Machine learning-driven multi-objective optimization and dynamic performance prediction of geopolymer concrete

Feng Dai et al.

Journal of Cleaner Production2026https://doi.org/10.1016/j.jclepro.2026.147501article
AJG 1ABDC C
Weight
0.44

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https://doi.org/https://doi.org/10.1016/j.jclepro.2026.147501

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@article{feng2026,
  title        = {{Machine learning-driven multi-objective optimization and dynamic performance prediction of geopolymer concrete}},
  author       = {Feng Dai et al.},
  journal      = {Journal of Cleaner Production},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jclepro.2026.147501},
}

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Machine learning-driven multi-objective optimization and dynamic performance prediction of geopolymer concrete

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.