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An intelligent multi-objective EPR technique with multi-step model selection for correlations of soil properties

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Abstract

Current multi-objective evolutionary polynomial regression (EPR) methodology has difficulties on decision-making of optimal EPR model. This paper proposes an intelligent multi-objective optimization-based EPR technique with multi-step automatic model selection procedure. A newly developed multi-objective differential evolution algorithm (MODE) is adopted to improve the optimization performance. The proposed EPR process is composed of two stages: (1) intelligent roughing model selection and (2) model delicacy identification. In the first stage, besides two objectives (model accuracy and model complexity), the model robustness measured by robustness ratio is considered as an additional objective in the multi-objective optimization. In the second stage, a new indicator named selection index is proposed and incorporated to find the optimal model. After intelligent roughing selection and delicacy identification, the optimal EPR model is obtained considering the combined effects of correlation coefficient, size of polynomial terms, number of involved variables, robustness ratio and monotonicity. To show the practicality of the proposed EPR technique, three illustrative cases helpful for geotechnical design are presented: (a) modelling of compressibility, (b) modelling of undrained shear strength and (c) modelling of hydraulic conductivity. For each case, a practical formula with better performance in comparison with various existing empirical equations is finally provided. All results demonstrate that the proposed intelligent MODE-based EPR technique is efficient and effective.

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References

  1. Abdul Ghani N, Shahin M, Nikraz H (2012) Use of evolutionary polynomial regression (EPR) for prediction of total sediment load of Malaysian rivers. Int J Eng 6(5):262–277

    Google Scholar 

  2. Ahangar-Asr A, Faramarzi A, Javadi AA (2010) A new approach for prediction of the stability of soil and rock slopes. Eng Comput 27(7):878–893

    MATH  Google Scholar 

  3. Ahangar-Asr A, Javadi AA, Johari A, Chen Y (2014) Lateral load bearing capacity modelling of piles in cohesive soils in undrained conditions: an intelligent evolutionary approach. Appl Soft Comput 24:822–828

    Google Scholar 

  4. Al-Tabbaa A, Wood DM (1987) Some measurements of the permeability of kaolin. Géotechnique 37(4):499–514. https://doi.org/10.1680/geot.1987.37.4.499

    Article  Google Scholar 

  5. Basheer IA (2000) Selection of methodology for neural network modeling of constitutive hystereses behavior of soils. Comput-Aided Civ Inf 15:440–458

    Google Scholar 

  6. Benson CH, Trast JM (1995) Hydraulic conductivity of thirteen compacted clays. Clays Clay Miner 43(6):669–681

    Google Scholar 

  7. Berilgen SA, Berilgen MM, Ozaydin İK (2006) Compression and permeability relationships in high water content clays. Appl Clay Sci 31(3):249–261

    Google Scholar 

  8. Biarez J, Hicher P-Y (1994) Elementary mechanics of soil behaviour: saturated remoulded soils. AA Balkema, Rotterdam

    Google Scholar 

  9. Bolt G (1956) Physico-chemical analysis of the compressibility of pure clays. Geotechnique 6(2):86–93

    Google Scholar 

  10. Burland J (1990) On the compressibility and shear strength of natural clays. Geotechnique 40(3):329–378

    Google Scholar 

  11. Cabalar AF, Cevik A (2011) Triaxial behavior of sand–mica mixtures using genetic programming. Expert Syst Appl 38(8):10358–10367

    Google Scholar 

  12. Cao S, Song W, Yilmaz E (2018) Influence of structural factors on uniaxial compressive strength of cemented tailings backfill. Constr Build Mater 174:190–201

    Google Scholar 

  13. Carrier N III (1985) Consolidation parameters derived from index tests. Geotechnique 35(2):211–213

    MathSciNet  Google Scholar 

  14. Carrier WD, Beckman JF (1984) Correlations between index tests and the properties of remoulded clays. Géotechnique 34(34):211–228

