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- W3157232983 abstract "To reduce the difficulty of obtaining the unconfined compressive strength (UCS) value of fiber-reinforced cemented paste backfill (CPB) and analyze the comprehensive impact of conventional and fiber variables on the compressive property, a new artificial intelligence model was proposed by combining a newly invented meta-heuristics algorithm (salp swarm algorithm, SSA) and extreme learning machine (ELM) technology. Aiming to test the reliability of that model, 720 UCS tests with different cement-to-tailing mass ratio, solid mass concentration, fiber content, fiber length, and curing time were carried out, and a strength evaluation database was collected. The obtained results show that the optimized SSA−ELM model can accurately predict the uniaxial compressive strength of the fiber-reinforced CPB, and the model performance of SSA−ELM model is better than ANN, SVR and ELM models. Variable sensitivity analysis indicates that fiber content and fiber length have a significant effect on the UCS of fiber-reinforced CPB." @default.
- W3157232983 created "2021-05-10" @default.
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- W3157232983 date "2021-04-01" @default.
- W3157232983 modified "2023-10-14" @default.
- W3157232983 title "Artificial intelligence model for studying unconfined compressive performance of fiber-reinforced cemented paste backfill" @default.
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- W3157232983 doi "https://doi.org/10.1016/s1003-6326(21)65563-2" @default.
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