An Uncertainty-Aware Explainable AI Framework for Sustainable Concrete Mixture Design and Decision Support
Year: 2026
Type: article
Abstract: Sustainable concrete mixture design requires reliable strength prediction, robust generalization, quantified uncertainty, and transparent environmental and economic assessment. This study presents an integrated, uncertainty-aware explainable AI framework combining family-aware validation, SHAP interpretation, applicability diagnostics, and multi-objective optimization. Following systematic auditing, 2623 records were retained from an initial dataset of 2700. Historical nested group-aware XGBoost validation achieved R2 = 0.857 ± 0.039 and RMSE = 5.386 ± 0.635 MPa. External evaluation employed 102 overlap-screened records from an independently sourced dataset of 427 observations, yielding R2 = −1.015 and RMSE = 11.115 MPa for the frozen model. External prediction-interval coverage reached 36.27% and 47.06% at nominal 90% and 95% levels, highlighting transferability and uncertainty-calibration challenges. A reproducible NSGA-II procedure incorporating absolute-volume constraints generated 200 candidate mixtures, accompanied by 35 sensitivity runs examining optimization and material assumptions. Carbon estimates employed documented emission-factor proxies, while cost assessment incorporated explicit procurement assumptions. The laboratory assessment of six concrete mixtures, supported by stamped reports documenting mixture compositions and specimen-level compressive strengths, yielded RMSE = 5.765 MPa. By integrating predictive modeling, interpretability, uncertainty, and engineering constraints, the framework provides a transparent basis for preliminary strength–carbon–cost screening and risk-informed sustainable concrete mixture design.
Source: Buildings
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225199858