    Google Scholar 

  15. Cerato AB, Lutenegger AJ (2004) Determining intrinsic compressibility of fine-grained soils. J Geotech Geoenviron 130(8):872–877

    Google Scholar 

  16. Chandler RJ (1988) The in situ measurement of the undrained shear strength of clays using the field vane. In: Richards A (ed) Vane shear strength testing in soils: field and laboratory studies. ASTM International, Conshohocken

    Google Scholar 

  17. Chapuis RP (2012) Predicting the saturated hydraulic conductivity of soils: a review. Bull Eng Geol Environ 71(3):401–434. https://doi.org/10.1007/s10064-012-0418-7

    Article  Google Scholar 

  18. Chen WB, Yin JH, Feng WQ, Borana L, Chen RP (2018) Accumulated permanent axial strain of a subgrade fill under cyclic high-speed railway loading. Int J Geomech 18(5):04018018. https://doi.org/10.1061/(asce)gm.1943-5622.0001119

    Article  Google Scholar 

  19. Chen WB, Feng WQ, Yin JH, Borana L, Chen RP (2019) Characterization of permanent axial strain of granular materials subjected to cyclic loading based on shakedown theory. Constr Build Mater 198:751–761. https://doi.org/10.1016/j.conbuildmat.2018.12.012

    Article  Google Scholar 

  20. Chen R, Zhang P, Wu H, Wang Z, Zhong Z (2019) Prediction of shield tunneling-induced ground settlement using machine learning techniques. Front Struct Civ Eng 13(6):1363–1378

    Google Scholar 

  21. Chen RP, Zhang P, Kang X, Zhong ZQ, Liu Y, Wu HN (2019) Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods. Soils Found 59(2):284–295. https://doi.org/10.1016/j.sandf.2018.11.005

    Article  Google Scholar 

  22. Chen WB, Liu K, Yin ZY, Yin JH (2020) Crushing and flooding effects on one-dimensional time-dependent behaviors of a granular soil. Int J Geomech 20(2):04019156. https://doi.org/10.1061/(asce)gm.1943-5622.0001560

    Article  Google Scholar 

  23. Ching J, Phoon K-K (2012) Modeling parameters of structured clays as a multivariate normal distribution. Can Geotech J 49(5):522–545

    Google Scholar 

  24. Ching J, Phoon K-K (2014) Transformations and correlations among some clay parameters—the global database. Can Geotech J 51(6):663–685. https://doi.org/10.1139/cgj-2013-0262

    Article  Google Scholar 

  25. Ching J, Phoon K-K, Chen Y-C (2010) Reducing shear strength uncertainties in clays by multivariate correlations. Can Geotech J 47(1):16–33

    Google Scholar 

  26. Ching J, Phoon K-K, Chen C-H (2013) Modeling piezocone cone penetration (CPTU) parameters of clays as a multivariate normal distribution. Can Geotech J 51(1):77–91. https://doi.org/10.1139/cgj-2012-0259

    Article  Google Scholar 

  27. D’Ignazio M, Phoon K-K, Tan SA, Länsivaara TT (2016) Correlations for undrained shear strength of Finnish soft clays. Can Geotech J 53(10):1628–1645. https://doi.org/10.1139/cgj-2016-0037

    Article  Google Scholar 

  28. Dasaka SM, Zhang LM (2012) Spatial variability of in situ weathered soil. Géotechnique 62(5):375–384. https://doi.org/10.1680/geot.8.P.151.3786

    Article  Google Scholar 

  29. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197

    Google Scholar 

  30. Dolinar B (2009) Predicting the hydraulic conductivity of saturated clays using plasticity-value correlations. Appl Clay Sci 45(1–2):90–94

    Google Scholar 

  31. Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization. Comput Intell Mag IEEE 1(4):28–39

