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Quantitative identification of cutoff wall construction defects using Bayesian approach based on excess pore water pressure

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Abstract

Cutoff wall has been widely used as a barrier to prevent the migration of contaminants in aquifers. Hydraulic impermeability and homogeneity should be ensured before operation. The difficulties in the rapid evaluation of newly constructed cutoff wall lie in high-quality sampling and time-consuming laboratory testing. In this study, piezocone penetration test is used to evaluate the barrier performance of a newly built cutoff wall. A Bayesian approach combined with genetic algorithm is adopted. The weak layers of cutoff wall are identified using excess pore water pressure data. The causes of impermeability defect are quantitatively characterized through optimized model parameters. Then, through comparison with the results of traditional soil behavior type based classification method, it points out the inapplicability of traditional method to evaluate the goodness of cutoff wall. Finally, the continuous horizontal hydraulic permeability profile is assessed by combining pore water pressure dissipation tests with adopted Bayesian approach. Results show that adopted Bayesian approach using excess pore water pressure can properly identify the weak layers of the newly built cutoff wall and reflect the causes. The analysis results can be used as guiding information for making remediation plans.

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Abbreviations

k :

Hydraulic permeability

TRD:

Trench cutting and re-mixing deep wall

QC/QA:

Quality control/quality assurance

CPTU:

Piezocone penetration test

q t :

Cone tip resistance

f s :

Sleeve friction

u 2 :

Pore water pressure at the cone shoulder

k h :

Horizontal hydraulic permeability

GGBS:

Ground granulated blast-furnace slag

XRF:

X-ray fluorescence

K D :

Dimensionless hydraulic conductivity index

r :

Cone radius

γ w :

Unit weight of water

U :

Cone penetration rate

σ ′ v0 :

Vertical effective stress

B q :

Dimensionless pore water pressure ratio

Q t :

Dimensionless tip resistance

σ v0 :

Total vertical stress

Δu :

Excess pore water pressure

C :

Correlation coefficient between reciprocal of Δu and kh

KS test:

Kolmogorov–Smirnov test

H 0 :

Hypothesis

D α :

The value under significance level of α, reject the H0

n :

The number of the sample

CDF:

Cumulative distribution function

F n(x):

Empirical cumulative distribution function

F(x):

Pre-specified theoretical CDF

p :

The probability of observing a test statistic as extreme as, or more extreme than, the observed value under the H0 hypothesis

h :

The hypothesis test result returned as logical value (1 or 0)

N :

Number of soil layers

h n :

Thickness of the nth layer

a n :

Model parameter of the nth layer characterizes the influence of uneven mixing of backfill materials

b :

Model parameter characterizes the influence of consolidation and ages

σ n :

Model parameter of the nth layer characterizes the homogeneity

θ n :

Vector of the model parameters

Θ N :

[θ1, θ2, …, θn, …, θN]

K N :

Normalizing constant

P (Δu| Θ N, N):

The likelihood function

P(Θ N|N):

Prior distribution

M n :

The number of excess pore water pressure data within the nth layer

P (Δu n , i |θ n, N):

Likelihood function for the ith Δun data

D n , i :

Depth of the Δun,i data

P (θ n |N):

Prior distribution of model parameter θn

PDF:

Probability distribution function

Θ N * :

Most probable model parameters for N

SuS:

Subset simulation

GA:

Genetic algorithm

h N * :

Optimal layer thickness for N

N * :

Optimal number of layers

H(Θ N *):

Hessian matrix of N evaluated at ΘN*

j N :

The number of model parameters for N

−ln[P(Θ N|Δu, N)]:

Objective function

SBT:

Soil behavior type

I c :

Soil behavior type index

Q tn :

Normalized cone tip resistance

P a :

Atmosphere pressure

c :

Stress exponent based on the soil behavior type

F r :

Normalized friction ratio

C–S–H:

Calcium silicate hydrate

HT:

Hydrotalcite

t 50 :

Time for 50% excess pore water pressure dissipation

c h :

Horizontal coefficient of consolidation

m v :

Coefficient of volume compressibility

T * :

The time factor

I r :

Rigidity index

G :

Shear modulus

s u :

Undrained shear strength

References

  1. ASTM D5778 (2000) Standard Test Method for Electronic Friction Cone and Piezocone Penetration Testing of Soils. ASTM International, West Conshohocken

  2. ASTM D2216 (2010a) Standard test methods for laboratory determination of water (moisture) content of soil and rock by mass. ASTM International, West Conshohocken

  3. ASTM D4318 (2010b) Standard test methods for liquid limit, plastic limit, and plasticity index of soils. ASTM International, West Conshohocken

  4. ASTM D7503 (2010c) Standard test method for measuring the exchange complex and cation exchange capacity of inorganic fine-grained soils. ASTM International, West Conshohocken

