Slump-based soil conditioning of EPB shield in gravelly sand and its prediction study
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摘要: 在砾砂地层中掘进时，渣土改良效果是影响盾构掘进效率的关键因素。通过坍落度试验研究了泡沫、膨润土泥浆和高分子聚合物对改良土体流塑性的影响。以试验结果作为数据样本集，采用SVR，KNR，RFR和BPNN等常用机器学习方法构建了土体坍落度的预测模型，并将预测值与实际值进行了对比分析。研究结果表明：（1）泡沫对砾砂渣土流塑性的改良效果较好；（2）对于高含水率的砾砂地层，应使用高黏度的膨润土泥浆或PAM溶液进行改良，以起到保水增黏、防止喷涌的目的；（3）对比SVR，KNR和BPNN模型，RFR模型在预测时的性能表现最佳，能够更准确地预测改良渣土的坍落度，并且对模型进行了可解释性分析。Abstract: The impact of soil conditioning is a critical factor influencing shield tunneling efficiency in strata of gravelly sand. Through a slump test, the impacts of foam, bentonite slurry, and polymer on the enhanced soil’s flow plasticity were examined. A prediction model of soil slump was provided using machine learning techniques like SVR, KNR, RFR, and BPNN, utilizing the test results as the data sample set. The predicted and real values were then compared and examined. The study indicates that: (1) Foam has a greater impact on enhancing the gravelly sandy soil’s flow flexibility. (2) High-viscosity bentonite slurry or PAM solution should be applied over gravelly sandy stratum with high water content to retain water, improve viscosity, and prevent blowout. (3) The RFR model outperforms the SVR, KNR, and BPNN models regarding prediction accuracy. It can also forecast the slump of the improved waste soil with greater precision. The model’s interpretability was examined as well.
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表 2 四种模型的测试集评价指标及排名
测试集 R2 排名 MSE
/mm2排名 RMSE
/mm排名 MAE
/mm排名 汇总 SVR 0.75554 2 597.75 2 24.449 2 15.226 2 8 KNR 0.75402 3 601.45 3 24.524 3 18.041 3 12 RFR 0.90255 1 287.22 1 16.947 1 12.300 1 4 BPNN 0.68028 4 781.76 4 27.960 4 20.833 4 16 
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表 3 四种模型的训练集评价指标及排名
训练集 R2 排名 MSE
/mm2排名 RMSE
/mm排名 MAE
/mm排名 汇总 SVR 0.89994 2 294.92 2 17.173 2 14.192 2 8 KNR 0.78865 4 622.95 4 24.959 4 119.665 4 16 RFR 0.92914 1 50.991 1 7.1408 1 4.8726 1 4 BPNN 0.79995 3 589.64 3 24.282 3 18.257 3 12
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