    Google Scholar 

  32. Ebrahimian B, Movahed V (2013) Evaluation of axial bearing capacity of piles in sandy soils by CPT results. Evaluation 29:31

    Google Scholar 

  33. Ebrahimian B, Movahed V (2017) Application of an evolutionary-based approach in evaluating pile bearing capacity using CPT results. Ships Offshore Struct 12(7):937–953

    Google Scholar 

  34. Faramarzi A, Javadi AA, Alani AM (2012) EPR-based material modelling of soils considering volume changes. Comput Geosci 48:73–85

    Google Scholar 

  35. Faramarzi A, Alani AM, Javadi AA (2014) An EPR-based self-learning approach to material modelling. Comput Struct 137:63–71

    Google Scholar 

  36. Favre J-L, Hattab M (2008) Analysis of the ‘Biarez–Favre’ and ‘Burland’ models for the compressibility of remoulded clays. CR Geosci 340(1):20–27

    Google Scholar 

  37. Gamse S, Zhou W-H, Tan F, Yuen K-V, Oberguggenberger M (2018) Hydrostatic-season-time model updating using Bayesian model class selection. Reliab Eng Syst Saf 169:40–50

    Google Scholar 

  38. Ghorbani A, Firouzi Niavol M (2017) Evaluation of induced settlements of piled rafts in the coupled static-dynamic loads using neural networks and evolutionary polynomial regression. Appl Comput Intell Soft Comput. https://doi.org/10.1155/2017/7487438

    Article  Google Scholar 

  39. Giasi CI, Cherubini C, Paccapelo F (2003) Evaluation of compression index of remoulded clays by means of Atterberg limits. Bull Eng Geol Environ 62(4):333–340. https://doi.org/10.1007/s10064-003-0196-3

    Article  Google Scholar 

  40. Giustolisi O, Savic D (2006) A symbolic data-driven technique based on evolutionary polynomial regression. J Hydroinform 8(3):207–222

    Google Scholar 

  41. Giustolisi O, Savic D (2006) Evolutionary polynomial regression (EPR). http://www.hydroinformatics.it/. Accessed 23 Jan 2020

  42. Giustolisi O, Savic D (2009) Advances in data-driven analyses and modelling using EPR-MOGA. J Hydroinform 11(3–4):225–236

    Google Scholar 

  43. Goldberg DE, Corruble V, Ganascia J-G, Holland J (1994) Algorithmes génétiques: exploration, optimisation et apprentissage automatique. Addison-Wesley, France

    Google Scholar 

  44. Habibagahi G, Bamdad A (2003) A neural network framework for mechanical behavior of unsaturated soils. Can Geotech J 40(3):684–693

    Google Scholar 

  45. Habibbeygi F, Nikraz H, Koul BK, Iovine G (2018) Regression models for intrinsic constants of reconstituted clays. Cogent Geosci 4(1):1546978. https://doi.org/10.1080/23312041.2018.1546978

    Article  Google Scholar 

  46. Hanzawa H, Fukaya T, Suzuki K (1990) Evaluation of engineering properties for an Ariake clay. Soils Found 30(4):11–24

    Google Scholar 

  47. Hawkins A, Larnach W, Lloyd I, Nash D (1989) Selecting the location, and the initial investigation of the SERC soft clay test bed site. Q J Eng Geol Hydrogeol 22(4):281–316

    Google Scholar 

  48. He S, Li J (2009) Modeling nonlinear elastic behavior of reinforced soil using artificial neural networks. Appl Soft Comput 9(3):954–961. https://doi.org/10.1016/j.asoc.2008.11.013

    Article  Google Scholar 

  49. Hong Z, Yin J, Cui Y-J (2010) Compression behaviour of reconstituted soils at high initial water contents. Geotechnique 60(9):691–700

    Google Scholar 

  50. Hong Z-S, Zeng L-L, Cui Y-J, Cai Y-Q, Lin C (2012) Compression behaviour of natural and reconstituted clays. Geotechnique 62(4):291–301

