quantum sensors, autonomous robotic platforms, offshore hydrocarbon deposits, geophysical modeling Adaptive machine learning algorithms for self-regulating earthmoving equipment control systems in difficult ground conditions, predictive analytics, neural

Abstract

This article presents a comprehensive analysis of the use of adaptive machine learning algorithms to create self-regulating control systems for earthmoving equipment operating in heterogeneous and complex ground conditions. The research is based on the integration of modern artificial intelligence methods with traditional models of soil mechanics to develop algorithms capable of adapting in real time to changing soil characteristics and optimizing the work processes of earthmoving machines. The results obtained during modeling and experimental verification show that the use of reinforcement learning methods in combination with deep neural networks can increase the efficiency of earthmoving operations by 23.7% compared with traditional control systems. The developed architecture of the neural network controller has demonstrated the ability to adapt to variations in soil density, degree of adhesion and humidity with an accuracy of parametric estimation up to 91.5%. The proposed methodology for assessing soil resistance based on the integration of physical models and machine learning algorithms reduces energy consumption by 18.2% and increases the accuracy of positioning the working bodies by 27.4%. The obtained results have significant potential for implementation in automated systems of the construction industry and create the basis for further development of intelligent methods of earthmoving machinery management in conditions of uncertainty of working environment parameters.