Research on the strength prediction model of Na based bentonite filling body based on ultrasonic transverse wave testing
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摘要:
充填体作为充填采矿法的核心单元,其强度是保障安全开采的重要指标。Na基膨润土的亚甲基蓝吸附量和生坯抗压强度较高,是制备充填体的优质添加剂,然而鲜有其掺量与充填体强度之间关系的研究成果。本文基于超声波测试技术和单轴压缩试验,分析不同Na基膨润土掺量充填体的横波波速、主频幅值、幅值衰减系数、波形分形维数在各龄期的变化规律,结合敏感性分析遴选出对抗压强度变化最敏感的声学参数。建立了不同Na基膨润土掺量下充填体强度的预测模型,并结合显著性检验及对比分析,对强度预测模型进行了验证。研究成果可为开展充填体单轴抗压强度预测的相关理论研究和工程应用提供参考和帮助。
Abstract:As the core unit of backfill mining method, the strength of backfill is an important indicator to ensure safe mining. Na-based bentonite features a high methylene blue adsorption capacity and green compressive strength, making it a high-quality additive for preparing filling materials. However, there are few studies probing into the relationship between the dosage and the strength of the filling materials. This paper analyzes the changes in shear wave velocity, dominant frequency amplitude, amplitude attenuation coefficient, and waveform fractal dimension of filling materials with different Na-based bentonite dosages at different ages through ultrasonic testing technology and uniaxial compression tests. Combined with sensitivity analysis, we selected the most sensitive acoustic parameters to changes in compressive strength. Furthermore, this study establishes a strength prediction model for backfill with different Na-bentonite contents and combining significance testing and comparative analysis. The research findings can serve as a valuable reference for theoretical research and engineering applications related to predicting the uniaxial compressive strength of backfill materials.
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表 1 试样的主要化学成分
Table 1. Chemical composition of sample materials
% 化学成分 SiO2 Al2O3 MgO SO3 Fe2O3 TiO2 MnO CaO K2O 水泥 20.41 4.68 2.66 3.16 3.20 0.27 — 62.23 0.71 粉煤灰 51.38 33.07 1.02 0.24 4.58 1.14 — 4.13 0.86 尾砂 63.72 18.77 2.77 0.75 4.08 0.50 0.21 3.98 4.26 炉渣 33.27 10.70 7.78 2.20 1.70 1.18 1.13 39.59 — 表 2 尾砂粒径分布
Table 2. Distribution of tail sand particle size
粒径/μm -16 -40 -80 -160 -233 -300 -400 +400 尾砂产率/% 4.64 7.81 9.15 18.25 10.30 20.63 27.21 2.01 尾砂累计/% 4.64 12.45 21.6 39.85 50.15 70.78 97.99 100.00 表 3 炉渣粒径分布
Table 3. Particle size distribution of slag
粒径/μm -5 +5~10 +10~20 +20~30 +30~40 +40 炉渣产率/% 12.50 37.69 28.19 15.86 5.77 0.29 炉渣累计/% 12.20 49.89 78.08 93.94 99.71 100.00 表 4 σ-的回归关系拟合
Table 4. Regression fitting results of σ-α relationship
回归模型 龄期/d 回归方程 R2 线性 7 σ=3.88-0.007 0.74 14 σ=8.47-0.04 0.92 28 σ=13.73-0.06 0.95 多项式 7 σ=6.28-0.07α-(2.9E-4)α2 0.89 14 σ=8.66-0.04α-(2.73E-5)α2 0.80 28 σ=12.98-0.05α-(1.89E-5)α2 0.80 指数 7 σ=3.2-47.31e(-α/12.08) 0.87 14 σ=29.9e (α/611.9)-18.16 0.96 28 σ=42.3e(α/588.9)-11.16 0.33 幂函数 7 σ=7.17α(-0.18) 0.64 14 σ=69α(-0.58) 0.77 28 σ==67.7α(-0.46) 0.97 对数函数 7 σ=ln(42.07-0.18α) 0.53 14 σ=ln(848.2-7.52α) 0.95 28 σ=ln(38 772-353.7α) 0.94 表 5 不同模型各龄期相关性系数
Table 5. Correlation coefficients of different models at different ages
养护龄期/d 文献[10]模型相关性系数γ2 本文模型相关性系数γ2 7 0.98 0.74 14 0.92 0.92 28 0.74 0.95 -
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