    Google Scholar 

  51. Hong S-J, Kim D-H, Lee M-J, Jie H-K, Lee W-J (2013) Evaluation of compression index for natural clay using the compression characteristic of reconstituted clay. J Korean Geotech Soc 29(3):5–13

    Google Scholar 

  52. Horpibulsuk S, Shibuya S, Fuenkajorn K, Katkan W (2007) Assessment of engineering properties of Bangkok clay. Can Geotech J 44(2):173–187

    Google Scholar 

  53. Horpibulsuk S, Yangsukkaseam N, Chinkulkijniwat A, Du YJ (2011) Compressibility and permeability of Bangkok clay compared with kaolinite and bentonite. Appl Clay Sci 52(1):150–159

    Google Scholar 

  54. Jamiolkowski M (1985) New developments in field and laboratory testing or soils. In: Proceedings of 11th international conference on SMFE, San Francisco, CA, pp 57–153

  55. Javadi AA, Faramarzi A, Ahangar-Asr A (2012) Analysis of behaviour of soils under cyclic loading using EPR-based finite element method. Finite Elem Anal Des 58:53–65

    Google Scholar 

  56. Jiang G, Chen W, Liu X, Yuan S, Wu L, Zhang C (2018) Field study on swelling-shrinkage response of an expansive soil foundation under high-speed railway embankment loads. Soils Found 58(6):1538–1552

    Google Scholar 

  57. Jin Y-F, Yin Z-Y, Wu Z-X, Daouadji A (2018) Numerical modeling of pile penetration in silica sands considering the effect of grain breakage. Finite Elem Anal Des 144:15–29. https://doi.org/10.1016/j.finel.2018.02.003

    Article  Google Scholar 

  58. Jin Y-F, Yin Z-Y, Zhou W-H, Yin J-H, Shao J-F (2019) A single-objective EPR based model for creep index of soft clays considering L2 regularization. Eng Geol 248:242–255. https://doi.org/10.1016/j.enggeo.2018.12.006

    Article  Google Scholar 

  59. Jin Y-F, Yin Z-Y, Zhou W-H, Huang H-W (2019) Multi-objective optimization-based updating of predictions during excavation. Eng Appl Artif Intell 78:102–123. https://doi.org/10.1016/j.engappai.2018.11.002

    Article  Google Scholar 

  60. Jin Y-F, Yin Z-Y, Zhou W-H, Horpibulsuk S (2019) Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method. Acta Geotech 14(6):1925–1947. https://doi.org/10.1007/s11440-019-00847-1

    Article  Google Scholar 

  61. Jin Y-F, Yin Z-Y, Zhou W-H, Shao J-F (2019) Bayesian model selection for sand with generalization ability evaluation. Int J Numer Anal Methods Geomech 43(14):2305–2327. https://doi.org/10.1002/nag.2979

    Article  Google Scholar 

  62. Johari A, Javadi AA, Habibagahi G (2011) Modelling the mechanical behaviour of unsaturated soils using a genetic algorithm-based neural network. Comput Geotech 38(1):2–13. https://doi.org/10.1016/j.compgeo.2010.08.011

    Article  Google Scholar 

  63. Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652–657

    Google Scholar 

  64. Karlsrud K, Hernandez-Martinez FG (2013) Strength and deformation properties of Norwegian clays from laboratory tests on high-quality block samples. Can Geotech J 50(12):1273–1293

    Google Scholar 

  65. Khoshkroudi SS, Sefidkouhi MAG, Ahmadi MZ, Ramezani M (2014) Prediction of soil saturated water content using evolutionary polynomial regression (EPR). Arch Agron Soil Sci 60(8):1155–1172

    Google Scholar 

  66. Kim YT, Leroueil S (2001) Modeling the viscoplastic behaviour of clays during consolidation: application to Berthierville clay in both laboratory and field conditions. Can Geotech J 38(3):484–497

