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A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

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Abstract

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

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References

  1. 1.

    Lee SJ, Lee SR, Kim YS (2003) An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation. Comput Geotech 30(6):489–503

  2. 2.

    Pujitha AK, Sivaswamy J (2018) Solution to overcome the sparsity issue of annotated data in medical domain. CAAI Trans Intell Technol 3(3):153–160

  3. 3.

    Adeli H (2001) Neural networks in civil engineering: 1989–2000. Comput-Aided Civi Infrastruct Eng 16(2):126–142

  4. 4.

    Panwar P, Michael P (2018) Empirical modelling of hydraulic pumps and motors based upon the Latin hypercube sampling method. Int J Hydromechatron 1(3):272–292

  5. 5.

    Gao W, Wang W, Dimitrov D, Wang Y (2018) Nano properties analysis via fourth multiplicative ABC indicator calculating. Arab J Chem 11(6):793–801

  6. 6.

    Zhang RL, Lowndes IS (2010) The application of a coupled artificial neural network and fault tree analysis model to predict coal and gas outbursts. Int J Coal Geol 84(2):141–152

  7. 7.

    Moayedi H, Huat B, Thamer A, Torabihaghighi A, Asadi A (2010) Analysis of longitudinal cracks in crest of Doroodzan Dam. Electron J Geotech Eng, USA (15D):337–347

  8. 8.

    Shahin MA, Jaksa MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Aust Geomech 36(1):49–62

  9. 9.

    Johnson JL (2018) Design of experiments and progressively sequenced regression are combined to achieve minimum data sample size. Int J Hydromechatron 1(3):308–331

  10. 10.

    Zhou Y, Sun Q, Liu J (2018) Robust optimisation algorithm for the measurement matrix in compressed sensing. CAAI Trans Intell Technol 3(3):133–139

  11. 11.

    Kostic S, Vasovic N, Todorovic K, Samcovic A (2016) Application of artificial neural networks for slope stability analysis in geotechnical practice. In: 2016 13th Symposium on neural networks and applications (neural) pp 89–94

  12. 12.

    Wang S-C (2003) Artificial neural network, interdisciplinary computing in java programming. Springer, Berlin, pp 81–100

  13. 13.

    Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2(4):311–319

  14. 14.

    Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems. Neural Comput Appl 21(1):189–201

  15. 15.

    Mukhlisin M, El-Shafie A, Taha MR (2012) Regularized versus non-regularized neural network model for prediction of saturated soil-water content on weathered granite soil formation. Neural Comput Appl 21(3):543–553

  16. 16.

    Lian C, Zeng ZG, Yao W, Tang HM (2014) Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis. Neural Comput Appl 24(1):99–107

  17. 17.

    Salsani A, Daneshian J, Shariati S, Yazdani-Chamzini A, Taheri M (2014) Predicting roadheader performance by using artificial neural network. Neural Comput Appl 24(7–8):1823–1831

  18. 18.

    Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181

  19. 19.

    Mert E (2014) An artificial neural network approach to assess the weathering properties of sancaktepe granite. Geotech Geol Eng 32(4):1109–1121

  20. 20.

    Moayedi H, Rezaei A (2017) An artificial neural network approach for under reamed piles subjected to uplift forces in dry sand. Neural Comput Appl 28:1–10

  21. 21.

    Shu SX, Gong WH (2016) An artificial neural network-based response surface method for reliability analyses of c-phi slopes with spatially variable soil. China Ocean Eng 30(1):113–122

  22. 22.

    Dong C, Dong XC, Gehman J, Lefsrud L (2017) Using BP neural networks to prioritize risk management approaches for China’s unconventional shale gas industry. Sustainability 9(6):18

  23. 23.

    Adams MD, Kanaroglou PS (2016) Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models. J Environ Manag 168:133–141

  24. 24.

    Lisboa PJG (2002) A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 15(1):11–39

  25. 25.

    Egmont-Petersen M, de Ridder D, Handels H (2002) Image processing with neural networks—a review. Pattern Recognit 35(10):2279–2301

  26. 26.

    Ayyildiz M, Cetinkaya K (2017) Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network. Proc Inst Mech Eng Part E-J Process Mech Eng 231(6):1206–1216

  27. 27.

    Gao W, Dimitrov D, Abdo H (2018) Tight independent set neighborhood union condition for fractional critical deleted graphs and ID deleted graphs. Discrete Contin Dyn Syst-S 12(4&5):711–721

  28. 28.

    Gao W, Guirao JLG, Basavanagoud B, Wu J (2018) Partial multi-dividing ontology learning algorithm. Inf Sci 467:35–58

  29. 29.

    Gao W, Guirao JLG, Abdel-Aty M, Xi W (2019) An independent set degree condition for fractional critical deleted graphs. Discret Contin Dyn Syst-S 12(4&5):877–886

  30. 30.

