Sustainability-focused evaluation of self-compacting concrete: Integrating explainable machine learning and mix design optimization

Aldawish, Abdulaziz and Kulasegaram, Sivakumar ORCID: https://orcid.org/0000-0002-9841-1339 2026. Sustainability-focused evaluation of self-compacting concrete: Integrating explainable machine learning and mix design optimization. Applied Sciences 16 (3) , 1460. 10.3390/app16031460

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (12MB) | Preview

Abstract

Self-compacting concrete (SCC) offers significant advantages in construction due to its superior workability; however, optimizing SCC mixture design remains challenging because of complex nonlinear material interactions and increasing sustainability requirements. This study proposes an integrated, sustainability-oriented computational framework that combines machine learning (ML), SHapley Additive exPlanations (SHAP), and multi-objective optimization to improve SCC mixture design. A large and heterogeneous publicly available global SCC dataset, originally compiled from 156 independent peer-reviewed studies and further enhanced through a structured three-stage data augmentation strategy, was used to develop robust predictive models for key fresh-state properties. An optimized XGBoost model demonstrated strong predictive accuracy and generalization capability, achieving coefficients of determination of �2 =0.835 for slump flow and �2 =0.828 for �50 time, with reliable performance on independent industrial SCC datasets. SHAP-based interpretability analysis identified the water-to-binder ratio and superplasticizer dosage as the dominant factors governing fresh-state behavior, providing physically meaningful insights into mixture performance. A cradle-to-gate life cycle assessment was integrated within a multi-objective genetic algorithm to simultaneously minimize embodied CO2 emissions and material costs while satisfying workability constraints. The resulting Pareto-optimal mixtures achieved up to 3.9% reduction in embodied CO2 emissions compared to conventional SCC designs without compromising performance. External validation using independent industrial data confirms the practical reliability and transferability of the proposed framework. Overall, this study presents an interpretable and scalable AI-driven approach for the sustainable optimization of SCC mixture design.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	MDPI
ISSN:	2076-3417
Date of First Compliant Deposit:	10 February 2026
Date of Acceptance:	21 January 2026
Last Modified:	10 Feb 2026 14:16
URI:	https://orca.cardiff.ac.uk/id/eprint/184573

Actions (repository staff only)

Edit Item