    Google Scholar 

  67. Kohestani VR, Hassanlourad M (2016) Modeling the mechanical behavior of carbonate sands using artificial neural networks and support vector machines. Int J Geomech 16(1):04015038. https://doi.org/10.1061/(ASCE)

    Article  Google Scholar 

  68. Kulhawy FH, Mayne PW (1990) Manual on estimating soil properties for foundation design. Electric Power Research Inst., Palo Alto, CA (USA); Cornell Univ., Ithaca

  69. Larsson R (1980) Undrained shear strength in stability calculation of embankments and foundations on soft clays. Can Geotech J 17(4):591–602

    Google Scholar 

  70. Larsson R, Larsson R (2007) Skjuvhållfasthet: utvärdering i kohesionsjord. Statens geotekniska institut (SGI) Linköping

  71. Leroueil S, Tavenas F, Samson L, Morin P (1983) Preconsolidation pressure of Champlain clays. Part II. Laboratory determination. Can Geotech J 20(4):803–816. https://doi.org/10.1139/t83-084

    Article  Google Scholar 

  72. Mesri G (1989) A reevaluation of using laboratory shear tests. Can Geotech J 26(1):162–164

    Google Scholar 

  73. Mishra AK, Ohtsubo M, Li L, Higashi T (2011) Controlling factors of the swelling of various bentonites and their correlations with the hydraulic conductivity of soil-bentonite mixtures. Appl Clay Sci 52(1):78–84. https://doi.org/10.1016/j.clay.2011.01.033

    Article  Google Scholar 

  74. Nagaraj T, Murthy BS (1983) Rationalization of Skempton’s compressibility equation. Geotechnique 33(4):433–443

    Google Scholar 

  75. Nagaraj T, Murthy BS (1986) A critical reappraisal of compression index equations. Geotechnique 36(1):27–32

    Google Scholar 

  76. Nagaraj TS, Pandian NS, Raju PSRN (1993) Stress state-permeability relationships for fine-grained soils. Géotechnique 43(2):333–336. https://doi.org/10.1680/geot.1993.43.2.333

    Article  Google Scholar 

  77. Nagaraj TS, Pandian NS, Raju PSRN (1994) Stress-state—permeability relations for overconsolidated clays. Géotechnique 44(2):349–352. https://doi.org/10.1680/geot.1994.44.2.349

    Article  Google Scholar 

  78. Najafzadeh M, Ghaemi A, Emamgholizadeh S (2019) Prediction of water quality parameters using evolutionary computing-based formulations. Int J Environ Sci Technol 16(10):6377–6396

    Google Scholar 

  79. Nassr A, Javadi A, Faramarzi A (2018) Developing constitutive models from EPR-based self-learning finite element analysis. Int J Numer Anal Methods Geomech 42(3):401–417

    Google Scholar 

  80. Nath A, DeDalal S (2004) The role of plasticity index in predicting compression behaviour of clays. Electron J Geotech Eng 9:1–7

    Google Scholar 

  81. Nishida Y, Nakagawa S (1969) Water permeability and plastic index of soils. In: Proceedings of IASH-UNESCO symposium, Tokyo, pp 573–578

  82. Park J, Koumoto T (2000) Compression characteristics of remolded clays. Transactions of the Japanese Society of Irrigation, Drainage and Reclamation Engineering (Japan)

  83. Penumadu D, Zhao RD (1999) Triaxial compression behavior of sand and gravel using artificial neural networks (ANN). Comput Geotech 24:207–230

    Google Scholar 

  84. Prakash K, Sridharan A (2002) Determination of liquid limit from equilibrium sediment volume. Géotechnique 52(9):693–696. https://doi.org/10.1680/geot.2002.52.9.693

    Article  Google Scholar 

  85. Qi X-H, Zhou W-H (2017) An efficient probabilistic back-analysis method for braced excavations using wall deflection data at multiple points. Comput Geotech 85:186–198