    Gao W, Wu H, Siddiqui MK, Baig AQ (2018) Study of biological networks using graph theory. Saudi J Biol Sci 25(6):1212–1219

  31. 31.

    Chou J-S, Thedja JPP (2016) Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems. Autom Constr 68:65–80

  32. 32.

    Lary DJ, Alavi AH, Gandomi AH, Walker AL (2016) Machine learning in geosciences and remote sensing. Geosci Front 7(1):3–10

  33. 33.

    Wong BK, Bodnovich TA, Selvi Y (1997) Neural network applications in business: a review and analysis of the literature (1988–1995). Decis Support Syst 19(4):301–320

  34. 34.

    Lazarevska M, Knezevic M, Cvetkovska M, Trombeva-Gavriloska A (2014) Application of artificial neural networks in civil engineering. Teh Vjesn 21(6):1353–1359

  35. 35.

    Chen JJ, Zeng ZG, Jiang P, Tang HM (2016) Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction. Neural Comput Appl 27(6):1771–1784

  36. 36.

    Zhang ZF, Liu ZB, Zheng LF, Zhang Y (2014) Development of an adaptive relevance vector machine approach for slope stability inference. Neural Comput Appl 25(7–8):2025–2035

  37. 37.

    Chou JS, Thedja JPP (2016) Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems. Autom Constr 68:65–80

  38. 38.

    Flood I, Kartam N (1994) Neural networks in civil engineering.1. principles and understanding. J Comput Civ Eng 8(2):131–148

  39. 39.

    Flood I, Kartam N (1994) Neural networks in civil engineering.2. systems and application. J Comput Civ Eng 8(2):149–162

  40. 40.

    Lu PZ, Chen SY, Zheng YJ (2012) Artificial intelligence in civil engineering. Math Probl Eng 145974:1–22

  41. 41.

    Li J, Hao H (2016) A review of recent research advances on structural health monitoring in Western Australia. Struct Monit Maint 3(1):33–49

  42. 42.

    Bolt G (1991) Fault models for artificial neural networks. IEEE, Piscataway

  43. 43.

    Lee C, Sterling R (1992) Identifying probable failure modes for underground openings using a neural network. Int J Rock Mech Min Sci 29(1):49–67

  44. 44.

    Goh ATC, Wong KS, Broms BB (1995) Estimation of lateral wall movements in braced excavations using neural networks. Can Geotech J 32(6):1059–1064

  45. 45.

    Watson JN, Fairfield CA, Wan C, Sibbald A (1995) The use of artificial neural networks in pile integrity testing. Civil Comp Press, Edinburgh

  46. 46.

    Lee IM, Lee JH (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18(3):189–200

  47. 47.

    Niroumand H, Kassim KA, Nazir R, Faizi K, Adhami B, Moayedi H, Loon W (2012) Slope stability and sheet pile and contiguous bored pile walls. Electron J Geotech Eng 17:19–27

  48. 48.

    Moayedi H, Nazir R, Mosallanezhad M (2015) Determination of reliable stress and strain distributions along bored piles. Soil Mech Found Eng 51(6):285–291

  49. 49.

    Nazir R, Moayedi H, Mosallanezhad M, Tourtiz A (2015) Appraisal of reliable skin friction variation in a bored pile. Proc Inst Civ Eng-Geotech Eng 168(1):75–86

  50. 50.

    Moayedi H, Armaghani DJ (2017) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34(2):347–356

  51. 51.

    Moayedi H, Mosallanezhad M (2017) Uplift resistance of belled and multi-belled piles in loose sand. Measurement 109:346–353

  52. 52.

    Moayedi H, Mosallanezhad M, Nazir R (2017) Evaluation of maintained load test (MLT) and pile driving analyzer (PDA) in measuring bearing capacity of driven reinforced concrete piles. Soil Mech Found Eng 54(3):150–154

  53. 53.

    Mosallanezhad M, Moayedi H (2017) Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci 10(22):10

  54. 54.

    Nazir R, Moayedi H, Subramaniam P, Gue S-S (2017) Application and design of transition piled embankment with surcharged prefabricated vertical drain intersection over soft ground. Arab J Sci Eng 43:1–10

  55. 55.

    Moayedi H, Hayati S (2018) Applicability of a CPT-based neural network solution in predicting load-settlement responses of bored pile. Int J Geomech 18(6):06018009

  56. 56.

    Moayedi H, Hayati S (2018) Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3555-5

  57. 57.

    Asadi A, Moayedi H, Huat BB, Boroujeni FZ, Parsaie A, Sojoudi S (2011) Prediction of zeta potential for tropical peat in the presence of different cations using artificial neural networks. Int J Electrochem Sci 6(4):1146–1158

  58. 58.

    Asadi A, Moayedi H, Huat BBK, Parsaie A, Taha MR (2011) Artificial neural networks approach for electrochemical resistivity of highly organic soil. Int J Electrochem Sci 6(4):1135–1145

  59. 59.

