{
  "abstract": "Ordinary Portland cement (OPC) production accounts for approximately 7–8% of global CO2 emissions, motivating the development of sustainable binders such as metakaolin-based geopolymers and hybrid alkali-activated cements (AACs). However, accurate prediction of compressive strength in these systems remains challenging because of nonlinear interactions among precursor composition, activator chemistry, water-related parameters, and curing conditions. This study develops a reliability-oriented machine-learning framework using literature-derived datasets comprising 223 metakaolin-based geopolymer mixtures and 262 hybrid AAC mixtures. Five tree-based ensemble algorithms were evaluated using five-fold GroupKFold validation, with literature source used as the grouping variable to reduce the risk of study-level information leakage. The framework further incorporated fold-wise hyperparameter optimization, inverse-frequency sample weighting during model fitting, source-wise sensitivity analysis, and leave-one-source-out evaluation. Under grouped internal validation, CatBoost achieved the best performance for the metakaolin-based geopolymer dataset (R2 = 0.8211 ± 0.1275, RMSE = 5.773 ± 2.333 MPa, MAE = 4.131 ± 1.543 MPa), whereas Gradient Boosting performed best for the hybrid AAC dataset (R2 = 0.8876 ± 0.0708, RMSE = 3.333 ± 1.181 MPa, MAE = 2.366 ± 0.737 MPa). TreeSHAP analysis revealed distinct model-derived feature associations: hybrid AAC predictions were most strongly associated with GGBFS content, followed by activator- and curing-related variables, whereas metakaolin-based geopolymer predictions were most strongly associated with the H2O/Na2O ratio, aggregate proportion, activator chemistry, and curing conditions. These SHAP-derived patterns represent statistical associations learned by the models rather than direct evidence of physical causation or reaction mechanisms. Overall, the proposed framework provides a transparent approach for preliminary compressive-strength screening and experimental prioritization of sustainable alkali-activated binders.",
  "abstract_shown": true,
  "affiliations": [
    "Qatar University , , P.O. Box , ,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jafla Thekke Keloth"
    },
    {
      "affs": [
        0
      ],
      "name": "Nasser Al Nuaimi"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohammed Z. E. B. Elshafie"
    },
    {
      "affs": [
        0
      ],
      "name": "Ibnelwaleed A. Hussein",
      "orcid": "0000-0002-6672-8649"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fvbm6tar1g2eh.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c07604",
  "fulltext_url": null,
  "id": "fvbm6tar1g2eh",
  "issns": [
    "2470-1343"
  ],
  "landing_url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c07604/5463999/Machine-Learning-Driven-Comparative-Prediction-of",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c07604",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/fvbm6tar1g2eh.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:20:52Z",
      "native_id": "10.1021/acsomega.6c07604",
      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c07604/5463999/Machine-Learning-Driven-Comparative-Prediction-of"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2470-1343",
      "subfield": "general"
    }
  ],
  "title": "Machine Learning-Driven Comparative Prediction of Compressive Strength in True Geopolymer and Hybrid Alkali-Activated Cement Systems",
  "title_text": "Machine Learning-Driven Comparative Prediction of Compressive Strength in True Geopolymer and Hybrid Alkali-Activated Cement Systems",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T00:21:08Z",
  "url": "https://research.draconitic.com/paper/fvbm6tar1g2eh",
  "venue": "ACS Omega",
  "versions": []
}