Abstract Machine learning (ML) has been widely utilized to estimate the compressive strength (fc) of fly ash (FA) concrete due to its high efficiency and accuracy. However, the inherent variability in concrete materials introduces significant uncertainty in fc value, which traditional deterministic ML models cannot quantify. To address this engineering problem, this study proposes to adopt natural gradient boosting (NGBoost), a renowned probabilistic ML model, to estimate fc for FA concrete.