    Asadi A, Shariatmadari N, Moayedi H, Huat BB (2011) Effect of MSW leachate on soil consistency under influence of electrochemical forces induced by soil particles. Int J Electrochem Sci 6(7):2344–2351

  60. 60.

    Benardos AG, Kaliampakos DC (2004) Modelling TBM performance with artificial neural networks. Tunn Undergr Space Technol 19(6):597–605

  61. 61.

    Ahmad I, El Naggar M, Khan AN (2007) Artificial neural network application to estimate kinematic soil pile interaction response parameters. Soil Dyn Earthq Eng 27(9):892–905

  62. 62.

    Chakraborty A, Goswami D (2017) Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arab J Geosci 10(17):11

  63. 63.

    Shahin MA (2015) A review of artificial intelligence applications in shallow foundations. Int J Geotech Eng 9(1):49–60

  64. 64.

    Fatehnia M, Amirinia G (2018) A review of genetic programming and artificial neural network applications in pile foundations. Int J Geo-Eng 9(1):20

  65. 65.

    Mabbutt S, Picton P, Shaw P, Black S (2012) Review of artificial neural networks (ANN) applied to corrosion monitoring. In: Ball A, Mishra R, Gu F, Rao BKN (eds) 25th international congress on condition monitoring and diagnostic engineering. Iop Publishing Ltd., Bristol

  66. 66.

    Shahin MA (2016) State-of-the-art review of some artificial intelligence applications in pile foundations. Geosci Front 7(1):33–44

  67. 67.

    Lai JX, Qiu JL, Feng ZH, Chen JX, Fan HB (2016) Prediction of soil deformation in tunnelling using artificial neural networks. Comput Intell Neurosci 16:33

  68. 68.

    Alimoradi A, Moradzadeh A, Naderi R, Salehi MZ, Etemadi A (2008) Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks. Tunn Undergr Space Technol 23(6):711–717

  69. 69.

    Alavi AH, Gandomi AH (2011) A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput 28(3–4):242–274

  70. 70.

    Zhang WG, Goh ATC (2016) Predictive models of ultimate and serviceability performances for underground twin caverns. Geomech Eng 10(2):175–188

  71. 71.

    Zhang WG, Goh ATC (2015) Regression models for estimating ultimate and serviceability limit states of underground rock caverns. Eng Geol 188:68–76

  72. 72.

    Asr AA, Javadi A (2016) Air losses in compressed air tunnelling: a prediction model. Proc Inst Civ Eng-Eng Comput Mech 169(3):140–147

  73. 73.

    Latifi N, Vahedifard F, Ghazanfari E, Horpibulsuk S, Marto A, Williams J (2018) Sustainable improvement of clays using low-carbon nontraditional additive. Int J Geomech 18(3):10

  74. 74.

    Moayedi H, Hayati S (2018) Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl Soft Comput 66:208–219

  75. 75.

    Moayedi H, Huat B, Kazemian S, Asadi A (2010) Optimization of shear behavior of reinforcement through the reinforced slope. Electron J Geotech Eng

  76. 76.

    Moayedi H, Huat BB, Asadi A (2010) Strain absorption optimization of reinforcement in geosynthetic reinforced slope-experimental and FEM modeling. Electron J Geotech Eng, USA 15

  77. 77.

    Nazir R, Ghareh S, Mosallanezhad M, Moayedi H (2016) The influence of rainfall intensity on soil loss mass from cellular confined slopes. Measurement 81:13–25

  78. 78.

    Nazir R, Moayedi H (2014) Soil mass loss reduction during rainfalls by reinforcing the slopes with the surficial confinement. World Academy of Science, Engineering and Technology. Int J Geol Environ Eng 8(6):381–384

  79. 79.

    Raftari M, Kassim KA, Rashid ASA, Moayedi H (2013) Settlement of shallow foundations near reinforced slopes. Electron J Geotech Eng 18:797–808

  80. 80.

    Shahri AA (2016) Assessment and prediction of liquefaction potential using different artificial neural network models: a case study. Geotech Geol Eng 34(3):807–815

  81. 81.

    Chern SG, Lee CY (2009) CPT-based simplified liquefaction assessment by using fuzzy-neural network. J Mar Sci Technol-Taiwan 17(4):326–331

  82. 82.

    Calabrese A, Lai CG (2013) Fragility functions of blockwork wharves using artificial neural networks. Soil Dyn Earthq Eng 52:88–102

  83. 83.

    Moayedi H, Huat BB, Moayedi F, Asadi A, Parsaie A (2011) Effect of sodium silicate on unconfined compressive strength of soft clay. Electron J Geotech Eng 16:289–295

  84. 84.

    Garg A, Garg A, Tai K, Barontini S, Stokes A (2014) A computational intelligence-based genetic programming approach for the simulation of soil water retention curves. Transp Porous Media 103(3):497–513

  85. 85.

