DOI: 10.17587/prin.17.108-116

Adaptive Control System for 3D Printing of Concrete based on Machine Learning and Sensor Data

A. S. Dolgov, Postgraduate Student, alexdolgoffkld@yandex.ru, M. A. Dmitrieva, Professor, dmitrieva_m@inbox.ru, Immanuel Kant Baltic Federal University, Kaliningrad, 236016, Russian Federation

Corresponding author: Aleksandr S. Dolgov, Postgraduate Student, Immanuel Kant Baltic Federal University, 236016, Kaliningrad, Russian Federation, E-mail: mr.alexofficial@gmail.com

This study introduces a novel software framework for adaptive control of the 3D concrete printing process, addressing one of the most pressing challenges in large-scale additive manufacturing with cementitious materials: the inherent variability of rheological and environmental conditions. Traditional extrusion-based systems typically rely on predefined process parameters, which fail to account for dynamic changes in material viscosity, temperature, and ambient humidity. As a result, the accuracy, stability, and reproducibility of the printed structures are often compromised, limiting the wider adoption of the technology in construction practice. To overcome these limitations, the proposed system integrates real-time sensor feedback with predictive models based on machine learning to dynamically adjust deposition parameters, including material feed rate, nozzle velocity, and layer height. This closed-loop approach enables the printer to respond adaptively to fluctuations in material properties and environmental conditions, thereby improving the dimensional precision, interlayer adhesion, and overall mechanical performance of the printed elements. The article details the architecture of the software, the mathematical formulations of the adaptive algorithms, and the implementation of sensor-driven control mechanisms. Experimental validation indicates that the method substantially reduces material waste and energy consumption, while simultaneously enhancing print reliability. In addition, the system demonstrates potential for integration with digital twin environments, offering new opportunities for predictive maintenance and process optimization. This research contributes to the advancement of automated construction technologies by providing a scalable and intelligent control solution, with direct benefits for sustainable building practices and the industrialization of concrete 3D printing.

Keywords: 3D concrete printing, adaptive process control, additive manufacturing in construction, sensor-based monitoring, machine learning algorithms, rheology of cementitious materials, mixture viscosity, setting time prediction, material feed rate optimization, moisture evaporation effects

pp. 108—116

For citation:
Dolgov A. S., Dmitrieva M. A. Adaptive Control System for 3D Printing of Concrete based on Machine Learning and Sensor Data, Programmnaya Ingeneria, 2026, vol. 17, no. 2, pp. 108—116. DOI: 10.17587/prin.17.108-116. (in Russian).

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