基于无监督方法确定岩土参数取值

阮永芬; 李鹏辉; 朱强; 王勇; 闫明

doi:10.16030/j.cnki.issn.1000-3665.202207046

基于无监督方法确定岩土参数取值

doi: 10.16030/j.cnki.issn.1000-3665.202207046

阮永芬^1,,
李鹏辉^1, ,,
朱强²,
王勇³,
闫明⁴

1.
昆明理工大学建筑工程学院，云南昆明　650500
2.
中铁十六局集团有限公司北京交通轨道建设工程有限公司，北京　101100
3.
昆明军龙岩土工程有限公司，云南昆明　650214
4.
中铁二十局集团第五工程有限公司，云南昆明　650000

基金项目: 中铁二十局集团第五工程有限公司科研计划项目（CR2005-5-JS-2021-009）

详细信息

作者简介:
阮永芬（1964-），女，博士，教授，主要从事岩土工程方面的研究。E-mail：rryy64@163.com

通讯作者:
李鹏辉（1999-），男，硕士研究生，主要从事岩土工程方面的研究。E-mail：1017343481@qq.com

中图分类号: TU443
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出版历程
- 收稿日期: 2022-07-28
- 修回日期: 2022-10-20
- 网络出版日期: 2023-04-03

Determination of geotechnical parameters based on the unsupervised learning method

1.
Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming, Yunnan　650500, China
2.
China Railway 16 Bureau Group Metro Engineering Construction Co. Ltd., Beijing　101100, China
3.
Kunming Junlong Geotechnical Engineering Co. Ltd., Kunming, Yunnan　650214, China
4.
No.5 Engineering Corporation Limited of CR20G, Kunming, Yunnan　650000, China

摘要

摘要: 随着城市工程建设的发展，建筑工程事故问题愈发突出，采用传统方法求取的岩土参数区间无法满足实际工程需要。基于无监督学习思想，选取工程性质较差的泥炭质土，结合工程经验选用8个物理指标作为输入集，利用主成分分析（PCA）算法实现多样本多参数去耦合的降维处理，得出各物理指标相关性及敏感度，结合其相关性及敏感度赋予不同埋深泥炭质土物理指标的综合评价值。利用k-means聚类分析泥炭质土物理指标、综合评价值及工程特性之间关系，为岩土参数选取提供理论基础。采用监督学习方法——BP神经网络算法分析无监督结果，验证（PCA—k-means）算法模型的合理性。将通过聚类分析得到的正态样本利用多种截尾法优化，得到可靠取值区间，并将取值结果与实际工程取值比较，验证该模型工程参数取值合理性。该算法模型具有较好的工程应用价值，所得研究结果可为工程勘察、设计、施工参数取值提供参考，也能为岩土参数取值分析提供新的分析方法。
- 主成分分析 /
- k-means聚类 /
- BP神经网络 /
- 截尾法 /
- 岩土参数
Abstract: With the development of urban engineering construction, the issue of construction engineering accidents has become more and more prominent. The geotechnical parameter interval obtained by using the traditional methods cannot meet the needs of actual engineering. Based on the idea of unsupervised learning, the peaty soil with the worst engineering properties is considered, and 8 physical indexes are selected as the input set. The principal component analysis (PCA) algorithm is used to realize the dimensionality reduction of multi-sample and multi-parameter decoupling, and the correlation and sensitivity of each physical index is obtained. Combined with its correlation and sensitivity, the comprehensive evaluation value of physical indexes of peat soil with different buried depths is given. The k-means clustering is used to analyze the relationship among physical index, and comprehensive evaluation value and engineering characteristics of peaty soil provide a theoretical basis for the selection of geotechnical parameters. The supervised learning method-BP neural network algorithm is used to analyze the unsupervised results and verify the accuracy of the (PCA—k-means) algorithm model. The normal samples obtained by clustering analysis are optimized by a variety of truncation methods to obtain a reliable value range, and the value results are compared with the actual engineering values to verify the rationality of the model engineering parameters. The algorithm model is of good engineering application value. The research results can provide references for engineering investigation, design and construction parameter values, and also provide a new analysis method for geotechnical parameter value analyses.
- principal component analysis /
- k-means clustering /
- BP neural networks /
- truncated method /
- geotechnical parameters

