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CLC number: TU4; P64

On-line Access: 2020-06-10

Received: 2019-12-05

Revision Accepted: 2020-02-23

Crosschecked: 2020-05-13

Cited: 0

Clicked: 423

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Lu-lu Zhang

https://orcid.org/0000-0001-8864-4377

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Journal of Zhejiang University SCIENCE A 2020 Vol.21 No.6 P.478-495

http://doi.org/10.1631/jzus.A1900558


Characterization of spatial variability with observed responses: application of displacement back estimation


Author(s):  Yi-xuan Sun, Lu-lu Zhang, Hao-qing Yang, Jie Zhang, Zi-jun Cao, Qi Cui, Jun-yi Yan

Affiliation(s):  State Key Laboratory of Ocean Engineering, Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200092, China; more

Corresponding email(s):   lulu_zhang@sjtu.edu.cn

Key Words:  Soil spatial variability, Probabilistic estimation, Displacement, Correlation length, Model test


Yi-xuan Sun, Lu-lu Zhang, Hao-qing Yang, Jie Zhang, Zi-jun Cao, Qi Cui, Jun-yi Yan. Characterization of spatial variability with observed responses: application of displacement back estimation[J]. Journal of Zhejiang University Science A, 2020, 21(6): 478-495.

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author="Yi-xuan Sun, Lu-lu Zhang, Hao-qing Yang, Jie Zhang, Zi-jun Cao, Qi Cui, Jun-yi Yan",
journal="Journal of Zhejiang University Science A",
volume="21",
number="6",
pages="478-495",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1900558"
}

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%A Yi-xuan Sun
%A Lu-lu Zhang
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%I Zhejiang University Press & Springer
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A1 - Yi-xuan Sun
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A1 - Hao-qing Yang
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A1 - Zi-jun Cao
A1 - Qi Cui
A1 - Jun-yi Yan
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DOI - 10.1631/jzus.A1900558


Abstract: 
soil spatial variability is difficult to evaluate due to insufficient test data. An alternative option is estimation by indirect methods such as inverse analysis. In this paper, two examples are presented to demonstrate the capability and accuracy of the probabilistic estimation method to characterize soil spatial variability with displacement responses. The first example is a soil slope subject to a surcharge load, in which the spatially varied field of the elastic modulus is estimated with displacements. The results show that estimations based on horizontal displacements were more accurate than those based on vertical displacements. The accuracy of the estimated field was substantially reduced by increasing variance of elastic modulus. However, the estimation was generally acceptable as the error was not more than 10%, even for the high variance case (COVE=1.5). The accuracy of estimation was also affected by the type of covariance function and the correlation length. When the correlation length decreased, the accuracy of estimation was reduced. The second example is a validation of laboratory model tests where a horizontal load was applied on a layered ground. The estimated thicknesses of soil layers were close to those in the real situation, which demonstrates the capacity of the estimation method.

基于观测响应的土体空间变异性表征:位移反分析应用

目的:由于现场勘察和室内土工试验数据的不足,因此土体空间变异性难以估计. 通过间接方法如反演分析方法进行估算是一个有效的途径,而土体参 数空间变异性概率反演估计的准确性受变异特性自身影响. 本文旨在通过算例研究和模型试验验证,明确影响土体空间变异性反演准确性的关键因素,以期为岩土勘察测试工程实践提供 参考.
创新点:1. 通过土坡空间变异性反演分析,揭示数据类型、变异系数、相关长度和协方差函数类型等对反演的影响; 2. 室内分层土模型试验验证表明,概率反演分析方法可有效地识别土体层厚和内摩擦角变异性.
方法:1. 通过边坡数值算例,研究位移监测数据类型、土体相关长度、弹性模量变异系数以及协方差函数对弹性模量空间变异性的位移反分析的影响(图5、6、9、11和12). 2. 开展室内模型试验,利用粒子图像测试技术获取位移监测数据,对分层土体内摩擦角的变异性进行识别,并研究软弱夹层位置与厚度对反分析的影响(图14).
结论:1. 水平位移比竖直位移更适合用于位移反分析. 2. 反分析精度在可接受范围内,且对于高变异性的情况(COVE=1.5),误差不超过10%; 此外,反分析精度还受协方差函数类型和相关长度的影响. 3. 反分析可识别出模型试验的土体分层,并且对内摩擦角的估计误差小于10%.

关键词:土体空间变异性; 概率估计; 位移; 相关长度; 模型试验

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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