  5. ASTM D4813 (2014) Standard test methods for specific gravity of soil solids by water pycnometer. ASTM International, West Conshohocken

  6. ASTM D2487-17 (2017) Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System). ASTM International, West Conshohocken

  7. ASTM D4972 (2018) Standard test method for pH of soils. ASTM International, West Conshohocken

  8. Bennert TA, Maher A, Jafari F (2005) Piezocone evaluation of a shallow soil-bentonite slurry wall. In: Waste containment and remediation, pp 1–14

  9. Cao ZJ, Wang Y (2013) Bayesian approach for probabilistic site characterization using cone penetration tests. J Geotech Geoenviron Eng 139(2):267–276

    Article  Google Scholar 

  10. Cao ZJ, Zheng S, Li DQ, Phoon KK (2019) Bayesian identification of soil stratigraphy based on soil behaviour type index. Can Geotech J 56(4):570–586

    Article  Google Scholar 

  11. Cerato AB, Lutenegger AJ (2002) Determination of surface area of fine-grained soils by the ethylene glycol mo-noethyl ether (EGME) method. Geotech Test J 25(3):315–321. https://doi.org/10.1520/GTJ11087J

    Article  Google Scholar 

  12. Chai JC, Agung PMA, Hino T, Igaya Y, Carter JP (2011) Estimating hydraulic conductivity from piezocone soundings. Géotechnique 61(8):699–708

    Article  Google Scholar 

  13. Chai J, Chanmee N (2018) A modified method for estimating permeability of clayey soils based on piezocone sounding results. Can Geotech J 55(9):1268–1281

    Article  Google Scholar 

  14. Ching J, Phoon KK, Chen YC (2010) Reducing shear strength uncertainties in clays by multivariate correlations. Can Geotech J 47(1):16–33

    Article  Google Scholar 

  15. Christian JT, Baecher GB (2011) Unresolved problems in geotechnical risk and reliability. In: Geo-risk 2011: risk assessment and management, pp 50–63

  16. Dagger R, Saftner D, Mayne P (2018) Cone penetration test design guide for state geotechnical engineers. Report No. 2018-32, Minnesota Dept. of Transportation, St. Paul, MN: 225 p. https://www.dot.state.mn.us/research/reports/2018/201832.pdf

  17. Dimitrova DS, Kaishev VK, Tan S (2020) Computing the Kolmogorov-Smirnov distribution when the underlying CDF is purely discrete, mixed, or continuous. J Stat Softw 95(1):1–42

    Google Scholar 

  18. Du YJ, Fan RD, Liu SY, Reddy KR, ** F (2015) Workability, compressibility and hydraulic conductivity of zeolite-amended clayey soil/calcium-bentonite backfills for slurry-trench cutoff walls. Eng Geol 195:258–268

    Article  Google Scholar 

  19. Du YJ, Wu J, Bo YL, Jiang NJ (2020) Effects of acid rain on physical, mechanical and chemical properties of GGBS–MgO-solidified/stabilized Pb-contaminated clayey soil. Acta Geotech 15(4):923–932

    Article  Google Scholar 

  20. Elsworth D, Lee DS (2005) Permeability determination from on-the-fly piezocone sounding. J Geotech Geoenviron Eng 131(5):643–653

    Article  Google Scholar 

  21. Fenton GA (1999) Random field modeling of CPT data. J Geotech Geoenviron Eng 125(6):486–498

    Article  Google Scholar 

  22. Gilbert RB, Tang WH (1995) Model uncertainty in offshore geotechnical reliability. Proceeding of the 27th offshore technology conference. Society of Petroleum Engineers, Houston, pp 557–567

    Google Scholar 

  23. Gruskovnjak A, Lothenbach B, Winnefeld F, Figi R, Ko SC, Adler M, Mäder U (2008) Hydration mechanisms of super sulphated slag cement. Cement Concrete Res 38(7):983–992

    Article  Google Scholar 

  24. Gull SF (1988) Bayesian inductive inference and maximum entropy. Maximum-entropy and Bayesian methods in science and engineering. Springer, Dordrecht, pp 53–74

    Chapter  Google Scholar 

  25. Jefferies MG, Davies MP (1993) Use of CPTU to estimate equivalent SPT N 60. Geotech Test J 16(4):458–468. https://doi.org/10.1520/GTJ10286J

    Article  Google Scholar 

  26. ** F, Al-Tabbaa A (2014) Evaluation of novel reactive MgO activated slag binder for the immobilization of lead and zinc. Chemosphere 117:285–294. https://doi.org/10.1016/j.chemosphere.2014.07.027

    Article  Google Scholar 

  27. ** F, Gu K, Al-Tabbaa A (2015) Strength and hydration properties of reactive MgO-activated ground granulated blastfurnace slag paste. Cement Concrete Comp 57:8–16. https://doi.org/10.1016/j.cemconcomp.2014.10.007

    Article  Google Scholar 

  28. ** F, Wang F, Al-Tabbaa A (2016) Three-year performance of in-situ solidified/stabilised soil using novel MgO-bearing binders. Chemosphere 144:681–688. https://doi.org/10.1016/j.chemosphere.2015.09.046

    Article  Google Scholar 

  29. Joshi K, Kechavarzi C, Sutherland K, Ng MYA, Soga K, Tedd P (2010) Laboratory and in situ tests for long-term hydraulic conductivity of a cement-bentonite cutoff wall. J Geotech Geoenviron Eng 136(4):562–572

    Article  Google Scholar 

  30. Katsumi T, Kamon M, Inui T, Araki S (2008) Hydraulic barrier performance of SBM cut-off wall constructed by the trench cutting and re-mixing deep wall method. In: GeoCongress 2008: geotechnics of waste management and remediation, pp 628–635

  31. Keramatikerman M, Chegenizadeh A, Nikraz H (2017) An investigation into effect of sawdust treatment on permeability and compressibility of soil-bentonite slurry cut-off wall. J Clean Prod 162:1–6

    Article  Google Scholar 

  32. Li L, Li J, Sun DA, Gong W (2019) A feasible approach to predicting time-dependent bearing performance of jacked piles from CPTu measurements. Acta Geotech 15(7):1935–1952. https://doi.org/10.1007/s11440-019-00875-x

    Article  Google Scholar 

  33. Li YC, Tong X, Chen Y, Ke H, Chen YM, Wen YD, Pan Q (2018) Non-monotonic piezocone dissipation curves of backfills in a soil-bentonite slurry trench cutoff wall. J Zhejiang Univ Sci A: Appl Phys Eng 19(4):277–288

    Article  Google Scholar 

  34. Malusis MA, Barlow LC (2020) Comparison of laboratory and field measurements of backfill hydraulic conductivity for a large-scale soil-bentonite cutoff wall. J Geotech Geoenviron Eng 146(8):04020070

    Article  Google Scholar 

  35. Malusis MA, Evans JC, Jacob RW, Ruffing D, Barlow L, Marchiori AM (2017) Construction and monitoring of an instrumented soil-bentonite cutoff wall: field research case study. In: Proceedings of the 29th Central Pennsylvania Geotechnical Conference, Hershey, PA, USA, pp 25–27

  36. Manassero M (1994) Hydraulic conductivity assessment of slurry wall using piezocone test. J Geotech En 120(10):1725–1746

    Article  Google Scholar 

  37. Monforte L, Arroyo M, Gens A, Carbonell JM (2018) Hydraulic conductivity from piezocone on-the-fly: a numerical evaluation. Géotechnique Lett 8(4):268–277

    Article  Google Scholar 

  38. Najjar SS, Gilbert RB (2009) Importance of lower-bound capacities in the design of deep foundations. J Geotech Geoenviron Eng 135(7):890–900

    Article  Google Scholar 

  39. Norris A, Di Emidio G, Malusis MA, Replogle M (2018) Modified bentonites for soil-bentonite cutoff wall applications with hard mix water. Appl Clay Sci 158:226–235

    Article  Google Scholar 

  40. Robertson PK (1990) Soil classification using the cone penetration test. Can Geotech J 27(1):151–158. https://doi.org/10.1139/t90-014

    Article  Google Scholar 

  41. Robertson PK (2010) Evaluation of flow liquefaction and liquefied strength using the cone penetration test. J Geotech Geoenviron Eng 136(6):842–853

    Article  Google Scholar 

  42. Robertson PK, Wride CE (1998) Evaluating cyclic liquefaction potential using the cone penetration test. Can Geotech J 35(3):442–459. https://doi.org/10.1139/t98-017

    Article  Google Scholar 

  43. Saka MP (2003) Optimum design of pitched roof steel frames with haunched rafters by genetic algorithm. Comput Struct 81(18–19):1967–1978

    Article  Google Scholar 

  44. Shand MA (2006) The chemistry and technology of magnesia. Wiley, Hoboken

    Book  Google Scholar 

  45. Takai A, Inui T, Katsumi T (2016) Evaluating the hydraulic barrier performance of soil-bentonite cutoff walls using the piezocone penetration test. Soils Found 56(2):277–290

    Article  Google Scholar 

  46. Tedd P, Butcher AP, Powell JJM (1997) Assessment of the piezocone to measure the in-situ properties of cement-bentonite slurry trench cut-off walls. In: Geoenvironmental engineering: contaminated ground: fate of pollutants and remediation, Thomas Telford, London, pp 48–55

    Google Scholar 

  47. Teh CI, Houlsby GT (1991) An analytical study of the cone penetration test in clay. Géotechnique 41(1):17–34

    Article  Google Scholar 

  48. Wang Y, Huang K, Cao Z (2013) Probabilistic identification of underground soil stratification using cone penetration tests. Can Geotech J 50(7):766–776

    Article  Google Scholar 

  49. Wang Y, Huang K, Cao Z (2014) Bayesian identification of soil strata in London clay. Géotechnique 64(3):239–246

    Article  Google Scholar 

  50. Wu SJ, Chow PT (1995) Steady-state genetic algorithms for discrete optimization of trusses. Comput Struct 56(6):979–991

    Article  Google Scholar 

  51. Wu H, ** F, Bo Y, Du Y, Zheng J (2018) Leaching and microstructural properties of lead contaminated kaolin stabilized by GGBS-MgO in semi-dynamic leaching tests. Constr Build Mater 172:626–634. https://doi.org/10.1016/j.conbuildmat.2018.03.164

    Article  Google Scholar 

  52. Wu HL, ** F, Zhou AN, Du YJ (2021) The engineering properties and reaction mechanism of MgO-activated slag cement-clayey sand-bentonite (MSB) cutoff wall backfills. Constr Build Mater 271:121890

    Article  Google Scholar 

  53. Xu H, Zhu W, Qian X, Wang S, Fan X (2016) Studies on hydraulic conductivity and compressibility of backfills for soil-bentonite cutoff walls. Appl Clay Sci 132:326–335

    Article  Google Scholar 

  54. Yang HQ, Zhang L, Pan Q, Phoon KK, Shen Z (2021) Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data. Acta Geotech 16:263–278

    Article  Google Scholar 

  55. Yi Y, Liska M, Al-Tabbaa A (2014) Properties of two model soils stabilized with different blends and contents of GGBS, MgO, lime, and PC. J Mater Civil Eng 26(2):267–274

    Article  Google Scholar 

  56. Yi Y, Liska M, Al-Tabbaa A (2012) Initial investigation into the use of GGBS-MgO in soil stabilisation. In: Grouting and deep mixing, pp 444–453

  57. Yin ZY, ** YF, Shen JS, Hicher PY (2018) Optimization techniques for identifying soil parameters in geotechnical engineering: comparative study and enhancement. Int J Numer Anal Methods 42(1):70–94

    Article  Google Scholar 

  58. Zhang LM, Tang WH, Zhang LL, Zheng JG (2004) Reducing uncertainty of prediction from empirical correlations. J Geotech Geoenviron Eng 130(5):526–534

    Article  Google Scholar 

  59. Zhang P, Yin ZY, ** YF, Liu XF (2021) Modelling the mechanical behaviour of soils using machine learning algorithms with explicit formulations. Acta Geotech. https://doi.org/10.1007/s11440-021-01170-4

    Article  Google Scholar 

  60. Zhang J, Zhang LM, Tang WH (2009) Bayesian framework for characterizing geotechnical model uncertainty. J Geotech Geoenviron Eng 135(7):932–940

    Article  Google Scholar 

  61. Zhao Z, Congress SSC, Cai G, Duan W (2021) Bayesian probabilistic characterization of consolidation behavior of clays using CPTU data. Acta Geotech. https://doi.org/10.1007/s11440-021-01277-8

    Article  Google Scholar 

  62. Zou H, Liu S, Cai G, Puppala AJ (2020) Probabilistic identification of contaminated soils using resistivity piezocone penetration tests. Acta Geotech 15(3):761–779

    Article  Google Scholar 

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Acknowledgements

The majority of the work presented in this paper is funded by the National Key R&D Program of China (Grant No. 2020YFC1807200) and the National Natural Science Foundation of China (Grant Nos. 41877231, 42072299).

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Authors and Affiliations

  1. Institute of Geotechnical Engineering, Southeast University, Nan**g, 211189, Jiangsu, China

    Meng Wu, Guojun Cai, Lulu Liu, Zhen Jiang & Cai** Wang

  2. School of Civil Engineering, Anhui Jianzhu University, No.292 Ziyun Road, Shushan District, Hefei, 230601, China

    Guojun Cai

  3. College of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China

    Zongjun Sun

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  1. Meng Wu
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  2. Guojun Cai
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Correspondence to Guojun Cai.

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Wu, M., Cai, G., Liu, L. et al. Quantitative identification of cutoff wall construction defects using Bayesian approach based on excess pore water pressure. Acta Geotech. 17, 2553–2571 (2022). https://doi.org/10.1007/s11440-021-01414-3

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