Compressibility indices of saturated clays by group method of data handling and genetic algorithms

Neural Computing and Applications

Volumn 28, Issue , 2017, Pages 551-564

  Moayed, Reza Ziaie ^a   Kordnaeij, Afshin ^a   Mola Abasi, Hossein ^b  

^a IMAM KHOMEINI INTERNATIONAL UNIVERSITY   (Iran)

^b BABOL NOSHIRVANI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Clay; Compression index; Genetic algorithms; Neural network; Oedometer test

Indexed keywords

ALGORITHMS; CLAY; COMPRESSIBILITY; CONSOLIDATION; EXPERIMENTAL REACTORS; GENETIC ALGORITHMS; NEURAL NETWORKS; SENSITIVITY ANALYSIS; SOIL MECHANICS;

COMPRESSIBILITY INDEX; COMPRESSION INDEX; CONSOLIDATION SETTLEMENT; GROUP METHOD OF DATA HANDLING; INITIAL VOID RATIOS; OEDOMETER TESTS; RECOMPRESSION INDEX; TRAINING AND TESTING;

DATA HANDLING;

EID: 84973118023     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-016-2390-9     Document Type: Article

References (54)

1. Skempton AW, Sowa VA (1963) The behavior of saturated clays during sampling and testing. Geotechnique 14(4):269–290. doi:10.1680/geot.1963.13.4.269
2. Griffiths DV, Fenton GA (2009) Probabilistic settlement analysis by stochastic and random finite-element methods. J Geotech Geoenviron 135(11):1629–1637. doi:10.1061/(ASCE)GT.1943-5606.0000126
3. Fenton GA, Griffiths DV (2002) Probabilistic foundation settlement on spatially random soil. J Geotech Geoenviron 128(5):381–390. doi:10.1061/(ASCE)1090-0241(2002)128:5(381)
4. Nova R, Montrasio L (1991) Settlements of shallow foundations on sand. Géotechnique 41(2):243–256. doi:10.1680/geot.1991.41.2.243
5. Park HI, Lee SR (2011) Evaluation of the compression index of soils using an artificial neural network. Comput Geotech 38(4):472–481. doi:10.1016/j.compgeo.2011.02.011
6. c) and primary consolidation settlement. Geo-congress, ASCE pp 2134–2143. doi:10.1061/9780784413272.208
7. Gunduz Z, Arman H (2007) Possible relationships between compression and recompression indices of a low-plasticity clayey soil. Arab J Sci Eng 32(2):179–189
8. Tiwari B, Ajmera B (2012) New correlation equations for compression index of remolded clays. J Geotech Geoenviron 138(6):757–762. doi:10.1061/(ASCE)GT.1943-5606.0000639
9. Achintya KNP (2013) Correlation between compression index of silt–clay matrices and their index properties. J Inst Eng India Ser A 94(1):9–14. doi:10.1007/s40030-013-0044-9
10. Akayuli CFA, Ofosu B (2013) Empirical model for estimating compression index from physical properties of weathered birimian phyllites. Electron J Geotech Eng 18:6135–6144
11. Yoon GL, Kim BT (2006) Regression analysis of compression index for Kwangyang marine clay. KSCE J Civ Eng 10(6):415–418. doi:10.1007/BF02823980
12. Ozer M, Isik NS, Orhan M (2008) Statistical and neural network assessment of the compression index of clay-bearing soils. Bull Eng Geol Environ 67(4):537–545. doi:10.1007/s10064-008-0168-8
13. Nakase A, Kamei T, Kusakabe O (1988) Constitutive parameters estimated by plasticity index. J Geotech Eng 114(7):844–858. doi:10.1061/(ASCE)0733-9410(1988)114:7(844)
14. Isik NS (2009) Estimation of swell index of fine grained soils using regression equations and artificial neural networks. Sci Res Essays 4(10):1047–1056
15. Nagaraj TS, Murty BRS (1985) Prediction of the pre-consolidation pressure and recompression index of soils. Geotech Test J 8(4):199–202. doi:10.1520/GTJ10538J