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图 1 会展中心地层构造图

Figure 1. Stratigraphic diagram of the convention center

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图 2 ρ与w、w_L、e、a_1-2关系曲线

Figure 2. Relationship of ρ and w, w_L, e and a_1-2

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图 3 w_u与w、w_L、e、a_1-2关系曲线

Figure 3. Relationship of w_u and w, w_L, e and a_1-2

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图 4 特征值变化规律

Figure 4. Variation of elgenvalues

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图 5 累计贡献率

Figure 5. Cumulative contribution

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图 6 判别指标分类结果

Figure 6. Discriminative index classification results

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图 7 综合评价分类结果

Figure 7. Results of comprehensive evaluation

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图 8 e、w与w_L分类结果

Figure 8. Classification results of e、w and w_L

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图 9 分类结果对比

Figure 9. Comparison of classification results

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图 10 综合评价值对比

Figure 10. Comprehensive evaluation and comparison

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图 11 3个物理指标原样本分布图

Figure 11. Original sample distribution map of three physical indicators

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图 12 分类后3个物理指标样本分布图

Figure 12. Sample distribution map of three physical indicators after classification

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表 1 各层泥炭质土的物理指标统计表

Table 1. Statistical table of physical indexes of peaty soil in each layer

地层编号	ρ /（g·cm⁻³）	Gs	w /%	w_L /%	w_P /%	e	a_1-2 /MPa⁻¹	w_u /%
$\text{③}_1^1$	1.61	2.30	110.1	135.4	100.0	2.49	1.62	24.33
$\text{③}_2 $	1.42	2.42	101.2	119.2	87.2	2.40	1.32	18.36
$\text{④}_1 $	1.25	1.95	145.7	182.1	146.4	2.93	1.49	40.10
$\text{⑤}_{12}$	1.27	1.97	130.6	174.3	140.6	2.52	1.12	38.77

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表 2 不同物理指标间的相关性系数

Table 2. Correlation between different physical indicators

R^,2	ρ	G_S	w	w_L	e	a_1-2	w_P	w_u
ρ	1.00
G_S	0.64	1.00
w	0.87	0.55	1.00
w_L	0.89	0.59	0.94	1.00
e	0.85	0.85	0.94	0.87	1.00
a_1-2	0.62	0.42	0.81	0.68	0.85	1.00
w_P	0.85	0.58	0.82	0.93	0.58	0.49	1.00
w_u	0.83	0.66	0.73	0.78	0.70	0.45	0.80	1.00

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表 3 各物理指标敏感度统计表

Table 3. Sensitivity statistics of each physical index

物理指标编码	X₁（ρ）	X₂ （G_S）	X₃ （w）	X₄（w_L）	X₅（e）	X₆（a_1-2）	X₇ （w_P）	X₈ （w_u）
敏感度	0.2349	0.2069	0.2540	0.2428	0.2608	0.2151	0.2410	0.2281

