network models into a closed control loop, it was possible to provide proactive process control, where parameter adjustment occurs based on the forecast of the material state, and not only on the fact of deviation from the norm.
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