Strength characteristics and strength prediction of fluid geopolymer solidified soil
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摘要:
地聚物胶凝材料能够替代水泥基胶凝材料作为固化剂应用于狭窄肥槽回填等工程问题中，有效降低水泥生产过程中的污染及能耗，但目前对于流态地聚物固化土胶凝材料的研究较少。采用3种新型绿色胶凝材料联合碱激发剂固化工程渣土形成流态地聚物固化土，通过对比其无侧限抗压强度，探究每种胶凝材料对于固化土强度特性的影响，同时建立强度预测模型，分析不同因素对于强度的影响程度。研究结果表明：固化土的强度随着碱激发剂模数的增加先提高后降低；固化土强度随着高炉矿渣（GGBS）、粉煤灰、稻壳灰掺量的增加均呈上升趋势，随着稻壳灰粒径的增长呈下降趋势；碱激发剂模数增至1.2、GGBS掺量增至10%、粉煤灰掺量增至8%和稻壳灰掺量增至11%时，固化土强度提升最为显著；强度预测模型预测结果的平均相对误差仅为5.57%，预测结果较为精准；预测模型中各层权值的计算结果表明养护龄期对于固化土强度影响最大，稻壳灰粒径影响程度最小。研究结果可以为固化土在实际工程的应用提供理论支持。
Abstract:Geopolymer cementitious materials can replace cement-based cementitious materials as curing agents in engineering problems, such as backfilling of narrow fertilizer troughs, and effectively reduce pollution and energy consumption in the cement production process. There are few studies on cementitious materials. Three new green cementitious materials combined with alkali activators are used to solidify engineering slag and form fluidized geopolymer-solidified soil. The strength prediction model is established to analyze the influence of different factors on the strength. The results show that the strength of the solidified soil increases first and then decreases with the increasing modulus of the alkali activator, increases with the content of GGBS, fly ash and rice husk ash, and decreases with the increasing particle size. When the modulus of alkali activator increases to 1.2, the content of GGBS increases to 10%, the content of fly ash increases to 8%, and the content of rice husk ash increases to 11%, the strength of the solidified soil increases significantly. The average relative error of the prediction results of the strength prediction model is only 5.57%, which is relatively accurate for the solidified soil. The calculation results of the weights of each layer in the prediction model show that the curing age has the greatest impact on the strength of the solidified soil, and the particle size of rice husk ash has the minimal impact. The research results can provide theoretical support for the application of solidified soil in practical engineering.
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表 1 粉煤灰、GGBS和稻壳灰的化学组成
Table 1. Chemical composition of fly ash, GGBS and rice husk ash
材料 w（SiO2）/% w（Al2O3）/% w（Fe2O3）/% w（MgO）/% w（CaO）/% w（Na2O）/% w（SO3）/% w（K2O）/% 粉煤灰 63.34 27.00 2.00 1.00 3.00 1.11 1.10 1.05 GGBS 35.41 20.24 0.18 8.16 31.64 1.36 1.79 0.29 稻壳灰 84.00 1.35 1.45 — 3.17 — 0.93 — 注：“—”表示不含此成分或含量极低。 
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表 2 流态地聚物固化土设计方案
Table 2. Design scheme of fluid geopolymer Solidified Soil
试验
编号GGBS
掺量/%粉煤灰
掺量/%碱激发剂
模数/（mol·L–1）稻壳灰
掺量/%稻壳灰
粒径/mmGF1 8 8 1.2 0 — GF2 10 8 1.2 0 — GF3 12 8 1.2 0 — GF4 14 8 1.2 0 — GF5 10 6 1.2 0 — GF6 10 10 1.2 0 — GF7 10 12 1.2 0 — GF8 10 8 0.6 0 — GF9 10 8 0.9 0 — GF10 10 8 1.5 0 — GFD1 10 8 1.2 5 1.2 GFD2 10 8 1.2 8 1.2 GFD3 10 8 1.2 11 1.2 GFD4 10 8 1.2 14 1.2 GFD5 10 8 1.2 11 0.6 GFD6 10 8 1.2 11 0.3 GFD7 10 8 1.2 11 0.15 GFD8 10 8 1.2 11 0.075
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表 3 不同隐含层层数于节点数的预测模型对比
Table 3. Comparison of prediction models with different hidden layers and nodes
隐含层层数 隐含层节点数 相关系数 均方误差 1 4 0.810 73 0.004 66 1 6 0.838 99 0.004 09 1 8 0.845 78 0.003 73 1 10 0.744 04 0.004 17 1 12 0.765 88 0.004 06 2 8、4 0.965 43 0.000 80 2 8、6 0.997 70 0.000 37 2 8、8 0.999 43 0.000 08 2 8、10 0.997 84 0.000 32
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表 4 测试样本误差分析表
Table 4. Error analysis of test samples
编号 GGBS掺量/% 粉煤灰掺量/% 碱激发模数 稻壳灰掺量/% 稻壳灰粒径/mm 养护龄期/d 预测值/MPa 实际值/MPa 绝对误差/MPa 相对误差/% 1 12 8 1.2 0 0 3 0.380 5 0.36 0.020 4 5.68 2 10 8 1.2 0 0 3 0.296 8 0.27 0.026 7 9.91 3 10 8 0.6 0 0 28 1.268 8 1.26 0.008 8 0.69 4 10 8 1.2 0 0 28 1.474 5 1.57 0.095 5 6.08 5 10 8 1.2 11 0.6 7 1.378 7 1.44 0.061 3 4.25 6 10 8 1.2 14 1 14 2.262 9 1.93 0.332 9 17.25 7 10 8 1.2 11 0.075 7 1.849 0 1.86 0.010 9 0.58 8 10 10 1.2 0 0 14 1.018 5 1.02 0.001 4 0.14
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表 5 预测模型训练与预测样本相对误差分布
Table 5. Relative error distribution of BP neural network training and prediction samples
相对误差分布范围 训练样本 预测样本 样本总数 占比/% >20% 5 0 5 5.68 (10%, 20%] 6 1 7 7.95 (1%, 10%] 25 4 29 32.96 ≤1% 44 3 47 53.41 总计 80 8 88 100
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表 6 预测模型权重贡献与权重贡献率
Table 6. weight contribution rate of prediction model
影响因素 权重贡献 权重贡献率/%（排名） GGBS掺量 0.501 169 6.92（5） 粉煤灰掺量 2.183 732 30.15（2） 碱激发剂模数 0.586 749 8.10（4） 稻壳灰掺量 1.197 763 16.54（3） 稻壳灰粒径 0.021 158 0.29（6） 养护龄期 2.752 821 38.00（1） 总计 7.243 328 100
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