    Google Scholar 

  86. Qi Y, Hou Z, Yin M, Sun H, Huang J (2015) An immune multi-objective optimization algorithm with differential evolution inspired recombination. Appl Soft Comput 29:395–410

    Google Scholar 

  87. Rashidian V, Hassanlourad M (2014) Application of an artificial neural network for modeling the mechanical behavior of carbonate soils. Int J Geomech 14(1):142–150. https://doi.org/10.1061/(asce)gm.1943-5622.0000299

    Article  Google Scholar 

  88. Rezaie-Balf M, Kisi O (2017) New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine. Hydrol Res 49(3):nh2017283. https://doi.org/10.2166/nh.2017.283

    Article  Google Scholar 

  89. Rezania M, Javadi AA, Giustolisi O (2010) Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression. Comput Geotech 37(1):82–92

    Google Scholar 

  90. Rezania M, Faramarzi A, Javadi AA (2011) An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement. Eng Appl Artif Intell 24(1):142–153

    Google Scholar 

  91. Savic D, Giustolisi O, Laucelli D (2009) Asset deterioration analysis using multi-utility data and multi-objective data mining. J Hydroinform 11(3–4):211–224

    Google Scholar 

  92. Shahin MA (2014) State-of-the-art review of some artificial intelligence applications in pile foundations. Geosci Front 7(1):33–44. https://doi.org/10.1016/j.gsf.2014.10.002

    Article  Google Scholar 

  93. Shahnazari H, Tutunchian MA, Rezvani R, Valizadeh F (2013) Evolutionary-based approaches for determining the deviatoric stress of calcareous sands. Comput Geosci 50:84–94

    Google Scholar 

  94. Shahnazari H, Shahin MA, Tutunchian MA (2014) Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils. Geotech Eng 12(1):55–64

    Google Scholar 

  95. Shen SL, Xu YS (2011) Numerical evaluation of land subsidence induced by groundwater pumping in Shanghai. Can Geotech J 48(9):1378–1392

    Google Scholar 

  96. Shen S-L, Wang J-P, Wu H-N, Xu Y-S, Ye G-L, Yin Z-Y (2015) Evaluation of hydraulic conductivity for both marine and deltaic deposits based on piezocone testing. Ocean Eng 110:174–182. https://doi.org/10.1016/j.oceaneng.2015.10.011

    Article  Google Scholar 

  97. Shen S-L, Wu Y-X, Misra A (2017) Calculation of head difference at two sides of a cut-off barrier during excavation dewatering. Comput Geotech 91:192–202

    Google Scholar 

  98. Sinha SK, Wang MC (2008) Artificial neural network prediction models for soil compaction and permeability. Geotech Geol Eng 26(1):47–64. https://doi.org/10.1007/s10706-007-9146-3

    Article  Google Scholar 

  99. Sivapullaiah PV, Sridharan A, Stalin VK (2000) Hydraulic conductivity of bentonite-sand mixtures. Can Geotech J 37(2):406–413. https://doi.org/10.1139/t99-120

    Article  Google Scholar 

  100. Sivapullaiah PV, Sridharan A, Stalin VK (2000) Hydraulic conductivity of bentonite-sand mixtures. Can Geotech J 37(37):406–413

    Google Scholar 

  101. Skempton A (1954) Discussion of the structure of inorganic soil. J Am Soc Civ Eng 80(478):19–22

    Google Scholar 

  102. Skempton AW, Jones O (1944) Notes on the compressibility of clays. Q J Geol Soc 100(1–4):119–135

    Google Scholar 

  103. Sridharan A, Nagaraj H (2000) Compressibility behaviour of remoulded, fine-grained soils and correlation with index properties. Can Geotech J 37(3):712–722

    Google Scholar 

  104. Sridharan A, Nagaraj HB (2005) Hydraulic conductivity of remolded fine-grained soils versus index properties. Geotech Geol Eng 23(1):43

