Explainable automl and multi-objective optimization for sustainable high-performance geopolymer concrete.

ABSTRACT: The urgent demand for sustainable concrete has intensified as greenhouse gas emissions increasingly threaten global environmental stability. High-performance geopolymer concrete (HPGC) presents a promising sustainable alternative to traditional cement-based materials. The mix design of HPGC considering compressive strength, cost, and carbon emissions is crucial while hard to implement using traditional non-destructive methods. To resolve the issue, this study proposes an integrated framework combining automated machine learning (AutoML) modeling and multi-objective optimization (MOO) to balance compressive strength, cost, and carbon emissions in HPGC mix-design. Our research mainly includes three innovations: (1) We integrate ML-based predictive modeling with MOO framework for HPGC; (2) we

SUBMITTER: Wu S 

PROVIDER: S-EPMC12475034 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature