TY  - EJOU
AU  - Bui, Quynh-Anh Thi 
AU  - Trinh, Son Hoang 
AU  - Sayadi, Maryam 
AU  - Khanali, Reza 

TI  - Interpretable Deep Learning Framework for Predicting Compressive Strength of Steel Fiber Reinforced Geopolymer Concrete
T2  - Computer Modeling in Engineering \& Sciences

PY  - 
VL  - 
IS  - 
SN  - 1526-1506

AB  - Geopolymer concrete has attracted increasing attention as a lower-carbon alternative to ordinary Portland cement concrete because it can utilize aluminosilicate-rich industrial by-products while still achieving satisfactory mechanical performance. However, the 28-day compressive strength of steel fiber-reinforced geopolymer concrete (SFGPC) is governed by multiple interacting mixture variables, which makes reliable prediction difficult, especially for medium-sized experimental datasets. This study developed an interpretable deep-learning framework to predict the 28-day compressive strength (CS28) of SFGPC using an original experimental dataset of 189 mixtures produced under a consistent laboratory protocol in Vietnam. The dataset covered nine mixture variables, including activator chemistry (NaOH, Na<sub>2</sub>SiO<sub>3</sub>, and Na<sub>2</sub>O), binder constituents (Fly Ash (FA), Ground Granulated Blast-Furnace Slag (GGBS), and silica fume (SF), aggregate contents, and steel fiber dosage. Three tabular models, namely Tabular Deep Polynomial Transformer (TabDPT), Tabular Mixer (TabM), and Extreme Gradient Boosting (XGBoost), were trained and evaluated. Among them, TabDPT achieved the best performance on the independent test set, with R<sup>2</sup> = 0.978 and RMSE = 2.214 MPa, while also showing more stable behavior under repeated 5-fold cross-validation than XGBoost. SHapley Additive exPlanations indicated that binder composition and activator chemistry were the dominant variables in the trained model, with GGBS and Na<sub>2</sub>O showing the strongest influence, whereas steel fiber dosage had a comparatively smaller contribution to CS28 within the investigated domain. Totally, the proposed framework can support preliminary mixture screening and strength-oriented design of SFGPC within the investigated material and curing domain.
KW  - Experimental dataset; steel fiber; geopolymer concrete; TabDPT; TabM; SHapley Additive exPlanations (SHAP) interpretation; in-context learning

DO  - 10.32604/cmes.2026.081794