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PREDICTION MODEL OF LOESS COLLAPSIBILITY IN GONGLIU COUNTY OF ILI RIVER VALLEY
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摘要: 由于湿陷性黄土地区地质灾害防治工程地基处理不当，使得黄土地区的非均匀湿陷性对地质灾害的防治工程造成一定威胁。因此，选取合适的参数建立黄土湿陷性预测模型能为黄土地区地质灾害防治工程的基础设计提供理论依据。本文以伊犁河谷地区巩留县黄土为研究对象，在前期收集该地区69组土工试验参数的基础上，借助数理统计的方法对该地区黄土湿陷系数和土性指标参数的相关性进行了分析，并采用多元线性回归理论和神经网络理论建立了该地区黄土湿陷性评价的预测模型。研究结果表明：研究区土体微观结构多表现为絮凝状结构，以支架接触方式为主，矿物颗粒多呈现薄片状，孔隙结构多呈现孔状或不规则状；研究区黄土湿陷系数与含水率、密度、干密度、饱和度、孔隙比、孔隙率相关系数在0.645~0.857之间，具有强或极强的相关性；通过对研究区建立的黄土湿陷性多元线性回归模型和RBF神经网络模型的综合对比，RBF神经网络模型更具有适用性、可信性和准确性，其准确性达到94.20%。因此，建立的RBF神经网络模型精度能够满足实际工程的需要，为解决该地区黄土湿陷性评价问题提供了新的思路。Abstract: Due to the improper foundation treatment of geological disaster prevention and control project in collapsible loess area,the non-uniform collapsibility of loess area poses a certain threat to the prevention and control project of geological disaster. Therefore,selecting appropriate parameters to establish a loess collapsibility prediction model can provide a theoretical basis for the basic design of geological disaster prevention and control projects in loess areas. In this paper,the loess of Gongliu County in the Ili River Valley is taken as the research object. On the basis of collecting a large number of geotechnical test parameters in the area in the early stage,the correlation between the loess collapsibility coefficient and the soil index parameters in the area is analyzed by means of mathematical statistics. The prediction model of loess collapsibility evaluation in the area is established using multiple linear regression theory and neural network theory. The results show that the microstructure of the soil in the study area is the mostly flocculated structure,mainly in the way of support contact. The mineral particles are mostly flaky,and the pore structure is mostly porous or irregular. The material composition is mainly sandstone,albite,calcite and dolomite. The correlation coefficient between loess collapsibility coefficient and these of water content,density,dry density,saturation,void ratio and porosity in the study area is between 0.645 and 0.857,which has strong or extremely strong correlation. Through the comprehensive comparison of the loess collapsibility multiple linear regression model and the RBF neural network model established in the study area,the RBF neural network model is more applicable,credible and accurate,and its accuracy reaches 94.20%. Therefore,the accuracy of the established RBF neural network model can meet the needs of practical engineering,which provides a new idea for solving the problem of collapsibility evaluation of loess in this area.
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Key words:
- Loess collapsibility /
- Soil indicators /
- Correlation /
- Prediction model /
- Ili River Valley
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图 3 巩留县黄土SEM微观结构图
a. 1000倍；b. 5000倍；c. 20 000倍；d. 50 000倍
Figure 3. SEM microstructure of loess in Gongliu County
图 4 巩留县黄土XRD试验分析图
a. XRD实验分析图；b. 矿物组成饼状图
Figure 4. XRD test analysis diagram of loess in Gongliu County
图 5 巩留县黄土湿陷系数与土性指标拟合图
a. 密度与湿陷系数；b. 孔隙比与湿陷系数；c. 孔隙率与湿陷系数；d. 干密度与湿陷系数；e. 饱和度与湿陷系数；f. 含水率与湿陷系数；g. 液性指数与湿陷系数
Figure 5. Fitting diagram of loess collapsibility coefficient and soil index in Gongliu County
图 6 巩留县黄土湿陷系数实际值与回归模型预测值对比
Figure 6. Comparison between the actual value of loess collapsibility coefficient in Gongliu County and the predicted value of regression model
图 7 巩留县黄土湿陷系数实际值与RBF神经网络模型预测值对比
Figure 7. Comparison between the actual value of loess collapsibility coefficient in Gongliu County and the predicted value of RBF neural network model
图 8 巩留县黄土湿陷系数实际值与模型预测值对比
Figure 8. Comparison between the actual value of loess collapsibility coefficient in Gongliu County and the predicted value of the model
表 1 研究区湿陷性黄土土性分析表
Table 1. Analysis table of collapsible loess soil in the study area
指标 平均值 标准差 变异系数 取样深度h/m 3.03 3.26 1.08 含水量ω/% 13.32 6.09 0.46 密度ρ/g·cm-3 1.55 0.24 0.16 干密度ρd/g·cm-3 1.36 0.16 0.12 孔隙比e 1.01 0.23 0.23 饱和度Sr/% 40.25 25.93 0.64 孔隙率n/% 49.45 6.06 0.12 液限ωL/% 25.47 2.07 0.08 塑限ωp/% 16.92 1.62 0.10 塑性指数Ip 8.54 1.05 0.12 液性指数IL -0.40 0.77 -1.91 压缩系数a/MPa-1 0.36 0.29 0.81 压缩模量Es/MPa 8.32 4.78 0.57 
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表 2 巩留县黄土湿陷系数与各土性参数之间相关性分析
Table 2. Correlation analysis between loess collapsibility and soil parameters in Gongliu County
相关性指标 回归方程 显著性值 相关系数 相关性 δs-ρ δs=-0.174ρ+0.328 0 -0.857 极强 δs-e δs=0.183e-0.126 0 0.849 极强 δs-n δs=0.007n-0.277 0 0.830 极强 δs-ρd δs=-0.251ρd+0.399 0 -0.829 极强 δs-Sr δs=-0.001Sr+0.116 0 -0.752 强 δs-ω δs=-0.005ω+0.128 0 -0.645 强 δs-IL δs=-0.037IL+0.043 0 -0.580 中等 δs-ωL δs=0.006ωL-0.095 0.019 0.251 弱 δs-ωP δs=0.006ωp-0.047 0.047 0.203 弱 δs-Es δs=-0.002Es+0.075 0.050 -0.200 无 δs-IP δs=0.009Ip-0.015 0.068 0.181 无 δs-a δs=0.029a+0.047 0.077 0.173 无 δs-h δs=-0.001 52h+0.064 32 0.293 -0.067 无
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表 4 回归系数及检验
Table 4. Regression coefficient and test
模型 参数 非标准化系数 标准系数 t Sig B 标准误差 1 (常数) 1.863 1.663 1.121 0.02 含水率ω -0.002 0.006 -0.278 -0.374 0.00 密度ρ -0.835 0.609 -4.103 -1.373 0.01 饱和度Sr 0.004 0.001 2.083 3.483 0.00 孔隙率n -0.013 0.017 -1.590 -0.766 0.00
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表 5 数据值处理摘要
Table 5. Data value processing summary
数据信息 样本数N/组 百分比 训练 35 50.7% 测试 30 43.5% 保留 4 5.8% 有效 69 100% 已排除 0 总计 69
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