    Google Scholar 

  105. Tan F, Zhou W-H, Yuen K-V (2016) Modeling the soil water retention properties of same-textured soils with different initial void ratios. J Hydrol 542:731–743

    Google Scholar 

  106. Tan F, Zhou WH, Yuen KV (2018) Effect of loading duration on uncertainty in creep analysis of clay. Int J Numer Anal Methods Geomech 42(11):1235–1254

    Google Scholar 

  107. Tanaka H, Locat J, Shibuya S, Soon TT, Shiwakoti DR (2001) Characterization of Singapore, Bangkok, and Ariake clays. Can Geotech J 38(2):378–400. https://doi.org/10.1139/t00-106

    Article  Google Scholar 

  108. Tavenas F, Leroueil S, La Rochelle P, Roy M (1978) Creep behaviour of an undisturbed lightly overconsolidated clay. Can Geotech J 15(3):402–423

    Google Scholar 

  109. Taylor DW (1948) Fundamentals of soil mechanics. Soil Sci 66(2):161

    Google Scholar 

  110. Tiwari B, Ajmera B (2011) New correlation equations for compression index of remolded clays. J Geotech Geoenviron Eng 138(6):757–762

    Google Scholar 

  111. Tiwari B, Ajmera B (2012) New correlation equations for compression index of remolded clays. J Geotech Geoenviron 138(6):757–762. https://doi.org/10.1061/(asce)gt.1943-5606.0000639

    Article  Google Scholar 

  112. Turk G, Logar J, Majes B (2001) Modelling soil behaviour in uniaxial strain conditions by neural networks. Adv Eng Softw 32:805–812

    MATH  Google Scholar 

  113. Vassallo R, Doglioni A, Grimaldi G, Di Maio C, Simeone V (2016) Relationships between rain and displacements of an active earthflow: a data-driven approach by EPRMOGA. Nat Hazards. https://doi.org/10.1007/s11069-11015-12140-11069

    Article  Google Scholar 

  114. Wood DM (2003) Geotechnical modelling, vol 1. CRC Press, Boca Raton

    Google Scholar 

  115. Wroth C, Wood D (1978) The correlation of index properties with some basic engineering properties of soils. Can Geotech J 15(2):137–145

    Google Scholar 

  116. Wu Y-X, Shen S-L, Yuan D-J (2016) Characteristics of dewatering induced drawdown curve under blocking effect of retaining wall in aquifer. J Hydrol 539:554–566

    Google Scholar 

  117. Wu H-N, Shen S-L, Yang J (2017) Identification of tunnel settlement caused by land subsidence in soft deposit of Shanghai. J Perform Constr Facil 31(6):04017092

    Google Scholar 

  118. Wu Z-x, Ji H, Yu C, Zhou C (2018) EPR-RCGA-based modelling of compression index and RMSE-AIC-BIC-based model selection for Chinese marine clays and their engineering application. J Zhejiang Univ-Sci A 19(3):211–224

    Google Scholar 

  119. Yao Y, Zhou A (2013) Non-isothermal unified hardening model: a thermo-elasto-plastic model for clays. Géotechnique 63(15):1328

    Google Scholar 

  120. Yao Y, Sun D, Luo T (2004) A critical state model for sands dependent on stress and density. Int J Numer Anal Methods Geomech 28(4):323–337

    MATH  Google Scholar 

  121. Yao Y, Sun D, Matsuoka H (2008) A unified constitutive model for both clay and sand with hardening parameter independent on stress path. Comput Geotech 35(2):210–222

    Google Scholar 

  122. Yao Y-P, Yamamoto H, Wang N-D (2008) Constitutive model considering sand crushing. Soils Found 48(4):603–608

    Google Scholar 

  123. Yao Y, Hou W, Zhou A (2009) UH model: three-dimensional unified hardening model for overconsolidated clays. Geotechnique 59(5):451–469

