{
  "abstract": "This study evaluates the interactive effects of nano-belite cement replacement ( $$\\:0\\text{\\--}20\\%\\text{\\:wt.}$$ ) and internal salt additions ( $$\\:5\\%\\text{\\:wt.}$$ of either $$\\:\\text{NaCl}$$ or $$\\:{\\text{Na}}_{2}{\\text{SO}}_{4}$$ ) on the compressive strength and bulk electrical resistivity of concrete composites up to 180 days of curing. A dataset of 225 experimental records was used to train a multilayer perceptron (MLP) neural network, which was subsequently coupled with the non-dominated sorting genetic algorithm II (NSGA-II) to optimize the mixture design. Experimental results indicate that under salt-free conditions, a $$\\:10\\%$$ nano-belite replacement optimizes microstructural development, yielding a compressive strength of $$\\:50.26\\text{\\:MPa}$$ and an electrical resistivity of $$\\:174.40\\text{}{\\Omega\\:}\\cdot\\:\\text{m}$$ at 180 days. The internal addition of $$\\:{\\text{Na}}_{2}{\\text{SO}}_{4}$$ acts synergistically with nano-belite by promoting pore refinement through secondary hydration products, elevating the 180-day compressive strength to $$\\:52.49\\text{\\:MPa}$$ . In contrast, internal $$\\:\\text{NaCl}$$ incorporation exhibits an antagonistic relationship at nano-belite dosages exceeding $$\\:5\\%$$ , causing a drop in electrical resistivity due to enhanced ionic mobility in the pore network. The developed MLP model predicted the experimental outcomes with high accuracy ( $$\\:{R}^{2}>0.98$$ for both properties). Multi-objective optimization via NSGA-II identified a balanced optimum replacement level at $$\\:9.82\\%$$ nano-belite. Independent validation testing of ten physical specimens fabricated at this optimal dosage demonstrated close agreement with model predictions, yielding relative errors of $$\\:2.94\\%$$ for bulk electrical resistivity and $$\\:13.13\\%$$ for compressive strength. Additionally, explicit algebraic equations derived from the network weights provide a direct mapping tool for mix design without the need for active machine learning execution.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Civil Engineering, University of Sistan and Baluchestan"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Habib Alrahman Jamshidzehi"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohammad Reza Sohrabi"
    },
    {
      "affs": [
        0
      ],
      "name": "Ahmad Reza Abbasian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/81n5c44pfx7bg.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-67355-7",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-67355-7",
  "id": "81n5c44pfx7bg",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-67355-7",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2205|Concrete and Cement Materials Research"
    ]
  ],
  "openalex": "W7206175802",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/81n5c44pfx7bg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:47Z",
      "native_id": "add7322f05fb481582624964a01424a5",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-67355-7"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Data-driven modeling and optimization of nano-belite cementitious composites incorporating internal NaCl and Na2SO4 additives",
  "title_text": "Data-driven modeling and optimization of nano-belite cementitious composites incorporating internal NaCl and Na2SO4 additives",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:47Z",
  "url": "https://research.draconitic.com/paper/81n5c44pfx7bg",
  "venue": "Scientific Reports",
  "versions": []
}