剪胀型土的试验大数据深度挖掘与本构关系研究

杨骏堂; 刘元雪; 郑颖人; 柏准; 赵久彬

doi:10.11779/CJGE202103015

剪胀型土的试验大数据深度挖掘与本构关系研究 English Version

陆军勤务学院岩土力学与地质环境保护重庆市重点实验室,重庆　401311

基金项目:

国家自然科学基金项目 41877219

重庆市自然科学基金项目 cstc2019jcyj-msxm0585

重庆市规划和自然资源局科技计划项目 KJ-2018016

详细信息

作者简介:
杨骏堂（1991— ）,男,博士研究生,主要从事大数据与岩土本构关系的研究工作。E-mail：yangjt@aliyun.com

通讯作者:
刘元雪, E-mail: lyuanxue@vip.sina.com

中图分类号: TU433
计量
- 文章访问数: 286
- HTML全文浏览量: 21
- PDF下载量: 161
出版历程
- 收稿日期: 2020-03-29
- 网络出版日期: 2022-12-04
- 刊出日期: 2021-02-28

Deep mining of big data of tests and constitutive relation of dilative soils

Army Logistics University of PLA, Chongqing Key Laboratory of Geomechanics and Geoenvironment Protection, Chongqing 401311, China

摘要

摘要: 由于受到传统本构理论的约束以及未对土体基本力学特性的共同变化规律进行深入研究,使得当前建立的大多数本构模型并不能良好的反映土体实际变形机制。搭建了基于Hadoop+Spark的大数据处理平台,结合泛函网络和AIC评判准则,提出了一种能用于剪胀型土试验大数据深度挖掘研究的分布式自适应自回归算法。利用该算法,基于各塑性系数的大数据特征关系,再结合其显著性和次要影响因素的综合作用,在广义塑性力学的理论基础上建立了剪胀型土的本构模型。通过模型的验证试验,结果表明本文模型的预测效果要优于修正剑桥模型和考虑剪胀性的类剑桥模型,并且对不同应力路径下的剪胀型土的本构特性具有更强的适应性。将大数据技术和广义塑性力学应用于土的本构关系研究,有效突破了传统本构理论的束缚,具有更为广泛的理论意义,同时也为土的本构关系研究提供了新的思路。
- 剪胀型土 /
- 力学特性 /
- 深度挖掘 /
- 大数据 /
- 本构关系
Abstract: Due to the restriction of the traditional constitutive theory and the lack of in-depth studies on the common change laws of the basic mechanical characteristics of soils, most of the constitutive models established at present cannot reflect the actual deformation mechanism of soils well. A big data processing platform of Hadoop and Spark is built. By using the functional network and the AIC criteria, a distributed adaptive auto-regressive algorithm is proposed for deep mining of big data of tests on dilative soils. Based on the big data characteristic relationship of each plastic coefficient, combined with its significant and secondary influence factors, the constitutive model for dilative soils is established based on the theory of generalized plastic mechanics. Through the model verification experiments, the results show that the proposed model is better than the modified Cambridge model and the similar Cambridge model considering the dilatancy, and has strong adaptability to the expression of the mechanical properties of the dilative soils under different stress paths. The big data technology and generalized plastic mechanics are applied to the studies on the constitutive relationship of soils, which effectively breaks through the shackles of the traditional constitutive theory, and is of more extensive theoretical significance. At the same time, it also provides a new idea for the studies on the constitutive relationship of soils.
- dilative soil /
- mechanical property /
- deep mining /
- big data /
- constitutive relation

HTML全文

图 1 Yarn-Standalone的工作流程图

Figure 1. Flow chart of work of Yarn-Standalone

下载: 全尺寸图片幻灯片

图 2 ${\bar{ε}}_{v d}^{p}$ ${\bar{ε}}_{v d}^{p}$ - ${\bar{p}}_{d}$ ${\bar{p}}_{d}$ 的大数据关系

Figure 2. Big data relationship between ${\bar{ε}}_{v d}^{p}$ ${\bar{ε}}_{v d}^{p}$ and ${\bar{p}}_{d}$ ${\bar{p}}_{d}$

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图 3 $B$ $B$ 与应力应变参数之间的MIC值

Figure 3. Values of MIC between $B$ $B$ and stress-strain parameters

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图 4 $B$ $B$ 随 $η$ $η$ 的变化过程

Figure 4. Change process of $B$ $B$ with $η$ $η$

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图 5 $B$ $B$ 与 $\bar{η}$ $\bar{η}$ 的大数据关系

Figure 5. Big data relationship between $B$ $B$ and $\bar{η}$ $\bar{η}$

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图 6 $B_{e}$ $B_{e}$ 与 $\bar{p}$ $\bar{p}$ 的大数据关系

Figure 6. Big data relationship between $B_{e}$ $B_{e}$ and $\bar{p}$ $\bar{p}$

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图 7 $B_{e}$ $B_{e}$ 与 ${\bar{ε}}_{v}^{p}$ ${\bar{ε}}_{v}^{p}$ 的大数据关系

