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Volume 30 Issue 9
Sep.  2023
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文章导航 >  矿物冶金与材料学报（英文）  > 2023  >  30(9): 1692-1704
Yafei Hu, Shenghua Yin, Keqing Li, Bo Zhang, and Bin Han, Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1692-1704. https://doi.org/10.1007/s12613-022-2563-8
Cite this article as:
Yafei Hu, Shenghua Yin, Keqing Li, Bo Zhang, and Bin Han, Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1692-1704. https://doi.org/10.1007/s12613-022-2563-8
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研究论文

固体废弃物资源综合利用：开发矿山用湿喷混凝土

  • 1)

    北京科技大学土木与资源工程学院，北京 100083，中国

  • 2)

    金属矿山高效开采与安全教育部重点实验室，北京 100083，中国



  • 通讯作者:

    韩斌    E-mail: bin.han@ustb.edu.cn

文章亮点

  • (1) 基于响应面法建立的湿喷混凝土强度响应模型具有较高的可靠性。
  • (2) 开发了尾砂湿喷混凝土并研究了多因素耦合的强度发展规律。
  • (3) 构建了高斯过程回归模型实现了尾砂湿喷混凝土强度的高精度预测与配合比优化。
  • 以固体废弃物开发湿喷混凝土具有重大的经济和环保效益。本文提出采用尾砂作为骨料，采用粉煤灰和矿渣粉作为辅助胶凝材料，开发矿山用尾砂湿喷混凝土（TWSC）。通过响应面法优化配合比实验方案，并基于实验结果构建多元非线性响应模型，以探究不同因素对TWSC强度的影响规律；通过构建高斯过程回归算法（GPR）对TWSC强度进行预测，并结合遗传算法（GA）对TWSC配合比进行优化。结果表明TWSC强度随矿渣粉掺量和尾砂细度模数的提高逐渐变大，随粉煤灰掺量的提高先变大后减小。当矿渣粉掺量小于80 kg·m−3时，其对TWSC中后期强度影响显著；当矿渣粉掺量高于80 kg·m−3时，其对TWSC的早期强度影响显著。与多元非线性回归、支持向量回归和极限学习机等方法相比，GPR对TWSC强度的预测精度最高（R = 0.998，RMSE = 0.143，VAF = 99.564），将GPR与GA结合构建的GRP–GA模型实现了多因素条件下的TWSC配合比优化。
    • Research Article

    Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines

    + Author Affiliations
      Abstract
    • The development of solid waste resources as constituent materials for wet shotcrete has significant economic and environmental advantages. In this study, the concept of using tailings as aggregate and fly ash and slag powder as auxiliary cementitious material is proposed and experiments are carried out by response surface methodology (RSM). Multivariate nonlinear response models are constructed to investigate the effect of factors on the uniaxial compressive strength (UCS) of tailings wet shotcrete (TWSC). The UCS of TWSC is predicted and optimized by constructing Gaussian process regression (GPR) and genetic algorithm (GA). The UCS of TWSC is gradually enhanced with the increase of slag powder dosage and fineness modulus, and it is enhanced first and then decreased with the increase of fly ash dosage. The microstructure of TWSC has the highest gray value and the highest UCS when the fly ash dosage is about 120 kg·m−3. The GPR–GA model constructed in this study achieves high accuracy prediction and optimization of the UCS of TWSC under multi-factor conditions.
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