    Google Scholar 

  124. Yao Y, Kong L, Hu J (2013) An elastic–viscous–plastic model for overconsolidated clays. Sci China Technol Sci 56(2):441–457

    Google Scholar 

  125. Yao Y-P, Kong L-M, Zhou A-N, Yin J-H (2014) Time-dependent unified hardening model: three-dimensional elastoviscoplastic constitutive model for clays. J Eng Mech 141(6):04014162

    Google Scholar 

  126. Yin Z-Y, Jin Y-F, Huang H-W, Shen S-L (2016) Evolutionary polynomial regression based modelling of clay compressibility using an enhanced hybrid real-coded genetic algorithm. Eng Geol 210:158–167

    Google Scholar 

  127. Yin Z-Y, Jin Y-F, Shen JS, Hicher P-Y (2018) Optimization techniques for identifying soil parameters in geotechnical engineering: comparative study and enhancement. Int J Numer Anal Methods Geomech 42(1):70–94. https://doi.org/10.1002/nag.2714

    Article  Google Scholar 

  128. Young P, Parkinson S, Lees M (1996) Simplicity out of complexity in environmental modelling: Occam’s razor revisited. J Appl Stat 23(2–3):165–210

    Google Scholar 

  129. Yuan S, Liu X, Buzzi O (2018) Effects of soil structure on the permeability of saturated Maryland clay. Géotechnique 69(1):72–78

    Google Scholar 

  130. Zeng L-L, Hong Z-S, Cai Y-Q, Han J (2011) Change of hydraulic conductivity during compression of undisturbed and remolded clays. Appl Clay Sci 51(1):86–93. https://doi.org/10.1016/j.clay.2010.11.005

    Article  Google Scholar 

  131. Zeng L-l, Hong Z-s, Chen F-q (2012) A law of change in permeability coefficient during compression of remolded clays. Rock Soil Mech 5:001 (in Chinese)

    Google Scholar 

  132. Zhang P, Chen RP, Wu HN (2019) Real-time analysis and regulation of EPB shield steering using Random Forest. Autom Constr 106:102860. https://doi.org/10.1016/j.autcon.2019.102860

    Article  Google Scholar 

  133. Zhang P, Yin Z-Y, Jin Y-F, Chan TH (2020) A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest. Eng Geol 265:105328

    Google Scholar 

  134. Zhou W-H, Yuen K-V, Tan F (2012) Estimation of maximum pullout shear stress of grouted soil nails using Bayesian probabilistic approach. Int J Geomech 13(5):659–664

    Google Scholar 

  135. Zhou C, Yin K, Cao Y, Ahmed B (2016) Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Eng Geol 204:108–120. https://doi.org/10.1016/j.enggeo.2016.02.009

    Article  Google Scholar 

  136. Zhou W-H, Tan F, Yuen K-V (2018) Model updating and uncertainty analysis for creep behavior of soft soil. Comput Geotech 100:135–143. https://doi.org/10.1016/j.compgeo.2018.04.006

    Article  Google Scholar 

  137. Zhu Q-Y, Jin Y-F, Yin Z-Y, Hicher P-Y (2013) Influence of natural deposition plane orientation on oedometric consolidation behavior of three typical clays from southeast coast of China. J Zhejiang Univ Sci A 14(11):767–777

    Google Scholar 

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Acknowledgements

This research was financially supported by a RIF project (Grant Nos. 15209119, PolyU R5037-18F) from Research Grants Council (RGC) of Hong Kong Special Administrative Region Government (HKSARG) of China.

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  1. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Yin-Fu Jin & Zhen-Yu Yin

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Jin, YF., Yin, ZY. An intelligent multi-objective EPR technique with multi-step model selection for correlations of soil properties. Acta Geotech. 15, 2053–2073 (2020). https://doi.org/10.1007/s11440-020-00929-5

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