Information and Measurement Systems for Vibration Control of Road Construction Machines

This paper focuses on the application of intelligent information and measurement technologies for controlling and predicting vibration loads on operators of road construction machinery. Vibrations in heavy equipment cabins are recognised as a significant occupational risk factor, contributing to musculoskeletal disorders, decreased performance, and long-term health issues such as vibration-induced diseases. Road construction machinery, particularly excavators, tractors, and bulldozers, generate complex vibration fields transmitted to operators through the seat, floor, and control handles. Therefore, ensuring continuous monitoring and predictive analysis of vibration loads is an important component of occupational safety and machine ergonomics. The study was conducted on a crawler excavator widely used in Ukrainian road construction projects. Experimental measurements were performed in real operating conditions using a portable vibration meter, with sensors mounted at three critical points of the operator’s workplace: the seat, the floor, and the control handles. Vibrations were assessed under different working modes, including idle running, bucket operation, and machine displacement. The analysis revealed that the vibration levels frequently exceeded permissible thresholds defined by ISO 2631-1 and Ukrainian regulations, particularly during active bucket operations and while holding the control handles. These findings highlight the urgent need for systematic vibration monitoring and preventive measures. The proposed approach integrates modern sensor technologies, microcontroller-based data acquisition, wireless transmission, and software modules for digital signal processing. A conceptual system architecture is presented, comprising multi-axis MEMS accelerometers, programmable controllers (Arduino/ESP32), and analytical modules implemented in Python and MATLAB. The novelty of the study lies in the development of an intelligent analytical module based on artificial neural networks, enabling not only real-time monitoring but also prediction of critical operating modes that pose the highest risks to operator health. The obtained results demonstrate the effectiveness of combining experimental data with machine learning methods for identifying hazardous vibration conditions. The study provides practical recommendations, including the implementation of vibration-damping seats, anti-vibration handle covers, and improved cabin design. The proposed system can be scaled for industrial applications, integrated into occupational health monitoring frameworks, and linked with predictive maintenance systems for construction machinery. Overall, the research contributes to the advancement of intelligent measurement technologies in occupational safety, emphasising their role in reducing vibration-related risks, extending machine service life, and enhancing overall productivity in road construction operations.