Figure 7. Big data relationship between $B_{e}$ $B_{e}$ and ${\bar{ε}}_{v}^{p}$ ${\bar{ε}}_{v}^{p}$

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图 8 $B_{e}$ $B_{e}$ 与 ${\bar{ε}}_{s}^{p}$ ${\bar{ε}}_{s}^{p}$ 的大数据关系

Figure 8. Big data relationship between $B_{e}$ $B_{e}$ and ${\bar{ε}}_{s}^{p}$ ${\bar{ε}}_{s}^{p}$

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图 9 $B_{e}$ $B_{e}$ 与 $\bar{d p}$ $\bar{d p}$ , $\bar{d q}$ $\bar{d q}$ 之间的大数据关系

Figure 9. Big data relationship between $B_{e}$ $B_{e}$ , $\bar{d p}$ $\bar{d p}$ and $\bar{d q}$ $\bar{d q}$

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图 10 $C$ $C$ 与 $\bar{η}$ $\bar{η}$ 的大数据关系

Figure 10. Big data relationship between $C$ $C$ and $\bar{η}$ $\bar{η}$

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图 11 $C_{e}$ $C_{e}$ 与 $\bar{p}$ $\bar{p}$ 的大数据关系

Figure 11. Big data relationship between $C_{e}$ $C_{e}$ and $\bar{p}$ $\bar{p}$

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图 12 $C_{e}$ $C_{e}$ 与 $\bar{ε_{v}^{p}}$ $\bar{ε_{v}^{p}}$ 的大数据关系

Figure 12. Big data relationship between $C_{e}$ $C_{e}$ and $\bar{ε_{v}^{p}}$ $\bar{ε_{v}^{p}}$

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图 13 $C_{e}$ $C_{e}$ 与 $\bar{ε_{s}^{p}}$ $\bar{ε_{s}^{p}}$ 的大数据关系

Figure 13. Big data relationship between $C_{e}$ $C_{e}$ and $\bar{ε_{s}^{p}}$ $\bar{ε_{s}^{p}}$

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图 14 $C_{e}$ $C_{e}$ 与 $\bar{d p}$ $\bar{d p}$ , $\bar{d q}$ $\bar{d q}$ 之间的大数据关系

Figure 14. Big data relationship among $C_{e}$ $C_{e}$ , $\bar{d p}$ $\bar{d p}$ and $\bar{d q}$ $\bar{d q}$

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图 15 预测曲线与试验值的比较

Figure 15. Comparison between predicted curves and experimental values

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表 1 部分数据主要来源

Table 1 Main sources of data

序号	国家	期刊名称	SJR	H-index
1	英国	Geotechnique	2.571	114
2	中国	岩土工程学报	0.655	42
3	加拿大	Canadian Geotechnical Journal	1.753	100
4	英国	Computers and Geotechnics	1.946	79
5	日本	Soils and Foundations	1.246	64

下载: 导出CSV

表 2 部分土样本的数据

Table 2 Data of some soil samples

编号	试验序列点	$d$ $d$	$p /kPa$ $p /kPa$	$q /kPa$ $q /kPa$	$d p /kPa$ $d p /kPa$	$d q /kPa$ $d q /kPa$	$ε_{v}^{p} /%$ $ε_{v}^{p} /%$	$ε_{s}^{p} /%$ $ε_{s}^{p} /%$	$\dots$ $\dots$
JZ-12	1	0.67	715.12	795.28	40.23	120.69	0.26	0.39	︙
	2	0.44	745.43	886.28	30.32	90.96	0.48	0.89	︙
	︙	︙	︙	︙	︙	︙	︙	︙	︙
	24	-0.02	820.84	1112.52	-2.51	-7.53	0.56	9.31	︙
	︙	︙	︙	︙	︙	︙	︙	︙	︙

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表 3 $B_{e}$ $B_{e}$ 与各次要影响因素之间的MIC值

Table 3 Values of MIC between $B_{e}$ $B_{e}$ and secondary influencing factors

$m_{p - B_{e}}$ $m_{p - B_{e}}$	$m_{ε_{v}^{p} - B_{e}}$ $m_{ε_{v}^{p} - B_{e}}$	$m_{ε_{s}^{p} - B_{e}}$ $m_{ε_{s}^{p} - B_{e}}$	$m_{d p - B_{e}}$ $m_{d p - B_{e}}$	$m_{d q - B_{e}}$ $m_{d q - B_{e}}$
0.689	0.485	0.654	0.660	0.661

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表 4 验证试验的模型参数值

Table 4 Values of model parameters validation experiments

编号	$λ$ $λ$	$κ$ $κ$	$M$ $M$	$M_{d}$ $M_{d}$	$ν$ $ν$
土1	0.0068	0.0042	1.66	0.95	0.31
土2	0.0161	0.0051	1.35	0.87	0.30
土3	0.0106	0.0049	1.58	0.89	0.32

下载: 导出CSV

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