16. Aladag CH, Kayabasi A, Gokceoglu C (2013) Estimation of pressuremeter modulus and limit pressure of clayey soils by various artificial neural network models. Neural Comput Appl 23(2):333–339. doi:10.1007/s00521-012-0900-y
17. Erzin Y, Cetin T (2012) The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces. Sci Iran Trans A Civ Eng 19(2):188–194. doi:10.1016/j.scient.2012.02.008
18. Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33. doi:10.1016/j.compgeo.2011.09.008
19. Yilmaz I, Marschalko M, Bednarik M, Kaynar O, Fojtova L (2012) Neural computing models for prediction of permeability coefficient of coarse-grained soils. Neural Comput Appl 21(5):957–968. doi:10.1007/s00521-011-0535-4
20. Erzin Y, Cetin T (2013) The prediction of the critical factor of safety of homogeneous slopes using neural networks and multiple regressions. Comput Geosci 51:305–313. doi:10.1016/j.cageo.2012.09.003
21. Erzin Y, Gul TO (2014) The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test. Neural Comput Appl 24(3–4):891–900. doi:10.1007/s00521-012-1302-x
22. Ikizler SB, Vekli M, Dogan E, Aytekin M, Kocabas F (2014) Prediction of swelling pressures of expansive soils using soft computing methods. Neural Comput Appl 24(2):473–485. doi:10.1007/s00521-012-1254-1
23. Tinoco J, Correia AG, Cortez P (2014) Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Comput Geotech 55:132–140. doi:10.1016/j.compgeo.2013.08.010
24. Erzin Y, Turkoz D (2015) Use of neural networks for the prediction of the CBR value of some Aegean sands. Neural Comput Appl. doi:10.1007/s00521-015-1943-7
25. Yadollahi MM, Benli A, Demirboga R (2016) Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites. Neural Comput Appl. doi:10.1007/s00521-015-2159-6
26. Hoang ND, Tien Bui D (2016) A novel relevance vector machine classifier with cuckoo search optimization for spatial prediction of landslides. J Comput Civ Eng. doi:10.1061/(ASCE)CP.1943-5487.0000557
27. Kolay PK, Rosmina AB, Ling NW (2008) Settlement prediction of tropical soft soil by artificial neural network (ANN). In: The 12th international conference of international association for computer methods and advances in geomechanics (IACMAG), Goa, India, pp 1843–1849
28. Desai VGM, Desai V, Rao DH (2009) Prediction of compression index using artificial neural networks. In: Indian geotechnical conference (IGC-2009), Guntur, India, pp 614–617
29. Farkhonde S, Bolouri J (2010) Estimation of compression index of clayey soils using artificial neural network. In: Fifth national conference on civil engineering, Mashhad, Iran, Paper ID, p 1151
30. Daryaei M, Kashefipour SM, Ahadian J, Ghobadian R (2010) Modeling the compression index of fine soils using artificial neural network and comparison with the other empirical equations. J Water Soil 24(4):659–667
31. Jianping J, Yangsong Z, Changhong Y, Guangyun G (2010) Application of BP neural network in prediction of compression index of soil. J Cent South Univ 41(2):722–727
32. Kumar VP, Rani CHS (2011) Prediction of compression index of soils using artificial neural networks (ANNs). Int J Eng Res Appl 1(4):1554–1558
33. Kumar Y, Venkatesh K, Kumar V (2012) Prediction of compression index of cohesive soil using neural network approach. Proc Int Conf Adv Archit Civ Eng 1:363–366
34. Kalantary F, Kordnaeij A (2012) Prediction of compression index using artificial neural network. Sci Res Essays 7(31):2835–2848. doi:10.5897/SRE12.297