    Erzin Y (2007) Artificial neural networks approach for swell pressure versus soil suction behaviour. Can Geotech J 44(10):1215–1223

  86. 86.

    Latifi N, Marto A, Eisazadeh A (2016) Experimental investigations on behaviour of strip footing placed on chemically stabilised backfills and flexible retaining walls. Arab J Sci Eng 41(10):4115–4126

  87. 87.

    Latifi N, Rashid ASA, Siddiqua S, Abd Majid MZ (2016) Strength measurement and textural characteristics of tropical residual soil stabilised with liquid polymer. Measurement 91:46–54

  88. 88.

    Bagtzoglou AC, Hossain F (2009) Radial basis function neural network for hydrologic inversion: an appraisal with classical and spatio-temporal geostatistical techniques in the context of site characterization. Stoch Environ Res Risk Assess 23(7):933–945

  89. 89.

    Juang CH, Jiang T, Christopher RA (2001) Three-dimensional site characterisation: neural network approach. Geotechnique 51(9):799–809

  90. 90.

    AttohOkine NO, Fekpe ESK (1996) Strength characteristics modeling of lateritic soils using adaptive neural networks. Constr Build Mater 10(8):577–582

  91. 91.

    Zhu JH, Zaman MM, Anderson SA (1998) Modelling of shearing behaviour of a residual soil with recurrent neural network. Int J Numer Anal Methods Geomech 22(8):671–687

  92. 92.

    Pal M (2006) Support vector machines-based modelling of seismic liquefaction potential. Int J Numer Anal Methods Geomech 30(10):983–996

  93. 93.

    Pala M, Caglar N, Elmas M, Cevik A, Saribiyik M (2008) Dynamic soil-structure interaction analysis of buildings by neural networks. Constr Build Mater 22(3):330–342

  94. 94.

    Nazzal MD, Tatari O (2013) Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus. Int J Pavement Eng 14(4):364–373

  95. 95.

    Park HI, Kweon GC, Lee SR (2009) Prediction of resilient modulus of granular subgrade soils and subbase materials using artificial neural network. Road Mater Pavement Des 10(3):647–665

  96. 96.

    Groholski DR, Hashash YMA (2013) Development of an inverse analysis framework for extracting dynamic soil behavior and pore pressure response from downhole array measurements. Int J Numer Anal Methods Geomech 37(12):1867–1890

  97. 97.

    Nazir R, Moayedi H, Pratikso A, Mosallanezhad M (2014) The uplift load capacity of an enlarged base pier embedded in dry sand. Arab J Geosci 8:1–12

  98. 98.

    Moayedi H (2019) Optimization of ANFIS with GA and PSO estimating α in driven shafts. Eng Comput 35:1–12

  99. 99.

    Chan WT, Chow YK, Liu LF (1995) Neural-network—an alternative to pile driving formulas. Comput Geotech 17(2):135–156

  100. 100.

    Ismail A, Jeng DS (2011) Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model). Eng Appl Artif Intell 24(5):813–821

  101. 101.

    Li YZ, Yao QF, Qin LK (2008) The application of neural network to deep foundation pit retaining structure displacement prediction. World Acad Union-World Acad Press, Liverpool

  102. 102.

    Chen YH, Wang YW (2012) The analysis on the deformation predition of pile-anchor retaining structure in deep foundation pit in Kunming. In: Zhou XG, Chu MJ, Liu JM, Qu SY, Fan HT (eds) Progress in Structure, Pts 1-4. Trans Tech Publications Ltd., Stafa-Zurich, pp 1222–1225

  103. 103.

    Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 99(1–2):51–60

  104. 104.

    Cao JW, Huang WH, Zhao T, Wang JZ, Wang RR (2017) An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature. Multidimens Syst Signal Process 28(3):921–943

  105. 105.

    Kwon S, Wilson JW (1998) Investigation of the influence of an excavation on adjacent excavations, using neural networks. J S Afr Inst Min Metall 98(3):147–156

  106. 106.

    Jan JC, Hung SL, Chi SY, Chern JC (2002) Neural network forecast model in deep excavation. J Comput Civ Eng 16(1):59–65

  107. 107.

    Chua CG, Goh ATC (2005) Estimating wall deflections in deep excavations using Bayesian neural networks. Tunn Undergr Space Technol 20(4):400–409

  108. 108.

    Huang FK, Wang GS (2007) ANN-based reliability analysis for deep excavation. IEEE, New York

  109. 109.

    Chern S, Tsai JH, Chien LK, Huang CY (2009) Predicting lateral wall deflection in top–down excavation by neural network. Int J Offshore Polar Eng 19(2):151–157

  110. 110.

    Yu J, Chen HM, Yu J, Chen HM (2009) Artificial neural network’s application in intelligent prediction of surface settlement induced by foundation pit excavation. Ieee Computer Soc, Los Alamitos

  111. 111.

