The use of AI in the probability of achieving concrete quality in batching plants
DOI:
https://doi.org/10.66987/EPKO.2026.31Keywords:
SonReb recalibration, concrete strength prediction, machine learning, maturity index, Conformal Prediction, NDTAbstract
Concrete quality control in Romania remains a deterministic, reactive process: compressive
strength is verified exclusively through destructive 28-day cube tests, precluding real-time intervention
during construction. The current SonReb-based NDT standard (NP 137-2014) was calibrated
predominantly on Portland cement (CEM I) concretes; applied to modern CEM II/III blended cements,
it introduces strength estimation errors of ±25–35%, rising to ±15–30 MPa at low curing temperatures.
This paper presents a doctoral research plan that integrates four data-source layers – SCADA batching
data, ERA5 climatic variables corrected by local IoT sensors, SonReb NDT measurements, and
destructive laboratory compression tests (324 cubes, DoE 3×3×6×6) – into a four-tier machine learning
architecture (XGBoost → residual ANN → MC Dropout → Conformal Prediction), delivering a hybrid
predictive system with a mathematically guaranteed ≥90% confidence interval
Objectives: RMSE < 3.8 MPa, R² > 0.90, CI-coverage ≥ 90%.
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Copyright (c) 2026 Ferenc-Zoltán SIMÓ, Zoltán KISS, Attila PUSKÁS
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