35. Rani CHS, Kumar VP, Togati VK (2013) Artificial neural networks (ANNS) for prediction of engineering properties of soils. Int J Innov Technol Explor Eng 3(1):123–130
36. Kashefipour SM, Daryaee M (2014) Modeling the compression index for fine soils using an intelligent method. J Biodivers Environ Sci 5(5):197–204
37. Shi XC, Gao YF (2013) Application of genetic arithmetic and support vector machine in prediction of compression index of clay. Appl Mech Mater 438:1167–1170. doi:10.4028/www.www.scientific.net/AMM.438-439.1167
38. Mohammadzadeh SD, Bolouri Bazaz J, Alavi AM (2014) An evolutionary computational approach for formulation of compression index of fine-grained soils. Eng Appl Artif Intell 33:58–68. doi:10.1016/j.engappai.2014.03.012
39. Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern SMC 1:364–378. doi:10.1109/TSMC.1971.4308320
40. Mola-Abasi H, Eslami A, Tabatabaeishorijeh P (2013) Shear wave velocity by polynomial neural networks and genetic algorithms based on geotechnical soil properties. Arab J Sci Eng 38(4):829–838. doi:10.1007/s13369-012-0525-6
41. Kalantary F, Ardalan H, Nariman-Zadeh N (2009) An investigation on the Su–NSPT correlation using GMDH type neural networks and genetic algorithms. Eng Geol 104(1):144–155. doi:10.1016/j.enggeo.2008.09.006
42. Ardalan H, Eslami H, Nariman-Zadeh N (2009) Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms. Comput Geotech 36(4):616–625. doi:10.1016/j.compgeo.2008.09.003
43. Eslami A, Mola-Abasi H, Tabatabaeishorijeh P (2014) A polynomial model for liquefaction potential prediction from CPT data. Sci Iran 21(1):44–52
44. Mola-Abasi H, Dikmen U, Shooshpasha I (2015) Prediction of shear-wave velocity from CPT data at Eskisehir (Turkey), using a polynomial model. Near Surf Geophys 13(2):155–167. doi:10.3997/1873-0604.2015010
45. Farlow SJ (1984) Self-organizing method in modelling: GMDH type algorithm. Marcel Dekker Inc., New York
46. Nariman-Zadeh N, Darvize A, Ahmad-Zadeh GR (2003) Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. Proc Inst Mech Eng B J Eng Manuf 217(6):779–790. doi:10.1243/09544050360673161
47. Atashkari K, Nariman-Zadeh N, Golcü M, Khalkhali A, Jamali A (2007) Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms. Energy Convers Manag 48(3):1029–1041. doi:10.1016/j.enconman.2006.07.007
48. Jamali A, Nariman-zadeh N, Darvizeh A, Masoumi A, Hamrang S (2009) Multi-objective Evolutionary Optimization Of Polynomial Neural Networks For Modelling And Prediction Of Explosive Cutting Process. Eng Appl Artif Intell 22(4):676–687. doi:10.1016/j.engappai.2008.11.005
49. Dorn M, Braga AL, Llanos CH, Coelho LS (2012) A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides. Expert Syst Appl 39(15):12268–12279. doi:10.1016/j.eswa.2012.04.046
50. ASTM D 2435 (2011) Standard test method for one-dimensional consolidation properties of soils. Annu Book ASTM Stand 04:08
51. Erzin Y, Rao BH, Singh DN (2008) Artificial neural network models for predicting soil thermal resistivity. Int J Therm Sci 47(10):1347–1358. doi:10.1016/j.ijthermalsci.2007.11.001
52. Erzin Y, Gumaste D, Gupta AK, Singh DN (2009) Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine grained soils. Can Geotech J 46(8):955–968. doi:10.1139/T09-035
53. Das BM (2002) Principles of geotechnical engineering, 5th edn. Brooks/Cole Thomson Learning, Pacific Grove
54. Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131. doi:10.1016/j.measurement.2014.08.007