Full Text:   <2026>

Summary:  <1543>

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: 2814

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Lu-lu Zhang

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

-   Go to

Article info.
Open peer comments

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.

@article{title="Characterization of spatial variability with observed responses: application of displacement back estimation",
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"
}

%0 Journal Article
%T Characterization of spatial variability with observed responses: application of displacement back estimation
%A Yi-xuan Sun
%A Lu-lu Zhang
%A Hao-qing Yang
%A Jie Zhang
%A Zi-jun Cao
%A Qi Cui
%A Jun-yi Yan
%J Journal of Zhejiang University SCIENCE A
%V 21
%N 6
%P 478-495
%@ 1673-565X
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1900558

TY - JOUR
T1 - Characterization of spatial variability with observed responses: application of displacement back estimation
A1 - Yi-xuan Sun
A1 - Lu-lu Zhang
A1 - Hao-qing Yang
A1 - Jie Zhang
A1 - Zi-jun Cao
A1 - Qi Cui
A1 - Jun-yi Yan
J0 - Journal of Zhejiang University Science A
VL - 21
IS - 6
SP - 478
EP - 495
%@ 1673-565X
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
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

Reference

[1]Ahmed AS, Jardani A, Revil A, et al., 2015. HT2DINV: a 2D forward and inverse code for steady-state and transient hydraulic tomography problems. Computers & Geosciences, 85:36-44.

[2]Atkinson KE, 1967. The numerical solution of Fredholm integral equations of the second kind. SIAM Journal on Numerical Analysis, 4(3):337-348.

[3]Baecher GB, Christian JT, 2003. Reliability and Statistics in Geotechnical Engineering. John Wiley & Sons, Chichester, UK.

[4]Bilgin Ö, Arens K, Dettloff A, 2019. Assessment of variability in soil properties from various field and laboratory tests. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 13(4):247-254.

[5]Bowles JE, 1996. Foundation Analysis and Design, 5th Edition. McGraw-Hill, New York, USA.

[6]Box GE, Tiao GC, 2011. Bayesian Inference in Statistical Analysis, Vol. 40. Wiley, New York, USA.

[7]Cao ZJ, Wang Y, 2013. Bayesian approach for probabilistic site characterization using cone penetration tests. Journal of Geotechnical and Geoenvironmental Engineering, 139(2):267-276.

[8]Chao WL, Wang XH, 2011. Research of mechanical parameter back analysis for the stratified soil slope. Applied Mechanics and Materials, 71-78:1893-1897.

[9]Chen RH, Wu CP, Huang FC, et al., 2013. Numerical analysis of geocell-reinforced retaining structures. Geotextiles and Geomembranes, 39:51-62.

[10]Chen Y, Zhang DX, 2006. Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Advances in Water Resources, 29(8):1107-1122.

[11]Chowdhury R, Zhang S, Flentje P, 2004. Reliability updating and geotechnical back-analysis. In: Jardine RJ, Potts DM, Higgins KG (Eds.), Advances in Geotechnical Engineering: the Skempton Conference. Thomas Telford, London, UK, p.815-821.

[12]Crisp MP, Jaksa MB, Kuo YL, et al., 2019. A method for generating virtual soil profiles with complex, multi-layer stratigraphy. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 13(2):154-163.

[13]Dasaka SM, Zhang LM, 2012. Spatial variability of in situ weathered soil. Géotechnique, 62(5):375-384.

[14]Fenton GA, Griffiths DV, 2002. Probabilistic foundation settlement on spatially random soil. Journal of Geotechnical and Geoenvironmental Engineering, 128(5):381-390.

[15]Gavin K, Xue J, 2009. Use of a genetic algorithm to perform reliability analysis of unsaturated soil slopes. Géotechnique, 59(6):545-549.

[16]Gelman A, Rubin DB, 1992. Inference from iterative simulation using multiple sequences. Statistical Science, 7(4):457-472.

[17]Ghanem RG, Spanos PD, 1991. Spectral stochastic finite-element formulation for reliability analysis. Journal of Engineering Mechanics, 117(10):2351-2372.

[18]Ghanem RG, Spanos PD, 2003. Stochastic Finite Elements: a Spectral Approach. Courier Corporation, Massachusetts, USA.

[19]Gilbert RB, Wright SG, Liedtke E, 1998. Uncertainty in back analysis of slopes: Kettleman hills case history. Journal of Geotechnical and Geoenvironmental Engineering, 124(12):1167-1176.

[20]Huang SP, Quek ST, Phoon KK, 2001. Convergence study of the truncated Karhunen–Loeve expansion for simulation of stochastic processes. International Journal for Numerical Methods in Engineering, 52(9):1029-1043.

[21]Huang SP, Mahadevan S, Rebba R, 2007. Collocation-based stochastic finite element analysis for random field problems. Probabilistic Engineering Mechanics, 22(2):194-205.