    Huang YT, Siller TJ (1997) Fuzzy representation and reasoning in geotechnical site characterization. Comput Geotech 21(1):65–86

  112. 112.

    Yilmaz O, Eser M, Berilgen M (2009) Applications of engineering seismology for site characterization. J. Earth Sci 20(3):546–554

  113. 113.

    Garcia-Fernandez M, Jimenez MJ (2012) Site characterization in the Vega Baja, SE Spain, using ambient-noise H/V analysis. Bull Earthq Eng 10(4):1163–1191

  114. 114.

    Orhan A, Turkoz M, Tosun H (2013) Preliminary hazard assessment and site characterization of MeAYelik campus area. EskiAYehir-Turk Nat Hazards Earth Syst Sci 13(1):75–84

  115. 115.

    Kim AR, Cho GC, Kwon TH (2014) Site characterization and geotechnical aspects on geological storage of CO2 in Korea. Geosci J 18(2):167–179

  116. 116.

    Cao ZJ, Wang Y, Li DQ (2016) Quantification of prior knowledge in geotechnical site characterization. Eng Geol 203:107–116

  117. 117.

    Wang JP (2016) Site characterization with multiple measurement profiles from different tests: a Bayesian approach. Soils Found 56(4):712–718

  118. 118.

    Aladejare AE, Wang Y (2017) Sources of uncertainty in site characterization and their impact on geotechnical reliability-based design. ASCE-ASME J Risk Uncertain Eng Syst Part A-Civ Eng 3(4):12

  119. 119.

    Roy N, Jakka RS (2017) Near-field effects on site characterization using MASW technique. Soil Dyn Earthq Eng 97:289–303

  120. 120.

    Samui P, Sitharam TG (2010) Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c). Int J Numer Anal Methods Geomech 34(7):755–770

  121. 121.

    Samui P, Sitharam TG (2010) Site characterization model using artificial neural network and kriging. Int J Geomech 10(5):171–180

  122. 122.

    Dwivedi VK, Dubey RK, Thockhom S, Pancholi V, Chopra S, Rastogi BK (2017) Assessment of liquefaction potential of soil in Ahmedabad region. West India J Indian Geophys Union 21(2):116–123

  123. 123.

    Monkul MM, Gultekin C, Gulver M, Akin O, Eseller-Bayat E (2015) Estimation of liquefaction potential from dry and saturated sandy soils under drained constant volume cyclic simple shear loading. Soil Dyn Earthq Eng 75:27–36

  124. 124.

    Shahri AA, Behzadafshar K, Rajablou R (2013) Verification of a new method for evaluation of liquefaction potential analysis. Arab J Geosci 6(3):881–892

  125. 125.

    Kayen R, Moss RES, Thompson EM, Seed RB, Cetin KO, Kiureghian AD, Tanaka Y, Tokimatsu K (2013) Shear-wave velocity-based probabilistic and deterministic assessment of seismic soil liquefaction potential. J Geotech Geoenviron Eng 139(3):407–419

  126. 126.

    Arango I, Lewis MR, Kramer C (2000) Updated liquefaction potential analysis eliminates foundation retrofitting of two critical structures. Soil Dyn Earthq Eng 20(1–4):17–25

  127. 127.

    Goh A (1994) Seismic liquefaction potential assessed by neural networks. J Geotech Eng 120(9):1467–1480

  128. 128.

    Seed HB, Tokimatsu K, Harder LF, Chung RM (1985) Influence of SPT procedures in soil liquefaction resistance evaluations. J Geotech Eng-ASCE 111(12):1425–1445

  129. 129.

    Goh ATC (1994) Nonlinear modelling in geotechnical engineering using neural networks. Trans Inst Eng, Aust Civ Eng 36(4):293–297

  130. 130.

    Juang CH, Chen CJX, Tien YM (1999) Appraising cone penetration test based liquefaction resistance evaluation methods: artificial neural network approach. Can Geotech J 36(3):443–454

  131. 131.

    Liu BY, Ye LY, Xiao ML, Miao S (2006) Artificial neural network methodology for soil liquefaction evaluation using CPT values. In: Huang DS, Li K, Irwin GW (eds) Intelligent computing, part I: international conference on intelligent computing, Icic 2006, part I. Springer, Berlin, pp 329–336

  132. 132.

    Shibata T, Teparaksa W (1988) Evaluation of liquefaction potentials of soils using cone penetration tests. Soils Found 28(2):49–60

  133. 133.

    Wang J, Rahman MS (1999) A neural network model for liquefaction-induced horizontal ground displacement. Soil Dyn Earthq Eng 18(8):555–568

  134. 134.

    Young-Su K, Byung-Tak K (2006) Use of artificial neural networks in the prediction of liquefaction resistance of sands. J Geotech Geoenviron Eng 132(11):1502–1504

  135. 135.

    Hsu SC, Yang MD, Chen MC, Lin JY (2011) Neural network modeling of liquefaction resistance from shear wave velocity. In: Zhou M (ed) 2011 3rd World congress in applied computing, computer science, and computer engineering. Information Engineering Research Inst, Newark, p 155

  136. 136.

    Zhang WG, Goh ATC, Zhang YM, Chen YM, Xiao Y (2015) Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines. Eng Geol 188:29–37

  137. 137.

    Goh ATC, Goh SH (2007) Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data. Comput Geotech 34(5):410–421

  138. 138.

    Lu P, Rosenbaum MS (2003) Artificial neural networks and Grey Systems for the prediction of slope stability. Nat Hazards 30(3):383–398

  139. 139.

    Li SJ, Liu YX (2004) Intelligent forecast procedures for slope stability with evolutionary artificial neural network. In: Yin FL, Wang J, Guo CG (eds) Advances in neural networks–Isnn 2004, Pt 2. Springer, Berlin, pp 792–798

  140. 140.

    Liu ZB, Shao JF, Xu WY, Chen HJ, Zhang Y (2014) An extreme learning machine approach for slope stability evaluation and prediction. Nat Hazards 73(2):787–804

  141. 141.

    Aghajani HF, Salehzadeh H, Shahnazari H (2015) Application of artificial neural network for calculating anisotropic friction angle of sands and effect on slope stability. J Cent South Univ 22(5):1878–1891

  142. 142.

    Rahul A, Khandelwal M, Rai R, Shrivastva BK (2015) Evaluation of dump slope stability of a coal mine using artificial neural network. Geomech Geophys Geo-Energy Geo-Resour 1(3–4):69–77

  143. 143.

    Gordan B, Armaghani DJ, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85–97

  144. 144.

    Li AJ, Khoo S, Lyamin AV, Wang Y (2016) Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm. Autom Constr 65:42–50

  145. 145.

    Yamagami T, Jiang JC, Ueta Y (1997) Back calculation of strength parameters for landslide control works using neural networks. A a Balkema Publishers, Leiden

  146. 146.

    Cai DS, Wang GY, Hu TS (1998) A neural network method of landslide prediction of the Geheyan reservoir area of Qingjiang. A a Balkema Publishers, Leiden

  147. 147.

    Kobayashi T, Furuta H, Hirokane M, Tanaka S, Tatekawa I (1998) Data mining and analysis for landslide risk using neural networks. A a Balkema Publishers, Leiden

  148. 148.

    Dahigamuwa T, Yu QY, Gunaratne M (2016) Feasibility study of land cover classification based on normalized difference vegetation index for landslide risk assessment. Geosciences 6(4):14

  149. 149.

    Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron highland, Malaysia. Landslides 7(1):13–30

  150. 150.

    Murillo-Garcia FG, Alcantara-Ayala I (2015) Landslide susceptibility analysis and mapping using statistical multivariate techniques: Pahuatlan, Puebla, Mexico. In: Wu W (ed) Recent advances in modeling landslides and Debris flows. Springer, Berlin, pp 179–194

  151. 151.

    Souza FT, Ebecken NFF (2004) A data mining approach to landslide prediction. In: Zanasi A, Ebecken NFF, Brebbia CA (eds) Data mining V: data mining, text mining and their business applications. Wit Press, Southampton, pp 423–432

  152. 152.

    Wu AL, Zeng ZG, Fu CJ (2014) Data mining paradigm based on functional networks with applications in landslide prediction. In: Proceedings of the 2014 international joint conference on neural networks. IEEE, New York, pp 2826–2830

  153. 153.

    Li Y, Chen G, Tang C, Zhou G, Zheng L (2012) Rainfall and earthquake-induced landslide susceptibility assessment using GIS and artificial neural network. Nat Hazards Earth Syst Sci 12(8):2719–2729

  154. 154.

    Xu C, Shen LL, Wang GL (2016) Soft computing in assessment of earthquake-triggered landslide susceptibility. Environ Earth Sci 75(9):17

  155. 155.

    Wang WD, Xie CM, Du XG (2009) Landslides susceptibility mapping based on geographical information system, GuiZhou, south–west China. Environ Geol 58(1):33–43

  156. 156.

    Ilia I, Koumantakis I, Rozos D, Koukis G, Tsangaratos P (2015) A geographical information system (GIS) based probabilistic certainty factor approach in assessing landslide susceptibility: the case study of Kimi, Euboea, Greece. Springer, Cham

  157. 157.

    Moher D, Liberati A, Tetzlaff J, Altman DG, Prisma Group (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097

  158. 158.

    Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA, Prisma PG (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ-Br Med J 349:25

  159. 159.

    Mardani A, Nilashi M, Zakuan N, Loganathan N, Soheilirad S, Saman MZM, Ibrahim O (2017) A systematic review and meta-analysis of SWARA and WASPAS methods: theory and applications with recent fuzzy developments. Appl Soft Comput 57:265–292

  160. 160.

    Welch V, Petticrew M, Tugwell P, Moher D, O’Neill J, Waters E, White H (2012) PRISMA-equity 2012 extension: reporting guidelines for systematic reviews with a focus on health equity. Plos Med 9(10):7

  161. 161.

    Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JPA, Clarke M, Devereaux PJ, Kleijnen J, Moher D (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Plos Med 6(7):28

  162. 162.

    Hill T, Marquez L, O’Connor M, Remus W (1994) Artificial neural network models for forecasting and decision making. Int J Forecast 10(1):5–15

  163. 163.

    Shafaei SM, Nourmohamadi-Moghadami A, Kamgar S (2016) Development of artificial intelligence based systems for prediction of hydration characteristics of wheat. Comput Electron Agric 128:34–45

  164. 164.

    Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251–257

  165. 165.

    Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366

  166. 166.

    Han J, Moraga C, Sinne S (1996) Optimization of feedforward neural networks. Eng Appl Artif Intell 9(2):109–119

  167. 167.

    Uncuoglu E, Laman M, Saglamer A, Kara HB (2008) Prediction of lateral effective stresses in sand using artificial neural network. Soils Found 48(2):141–153

  168. 168.

    Lian C, Zeng ZG, Yao W, Tang HM (2013) Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat Hazards 66(2):759–771

  169. 169.

    Protopapadakis E, Schauer M, Pierri E, Doulamis AD, Stavroulakis GE, Bohrnsen JU, Langer S (2016) A genetically optimized neural classifier applied to numerical pile integrity tests considering concrete piles. Comput Struct 162:68–79

  170. 170.

    Mustafa MR, Rezaur RB, Rahardjo H, Isa MH (2012) Prediction of pore-water pressure using radial basis function neural network. Eng Geol 135:40–47

  171. 171.

    Shu SX, Gong WH (2015) Radial basis function neural network-based method for slope stability analysis under two-dimensional random field. Rock Soil Mech 36(4):1205–1210

  172. 172.

    Kang F, Li JJ, Xu Q (2017) System reliability analysis of slopes using multilayer perceptron and radial basis function networks. Int J Numer Anal Methods Geomech 41(18):1962–1978

  173. 173.

    Zhang W, Dai BB, Liu Z, Zhou CY (2017) Modeling free-surface seepage flow in complicated fractured rock mass using a coupled RPIM-FEM method. Transp Porous Media 117(3):443–463

  174. 174.

    Samui P, Kurup P, Dhivya S, Jagan J (2016) Reliability analysis of quick sand condition. Geotech Geol Eng 34(2):579–584

  175. 175.

    Asadizadeh M, Hossaini MF (2016) Predicting rock mass deformation modulus by artificial intelligence approach based on dilatometer tests. Arab J Geosci 9(2):15

  176. 176.

    Peng C, Wu W, Zhang BY (2015) Three-dimensional simulations of tensile cracks in geomaterials by coupling meshless and finite element method. Int J Numer Anal Methods Geomech 39(2):135–154

  177. 177.

    Wang Q, Lin J, Ji J, Fang H (2014) Reliability analysis of geotechnical engineering problems based on an RBF metamodeling technique. Crc Press-Taylor & Francis Group, Boca Raton

  178. 178.

    Liao KW, Fan JC, Huang CL (2011) An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts. Comput Geotech 38(8):978–986

  179. 179.

    Liao KW, Huang CL (2011) Estimation of groutability of permeation grouting with microfine cement grouts using RBFNN. In: Liu D, Zhang H, Polycarpou M, Alippi C, He H (eds) Advances in neural networks—Isnn 2011, Pt Iii. Springer, Berlin, p 475

  180. 180.

    Ibric S, Jovanovic M, Djuric Z, Parojcic J, Solomun L, Lucic B (2007) Generalized regression neural networks in prediction of drug stability. J Pharm Pharmacol 59(5):745–750

  181. 181.

    Pal M, Deswal S (2008) Modeling pile capacity using support vector machines and generalized regression neural network. J Geotech Geoenviron Eng 134(7):1021–1024

  182. 182.

    Jiang P, Zeng ZG, Chen JJ, Huang TW (2014) Generalized regression neural networks with K-fold cross-validation for displacement of landslide forecasting. In: Zeng Z, Li Y, King I (eds) Advances in Neural Networks–—Isnn 2014. Springer, Berlin, pp 533–541

  183. 183.

    Goorani M, Hamidi A (2015) A generalized plasticity constitutive model for sand–gravel mixtures. Int J Civ Eng 13(2B):133–145

  184. 184.

    Rajesh BG, Choudhury D (2017) Generalized seismic active thrust on a retaining wall with submerged backfill using a modified pseudodynamic method. Int J Geomech 17(3):10

  185. 185.

    Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63–68

  186. 186.

    Li HZ, Guo S, Li CJ, Sun JQ (2013) A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl-Based Syst 37:378–387

  187. 187.

    Kumar CS, Arumugam V, Sengottuvelusamy R, Srinivasan S, Dhakal H (2017) Failure strength prediction of glass/epoxy composite laminates from acoustic emission parameters using artificial neural network. Appl Acoust 115:32–41

  188. 188.

    Vardhan H, Bordoloi S, Garg A, Garg A, Sreedeep S (2017) Compressive strength analysis of soil reinforced with fiber extracted from water hyacinth. Eng Comput 34(2):330–342

  189. 189.

    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

  190. 190.

    Samui P (2012) Determination of ultimate capacity of driven piles in cohesionless soil: a multivariate adaptive regression spline approach. Int J Numer Anal Methods Geomech 36(11):1434–1439

  191. 191.

    Samui P, Das SK, Sitharam TG (2009) Application of soft computing techniques to expansive soil characterization. In: Gopalakrishnan K, Ceylan H, Okine NOA (eds) Intelligent and soft computing in infrastructure systems engineering: recent advances. Springer, Berlin, pp 305–323

  192. 192.

    Jang JSR, Sun CT (1995) Neuro-fuzzy modeling and control. Proc IEEE 83(3):378–406

  193. 193.

    Jang SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst, Man, Cybern 23(3):665–685

  194. 194.

    Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33

  195. 195.

    Balamurugan G, Ramesh V, Touthang M (2016) Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur. India Nat Hazards 84(1):465–488

  196. 196.

    Ramesh V, Anbazhagan S (2015) Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models. Environ Earth Sci 73(12):8009–8021

  197. 197.

    Bui DT, Pradhan B, Revhaug I, Nguyen DB, Pham HV, Bui QN (2015) A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomat Nat Hazards Risk 6(3):243–271

  198. 198.

    Vasu NN, Lee SR, Pradhan AMS, Kim YT, Kang SH, Lee DH (2016) A new approach to temporal modelling for landslide hazard assessment using an extreme rainfall induced-landslide index. Eng Geol 215:36–49

  199. 199.

    denHartog MH, Babuska R, Deketh HJR, Grima MA, Verhoef PNW, Verbruggen HB (1997) Knowledge-based fuzzy model for performance prediction of a rock-cutting trencher. Int J Approx Reason 16(1):43–66

  200. 200.

    Ghaboussi J, Sidarta DE (1998) New nested adaptive neural networks (NANN) for constitutive modeling. Comput Geotech 22(1):29–52

  201. 201.

    Grima MA, Babuska R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36(3):339–349

  202. 202.

    Baykasoglu A, Cevik A, Ozbakir L, Kulluk S (2009) Generating prediction rules for liquefaction through data mining. Expert Syst Appl 36(10):12491–12499

  203. 203.

    Kayadelen C, Taskiran T, Gunaydin O, Fener M (2009) Adaptive neuro-fuzzy modeling for the swelling potential of compacted soils. Environ Earth Sci 59(1):109–115

  204. 204.

    Sezer A, Goktepe BA, Altun S (2010) Adaptive neuro-fuzzy approach for sand permeability estimation. Environ Eng Manag J 9(2):231–238

  205. 205.

    Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE, Piscataway

  206. 206.

    Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13(2):1085–1098

  207. 207.

    Marto A, Hajihassani M, Armaghani DJ, Mohamad ET, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J 2014:1–11

  208. 208.

    Thangavelautham J, Smith A, El Samid NA, Ho A, Boucher D, Richard J, D’Eleuterio GMT (2008) Multirobot lunar excavation and ISRU using artificial-neural-tissue controllers. In: ElGenk MS (ed) Space technology and applications international forum staif 2008. Amer Inst Physics, Melville, p 229

  209. 209.

    Manouchehrian A, Gholamnejad J, Sharifzadeh M (2014) Development of a model for analysis of slope stability for circular mode failure using genetic algorithm. Environ Earth Sci 71(3):1267–1277

  210. 210.

    Lian C, Zeng ZG, Yao W, Tang HM, Chen CLP (2016) Landslide displacement prediction with uncertainty based on neural networks with random hidden weights. IEEE Trans Neural Netw Learn Syst 27(12):2683–2695

  211. 211.

    Gandomi AH, Kashani AR (2018) Automating pseudo-static analysis of concrete cantilever retaining wall using evolutionary algorithms. Measurement 115:104–124

  212. 212.

    Ghorbani A, Jokar MRA (2016) A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem. Comput Ind Eng 101:116–127

  213. 213.

    Al Dossary MA, Nasrabadi H (2016) Well placement optimization using imperialist competitive algorithm. J Pet Sci Eng 147:237–248

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Correspondence to Ahmad Safuan A. Rashid.

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Moayedi, H., Mosallanezhad, M., Rashid, A.S.A. et al. A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Comput & Applic 32, 495–518 (2020) doi:10.1007/s00521-019-04109-9

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Keywords

  • PRISMA
  • ANN
  • Soft computing
  • Geotechnical engineering