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基于超聲波波速及BP神經網絡的膠結充填體強度預測

Prediction of cemented backfill strength by ultrasonic pulse velocity and BP neural network

  • 摘要: 尾砂膠結充填體作為一種水泥基多相復合材料,其單軸抗壓強度與超聲波波速受水泥含量、固體質量分數、試件形態等因素影響.通過制備三種形態(7.07 cm×7.07 cm×7.07 cm立方體,Φ5 cm×10 cm圓柱體和Φ7 cm×14 cm圓柱體)的試件并進行單軸抗壓強度試驗和聲波波速測試,對充填體強度和波速受水泥含量、固體質量分數和試件形態影響的規律進行了灰色-關聯度分析.結果表明:水泥含量是影響強度的關鍵核心因素,關聯度為0.837;固體質量分數是影響波速的關鍵核心因素,關聯度為0.712.建立了充填體強度-波速指數函數預測模型和BP神經網絡預測模型,通過對兩種預測模型進行統計分析的F檢驗和t檢驗驗證了兩種方法在充填體強度預測的可行性,為膠結充填體的強度預測提供了新方法.

     

    Abstract: Tailing-cemented backfill is a cement-based heterogeneous composite whose uniaxial compressive strength (UCS) and ultrasonic pulse velocity (UPV) are dependent on cement dosage, solid content, sample type, etc. In this paper, uniaxial compressive test and ultrasonic pulse velocity test of three types of backfill samples (7.07 cm×7.07 cm×7.07 cm cube, Φ5 cm×10 cm cylinder and Φ7 cm×14 cm cylinder) were performed, and the effects of cement dosage, solid content and sample type on the backfill strength and ultrasonic pulse velocity were investigated by grey correlative degree analysis. The results show that cement dosage is the key to the backfill strength with a correlative degree of 0.837, while the ultrasonic pulse velocity is mostly influenced by solid content with a correlation degree of 0.712. An exponential prediction relation between UCS and UPV and a BP neural network prediction model were built, and they were validated by F-test and t-test of statistical analysis, respectively. The methods proposed can be new approaches for predicting the backfill strength.

     

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