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  • Open Access

    ARTICLE

    Utilization of Low-Alkalinity Cementitious Materials in Cemented Paste Backfill of Gold Mine Tailings

    Jiamao Li1,2,*, Chuimin Zhang1, Lin Li1, Chuangang Fan1,*, Zhaofang He3, Yuandi Qian3

    Journal of Renewable Materials, Vol.10, No.12, pp. 3439-3458, 2022, DOI:10.32604/jrm.2022.021214 - 14 July 2022

    Abstract The purpose of this paper was to explore the possibility of using low-alkalinity cementitious materials as binders, in which ground blast furnace slag and fly ash acted as a partial replacement of ordinary Portland cement, and CaSO4, Na2SO4, and CaO were used as a sulfate activator and alkali-activated additives, to solidify gold mine tailings for preparation of a green, inexpensive cemented paste backfill (CPB). For this target, the effects of cement/ tailings ratio, superplasticizer dosage, solid content, tailings fineness on the mechanical properties of the CPB were investigated. Additionally, the hydration mechanism of the CPB was… More >

  • Open Access

    ARTICLE

    A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM

    Bo Zhang1,2, Keqing Li1,2, Siqi Zhang1,2, Yafei Hu1,2, Bin Han1,2,*

    Journal of Renewable Materials, Vol.10, No.12, pp. 3539-3558, 2022, DOI:10.32604/jrm.2022.021845 - 14 July 2022

    Abstract Cemented paste backfill (CPB) is a sustainable mining technology that is widely used in mines and helps to improve the mine environment. To investigate the relationship between aggregate grading and different affecting factors and the uniaxial compressive strength (UCS) of the cemented paste backfill (CPB), Talbol gradation theory and neural networks is used to evaluate aggregate gradation to determine the optimum aggregate ratio. The mixed aggregate ratio with the least amount of cement (waste stone content river sand content = 7:3) is obtained by using Talbol grading theory and pile compactness function and combined with… More > Graphic Abstract

    A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM

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