Abstract
This study develops and validates a method for real-time monitoring and structural integrity assessment of reinforced concrete facilities in Karaganda, Kazakhstan, integrating finite element modeling (FEM), machine learning (ML), and digital signal processing (DSP). Three pilot objects were analyzed: a three-span bridge, an 18-storey residential building, and a reinforced concrete highway section. FEM models built in ANSYS 2024 R1 were linked with calibrated sensor networks (strain gauges, accelerometers, thermocouples, tiltmeters, weather stations). Data processing was performed in MATLAB and SciPy, with ridge regression models (R² ≈ 0.85) used for defect prediction. Results showed close correspondence between calculations and measurements: deviations of 2% for the bridge (r = 0.98) and 4% for the building (r = 0.95) met the ≤5% accuracy target. The road section produced a 25% error (r = 0.90), mainly due to frost heave and heterogeneous traffic. Cost–benefit analysis indicated net efficiency within five years, with cumulative savings of 110–120 million KZT versus 67 million KZT in costs. The findings confirm the effectiveness of integrated digital monitoring for preventive maintenance, though further validation in different climates and materials is required.