[22]Ishii Y, Ota K, Kuraoka S, et al., 2012. Evaluation of slope stability by finite element method using observed displacement of landslide. Landslides, 9(3):335-348.

[23]Jiang SH, Li DQ, Zhang LM, et al., 2014. Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method. Engineering Geology, 168:120-128.

[24]Jiang SH, Li DQ, Cao ZJ, et al., 2015. Efficient system reliability analysis of slope stability in spatially variable soils using Monte Carlo simulation. Journal of Geotechnical and Geoenvironmental Engineering, 141(2):04014096.

[25]Jiang SH, Papaioannou I, Straub D, 2018. Bayesian updating of slope reliability in spatially variable soils with in-situ measurements. Engineering Geology, 239:310-320.

[26]Jin YF, Yin ZY, Shen SL, et al., 2016. Selection of sand models and identification of parameters using an enhanced genetic algorithm. International Journal for Numerical and Analytical Methods in Geomechanics, 40(8):1219-1240.

[27]Jin YF, Yin ZY, Shen SL, et al., 2017. A new hybrid real-coded genetic algorithm and its application to parameters identification of soils. Inverse Problems in Science and Engineering, 25(9):1343-1366.

[28]Jin YF, Yin ZY, Wu ZX, et al., 2018. Identifying parameters of easily crushable sand and application to offshore pile driving. Ocean Engineering, 154:416-429.

[29]Jin YF, Yin ZY, Zhou WH, et al., 2019a. Bayesian model selection for sand with generalization ability evaluation. International Journal for Numerical and Analytical Methods in Geomechanics, 43(14):2305-2327.

[30]Jin YF, Yin ZY, Zhou WH, et al., 2019b. Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method. Acta Geotechnica, 14(6):1925-1947.

[31]Jin YF, Yin ZY, Zhou WH, et al., 2019c. Multi-objective optimization-based updating of predictions during excavation. Engineering Applications of Artificial Intelligence, 78:102-123.

[32]Karhunen K, 1947. Über lineare methoden in der wahrscheinlichkeitsrechnung. Annales Academiae Scientiarum Fennicae, 37:1-79 (in German).

[33]Ledesma A, Gens A, Alonso EE, 1996. Parameter and variance estimation in geotechnical backanalysis using prior information. International Journal for Numerical and Analytical Methods in Geomechanics, 20(2):119-141.

[34]Lee KZZ, Chang NY, 2012. Predictive modeling on seismic performances of geosynthetic-reinforced soil walls. Geotextiles and Geomembranes, 35:25-40.

[35]Leshchinsky B, Ling HI, 2013. Numerical modeling of behavior of railway ballasted structure with geocell confinement. Geotextiles and Geomembranes, 36:33-43.

[36]Li DQ, Qi XH, Phoon KK, et al., 2014. Effect of spatially variable shear strength parameters with linearly increasing mean trend on reliability of infinite slopes. Structural Safety, 49:45-55.

[37]Li DQ, Jiang SH, Cao ZJ, et al., 2015. A multiple response-surface method for slope reliability analysis considering spatial variability of soil properties. Engineering Geology, 187:60-72.

[38]Li JH, Zhang LM, 2011. Study of desiccation crack initiation and development at ground surface. Engineering Geology, 123(4):347-358.

[39]Li JH, Zhou Y, Zhang LL, et al., 2016. Random finite element method for spudcan foundations in spatially variable soils. Engineering Geology, 205:146-155.

[40]Li YH, Zhang Q, Lin ZB, et al., 2016. Spatiotemporal evolution rule of rocks fracture surrounding gob-side roadway with model experiments. International Journal of Mining Science and Technology, 26(5):895-902.

[41]Liu K, Vardon PJ, Hicks MA, 2018. Sequential reduction of slope stability uncertainty based on temporal hydraulic measurements via the ensemble Kalman filter. Computers and Geotechnics, 95:147-161.

[42]Loève M, 1948. Fonctions Aléatoires de Second Ordre. Supplement to P. Levy, Proces Stochastiques et Mouvement Brownien. Gauthier-Villars, Paris, France (in French).

[43]Mavritsakis A, 2017. Evaluation of Inverse Analysis Methods with Numerical Simulation for Slope Excavation. MS Thesis, Delft University of Technology, Delft, the Netherland.

[44]Novák V, Šimåunek J, van Genuchten MT, 2000. Infiltration of water into soil with cracks. Journal of Irrigation and Drainage Engineering, 126(1):41-47.

[45]Pan QJ, Qu XR, Liu LL, et al., 2020. A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations. International Journal for Numerical and Analytical Methods in Geomechanics, 44(6):874-889.

[46]Phoon KK, Kulhawy FH, 1999a. Characterization of geotechnical variability. Canadian Geotechnical Journal, 36(4):612-624.

