@Article{cmc.2026.086222, AUTHOR = {Jia Chen, Zhicheng Liao, Mengdi Hou, Jianbo Huang}, TITLE = {Interpretable Machine Learning for Compressive and Flexural Strength Prediction of Fly Ash Blended 3D Printed Concrete with Uncertainty Quantification}, JOURNAL = {Computers, Materials \& Continua}, VOLUME = {}, YEAR = {}, NUMBER = {}, PAGES = {{pages}}, URL = {http://www.techscience.com/cmc/online/detail/27633}, ISSN = {1546-2226}, ABSTRACT = {Fly ash (FA) blended 3D printed concrete (3DPC) offers improved sustainability but requires strength prediction models validated at the mix-composition level rather than within familiar formulations. This study applies leave-one-mix-out (LOMO) cross-validation to benchmark eight machine learning algorithms on 126 experimental records spanning seven FA-blended 3DPC compositions (FA 0–15 wt.%, W/B 0.30–0.35, age 1–28 days). ExtraTrees and ElasticNet achieve the highest composition-level generalisation for compressive strength (CS, R2=0.786±0.253) and flexural strength (FS, R2=0.915±0.061, RMSE =0.301 MPa), respectively. SHAP analysis identifies FA replacement percentage as the dominant CS predictor (mean |SHAP|=3.92 MPa, negative effect) and curing age as the dominant FS predictor (1.06 MPa), a reversal consistent with distinct failure mechanisms under compression and flexure. FA substitution at 5%–7.5% with W/B =0.30 is identified as the optimal design range, with content limited to 5% when structural loading within three days is required. LOMO residual prediction intervals are well calibrated from 50% to 99% nominal coverage, with empirical deviations not exceeding 1.4 percentage points; at the 90% level, interval half-widths are ±4.60 MPa for CS and ±0.494 MPa for FS. Design-space mapping over the FA% × age prediction grid at W/B =0.30 identifies minimum curing ages of 3 days at FA ≤7.5 wt.% and 13.5 days at FA =10 wt.% for simultaneous CS and FS target compliance; Sobol variance decomposition confirms FA% (S1=0.553) and curing age (S1=0.526) as the dominant sensitivity drivers for CS and FS, respectively, in close agreement with the SHAP attribution. A graphical user interface integrating the two best models returns simultaneous CS and FS predictions with 90% calibrated prediction intervals from three mix design inputs, supporting uncertainty-aware strength estimation without programming expertise. Both models are local interpolators conditioned on the experimental design space (FA 0–15 wt.%, W/B 0.30–0.35, age 1–28 days); predictions outside these boundaries have not been validated and should be interpreted with caution. The dataset and source code are publicly available at https://github.com/lucassivan/ML-FA-3DPC.}, DOI = {10.32604/cmc.2026.086222} }