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表 4 各组泥炭质土综合评价值排序

Table 4. Ranking of comprehensive evaluation of the peaty soil

组类	ρ/（g·cm⁻³）	w/%	G_S	e	w_L/%	w_P/%	a_1-2/MPa^-1	w_u/%	综合评价
1	1.565	57.590	2.517	1.536	68.690	42.690	0.722	8.100	–1.295
2	1.533	67.980	2.397	1.565	71.730	44.910	0.812	12.670	–1.162
3	1.499	68.280	2.428	1.699	76.180	48.270	1.103	11.690	–1.096
4	1.483	72.120	2.436	1.824	84.800	49.560	0.977	12.270	–1.035
5	1.452	73.690	2.238	1.882	92.400	58.890	0.850	13.678	–0.928
6	1.454	78.070	2.426	1.971	90.320	53.550	1.121	12.100	–0.946
7	1.455	82.180	2.426	1.957	95.363	62.181	0.833	13.318	–0.900
…	…	…	…	…	…	…	…	…	…
34	1.115	211.200	1.796	4.406	228.270	149.400	3.802	43.750	1.293
35	1.134	220.200	1.164	4.761	228.710	144.100	4.718	44.346	1.496
36	1.117	232.700	1.311	4.818	238.770	149.310	5.031	40.817	1.566
37	1.121	247.000	0.996	5.327	249.400	156.300	6.006	41.710	1.861
38	1.079	287.182	0.990	5.839	304.936	207.800	5.978	56.455	2.574

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表 5 不同截尾法的原物理指标区间范围

Table 5. Original physical index range of different censoring methods

物理指标	均值	标准差	偏度系数	变异系数	3σ区间范围	c₃₃区间范围	c₃区间范围
e	3.03	1.20	0.94	0.39	[0,6.63]	[0.56,7.76]	[0，7.76]
w/%	133.02	62.09	0.93	0.46	[0,319.29]	[4.48,377.03]	[0,377.03]
w_L/%	148.63	61.26	0.87	0.41	[0,332.41]	[18.14,385.71]	[0,385.71]

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表 6 物理指标样本检验结果

Table 6. Normal test result of the physical index sample

指标	分布类型	D值	P值	检验结果
e（图11）	正态分布	0.155	1.81×10^–4	排除
e（图12）	正态分布	0.090	1.00	接受
w（图11）	正态分布	0.189	2.36×10^–12	排除
w（图12）	正态分布	0.064	0.710	接受
w_L（图11）	正态分布	0.146	1.62×10^–7	排除
w_L（图12）	正态分布	0.072	0.190	接受

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表 7 不同截尾法的新样本区间范围

Table 7. Range of new sample intervals for different censoring methods

物理指标	均值	标准差	偏度系数	变异系数	3σ区间范围	c₃₃区间范围	c₃区间范围
e	2.19	0.40	0.39	0.18	[0.99,3.39]	[1.45,3.55]	[0.99,3.55]
w/%	88.68	17.40	0.39	0.18	[36.48,140.88]	[43.26,147.66]	[36.48,140.88]
w_L/%	105.55	22.11	0.28	0.20	[39.22,171.88]	[45.41,178.07]	[39.22,178.08]
ρ/（g·cm⁻³）	1.40	0.08	0.08	0.06	[1.13,1.66]	[1.15,1.67]	[1.13,1.67]
G_s	2.25	0.45	–3.08	0.20	[0.89,3.61]	[1.15,3.55]	[1.15,3.61]
a_1-2/MPa⁻¹	1.24	0.56	1.56	0.45	[0,2.93]	[0.43,3.81]	[0,2.93]
w_P/%	68.76	18.06	0.38	0.26	[14.57,122.97]	[21.48,129.88]	[14.57,129.88]
w_u/%	17.63	7.64	2.02	0.43	[0,40.57]	[10.17,56.06]	[0,56.06]

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表 8 各层泥炭质土设计参数检验结果

Table 8. Test results of design parameters of the peaty soil in each layer

地层编号	ρ /（g·cm⁻³）	w /%	e	I_L	a_1-2 /MPa⁻¹	综合评价	所属类
$ \text{③}_1^1$	1.37	103.0	2.41	0.15	1.63	−0.23	第一类
$\text{③}_2 $	1.39	91.1	2.33	0.12	1.30	−0.26	第一类
$\text{④}_1 $	1.20	142.3	2.92	−0.14	1.47	−0.08	第一类
$\text{⑤}_{12} $	1.26	127.7	2.53	−0.28	1.12	−0.16	第一类

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