[47]Phoon KK, Kulhawy FH, 1999b. Evaluation of geotechnical property variability. Canadian Geotechnical Journal, 36(4):625-639.

[48]Phoon KK, Tang C, 2019. Characterisation of geotechnical model uncertainty. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 13(2):101-130.

[49]Qi XH, Li DQ, 2018. Effect of spatial variability of shear strength parameters on critical slip surfaces of slopes. Engineering Geology, 239:41-49.

[50]Smolyak SA, 1963. Quadrature and interpolation formulae on tensor products of certain function classes. Doklady Akademii Nauk SSSR, 4(5):1042-1045.

[51]Sun HY, Wang J, Wang DF, et al., 2020. Optimal design of prefabricated vertical drain-improved soft ground considering uncertainties of soil parameters. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 21(1):15-28.

[52]Tang WH, Ang AHS, 2007. Probability Concepts in Engineering: Emphasis on Applications in Civil & Environmental Engineering, 2nd Edition. John Wiley & Sons, New York, USA.

[53]Vardon PJ, Liu K, Hicks MA, 2016. Reduction of slope stability uncertainty based on hydraulic measurement via inverse analysis. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 10(3):223-240.

[54]Vrugt JA, ter Braak CJF, Clark MP, et al., 2008. Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov Chain Monte Carlo simulation. Water Resources Research, 45(12):W00B09.

[55]Wang L, Hwang JH, Luo Z, et al., 2013. Probabilistic back analysis of slope failure–a case study in Taiwan. Computers and Geotechnics, 51:12-23.

[56]Xiao J, Liu G, Liu J, et al., 2019. Parameters of a discrete element ballasted bed model based on a response surface method. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 20(9):685-700.

[57]Xiu DB, 2007. Efficient collocational approach for parametric uncertainty analysis. Communications in Computational Physics, 2(2):293-309.

[58]Yan L, Meng QX, Xu WY, et al., 2017. A numerical method for analyzing the permeability of heterogeneous geomaterials based on digital image processing. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 18(2):124-137.

[59]Yang HQ, Zhang LL, Li DQ, 2018. Efficient method for probabilistic estimation of spatially varied hydraulic properties in a soil slope based on field responses: a Bayesian approach. Computers and Geotechnics, 102: 262-272.

[60]Yang HQ, Zhang LL, Xue JF, et al., 2019. Unsaturated soil slope characterization with Karhunen-Loève and polynomial chaos via Bayesian approach. Engineering with Computers, 35(1):337-350.

[61]Yang HQ, Chen XY, Zhang LL, et al., 2020. Conditions of hydraulic heterogeneity under which Bayesian estimation is more reliable. Water, 12(1):160.

[62]Yang J, Yin ZY, Laouafa F, et al., 2019a. Analysis of suffusion in cohesionless soils with randomly distributed porosity and fines content. Computers and Geotechnics, 111: 157-171.

[63]Yang J, Yin ZY, Laouafa F, et al., 2019b. Internal erosion in dike-on-foundation modeled by a coupled hydromechanical approach. International Journal for Numerical and Analytical Methods in Geomechanics, 43(3):663-683.

[64]Yin ZY, Jin YF, Shen SL, et al., 2017. An efficient optimization method for identifying parameters of soft structured clay by an enhanced genetic algorithm and elastic-viscoplastic model. Acta Geotechnica, 12(4):849-867.

[65]Zhang J, Tang WH, Zhang LM, 2010. Efficient probabilistic back-analysis of slope stability model parameters. Journal of Geotechnical and Geoenvironmental Engineering, 136(1):99-109.

[66]Zhang J, Zhou CW, Jia C, et al., 2017. Powell inversion mechanical model of foundation parameters with generalized Bayesian theory. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 18(7):567-578.

[67]Zhang LL, Zhang J, Zhang LM, et al., 2010. Back analysis of slope failure with Markov Chain Monte Carlo simulation. Computers and Geotechnics, 37(7-8):905-912.

[68]Zhang LL, Zuo ZB, Ye GL, et al., 2013. Probabilistic parameter estimation and predictive uncertainty based on field measurements for unsaturated soil slope. Computers and Geotechnics, 48:72-81.

[69]Zhang LL, Zheng YF, Zhang LM, et al., 2014. Probabilistic model calibration for soil slope under rainfall: effects of measurement duration and frequency in field monitoring. Géotechnique, 64(5):365-378.

[70]Zhang LL, Li JH, Li X, et al., 2016. Rainfall-induced Soil Slope Failure: Stability Analysis and Probabilistic Assessment. CRC Press, Taylor & Francis Group, Boca Raton, USA.

[71]Zhang SR, Hu AK, Wang C, 2016. Three-dimensional inversion analysis of an in situ stress field based on a two-stage optimization algorithm. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 17(10):782-802.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE