Abstract
Background
The use of incinerated bottom ash (IBA) as a sustainable construction material offers potential environmental benefits but introduces complex interactions with cement chemistry. Magnesium phosphate cement (MPC), known for its rapid hardening and superior bonding, can be optimized through the controlled incorporation of IBA. However, limited studies have addressed how the chemical components of IBA affect the compressive strength of MPC, particularly using data-driven approaches.
Methods
A database of 396 experimental samples was compiled from previous studies considering mix proportions, oxide compositions, and curing conditions. Four ensemble machine learning algorithms—Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), Gradient Boosting Regressor (GBR), and Random Forest (RFR)—were employed to predict compressive strength. Model robustness was validated through 5-fold cross-validation. Feature interpretation was achieved using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to quantify individual and interactive effects of chemical and physical parameters.
Results
The XGB model achieved the highest predictive accuracy, with mean training and testing R2 values greater than 0.90 and 0.80, and the lowest mean absolute percentage error of 16.71%. SHAP analysis identified curing age as the most dominant factor, followed by FA/C, W/C, and MgO/PO4 ratios. IBA content and specific oxides such as Fe2O3 and Al2O3 contributed positively to strength within optimal ranges. PDP confirmed nonlinear dependencies, indicating a 26% reduction in strength as W/C increased from 0.1 to 0.6, while extended curing up to 28 days improved performance substantially.
Conclusion
The integration of SHAP and PDP provided a transparent interpretation of feature interactions in IBA-modified MPC. The developed XGB model demonstrated strong generalization and interpretability. The combined modeling approach offers a reliable predictive framework for optimizing IBA incorporation in sustainable binder systems and advancing eco-efficient material design.
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Details
1 Khulna University of Engineering and Technology, Department of Building Engineering and Construction Management, Khulna, Bangladesh (GRID:grid.443078.c) (ISNI:0000 0004 0371 4228)
2 Kafrelsheikh University, Civil Engineering Department, Faculty of Engineering, Kafrelsheikh, Egypt (GRID:grid.411978.2) (ISNI:0000 0004 0578 3577)
3 King Khalid University, Department of Civil Engineering, College of Engineering, Abha, Saudi Arabia (GRID:grid.412144.6) (ISNI:0000 0004 1790 7100)
4 International University of Business Agriculture and Technology, Department of Civil Engineering, College of Engineering and Technology, Dhaka, Bangladesh (GRID:grid.443015.7) (ISNI:0000 0001 2222 8047)
5 Kampala International University, Western Campus, Department of Civil Engineering, Ishaka–Bushenyi, Uganda (GRID:grid.440478.b) (ISNI:0000 0004 0648 1247)