Skip to main content
SpringerLink
Log in

A Scientometrics Review of Soil Properties Prediction Using Soft Computing Approaches

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

In this world, several types of soils are available with their different engineering properties. Determining each soil's engineering properties is difficult because the laboratory procedures are time-consuming. Therefore, several researchers have employed different soft computing techniques to assess soil properties. The soft computing approaches are classified into machine, hybrid, blended, and deep learning. The learning process of these approaches is sub-categorized as supervised, unsupervised, and reinforced learning. This review article presents the performance comparison of different soft computing approaches in predicting the compaction parameters, soaked CBR, unsoaked CBR, and unconfined compressive strength. Several researchers have reported comparisons of the several models and presented optimum performance models based on performance metrics. However, different training databases were utilized in the reported studies. Therefore, the optimum performance model/approach is questionable. In addition, it is well-recognized that the multicollinearity of the training database affects the performance and accuracy of the soft computing models, which has not been studied yet. Very few researchers have performed statistical tests to ensure the quality and quantity of the database. Nowadays, researchers are developing different hybrid approaches to assess soil properties, but the configuration of the hyperparameters is still unknown to obtain the best prediction. This review article introduces several ideas to geotechnical designers/ engineers to develop the optimum performance soft computing model for predicting soil properties. Also, this article will help scientists and researchers get new ideas for innovative research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

No database has been used in this research.

Abbreviations

A:

Activity

A/B:

Amount of Alkali to binder proportion

A20:

A20-Index

AD:

Anderson Darling Test

AI:

Artificial intelligence

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural networks

ANOVA:

Analysis of variance

Apt:

Asphalt

BAT:

Bat optimization algorithm

BBO-RBF:

Biogeography-based radial basis function

BF:

Bias factor

BFS:

Blast furnace slag

BHB:

Blackhole optimization algorithm

BR:

Bayesian regularization algorithm

BRNN:

Bayesian regularization algorithm-based neural network

C:

Clay content

c:

Cohesion

C/M:

Ratio of clay to silt

CAM:

Cosine amplitude method

CBR:

California bearing ratio

CBR10 :

CBR sample compacted by 10 blows

CBR30 :

CBR sample compacted by 30 blows

CBR60 :

CBR sample compacted by 60 blows

CC:

Coefficient of curvature

Cc :

Cement condition

Ccc :

Curing condition

CD:

Compaction degree

Ce:

Cement

CFB:

Cascade and feed forward back propagation algorithm

COD:

Coefficient of determination

CODs:

Coefficient of determinations

CS:

Coarse sand content

CS:

Coarse sand content

CU:

Coefficient of Uniformity

D:

Depth of the cone in soil sample

D10:

Particle size at 10% finer

D30:

Particle size at 30% finer

D50:

Particle size at 50% finer

D50:

Particle size at 50% finer

D60:

Particle size at 60% finer

DCP:

Dynamic cone penetrometer

DCPI:

Dynamic Cone Penetrometer Index

DENN:

Differential evolution algorithm-based neural network

DL:

Deep learning

DT:

Decision tree

DUW:

Dry unit weight

EC, E:

Compaction energy

ELM:

Extreme learning machine

ELM-GWO:

Grey Wolf Optimization Algorithm-Based ELM

ELM-HHO:

Harris Hawks Optimization Based ELM

ELM-IPSO:

Improved Particle Swarm Optimization Based ELM

ELM-MPSO:

Modified PSO Based ELM

ELM-PSO:

PSO-optimized ELM

ELM-PSO:

Particle swarm optimized ELM

ELM-SMA:

Slime mould algorithm based ELM

ELM-TPSO:

Time-varying acceleration coefficients PSO-based ELM

EPR:

Evolutionary polynomial regression

EQ:

Equilibrium optimizer

ERF:

Evolutionary random forest

FA:

Fly Ash

FC:

Fine Content

FN:

Functional Network

FS:

Fine Sand Content

G:

Gravel Content

GA:

Genetic Algorithm

GB:

Gradient Boosting

GC:

Clayey Gravel

GDM:

Gradient Descent with Momentum Algorithm

GEP:

Gene Expression Programming

GGBS:

Ground Granulated Blast-furnace Slag

GMDH:

Group Method of Data Handling

GOA:

Grasshopper Optimization Algorithm

GP:

Genetic Programming

GPR:

Gaussian Process Regression

GPs:

Gradational Parameters

ɣw:

Wet Density

HL:

Hybrid Learning

HLi:

Hydrated Lime

HLi:

Hydrated Lime

ICA:

Imperialist Competitive Algorithm

IOA:

Index of Agreement

IOS:

Index of Scatter

IP:

Plasticity Index

kNN:

K-nearest neighbors

L:

Specimen Length

L1:

Position of 1st Layer

LC :

Lime Content

LGBM:

Light Gradient Boosting Machine

Li:

Lime

LMI:

Legate and McCabe's Index

LMNN:

Levenberg–Marquardt algorithm based neural network

LSBoost:

Least square boost

LSSVM:

Least square support vector machine

M:

Silt content

M':

Molar concentration of alkali solution

M5P:

M5 Tree

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MARS:

Multivariate adaptive regression splines

MARS-C:

Multivariate adaptive regression splines-cubic

MARS-L:

Multivariate adaptive regression splines-linear

MCP:

Modified compaction parameters

MDD:

Maximum dry density

MDDM:

Maximum dry density by modified proctor test

MDDP:

Predicted maximum dry density

MDDS:

Maximum dry density by standard proctor test

MEP:

Multi expression programming

MGGP:

Multi-gen genetic programming

ML:

Machine learning

MLP:

Multilayer perceptron

MLR:

Multilinear regression

MPMR:

Minimax probability machine regression

MPMT:

Miniaturized pressure meter

MRA:

Multilinear regression analysis

MS:

Medium sand content

MS :

Microsilica percentage

N60:

Corrected SPT-N

Na/Al:

Atomic proportion of Na to lL

NL:

Number of layers

NMBE:

Normalized mean bias error

NMC:

Natural moisture content

NN:

Neural network

NS:

Nash–sutcliffe efficiency

NWC:

Natural water content

OBLGOA:

Opposition-based learning grasshopper optimization algorithm

OC:

Organic content

OEM:

Optimizable ensemble machine

OLS:

Ordinary least squares

OMC:

Optimum moisture content

OMCM:

Optimum moisture content by modified proctor test

OMCP:

Predicted optimum moisture content

OMCS:

Optimum moisture content by standard proctor test

owc:

Optimum water content

NNNNɸ:

Angle of internal friction

PA:

Pond ash

PCA:

Principal component analysis

pH:

Potential hydrogen

PI:

Plasticity Index

PL:

Plastic limit

PPV:

Peak particle velocity

PSO:

Particle swarm optimization algorithm

PSO-RBF:

Particle swarm optimized radial basis function

PZ:

Pozzolans

R:

Performance of model in terms of correlation coefficient

R2:

Coefficient of determinations

RBF:

Radial basis function

REC:

Regression error characteristics curve

REPTs:

Reduced error pruning trees

RF:

Random forest

RHA:

Rice husk ash

RMSE:

Root mean square error

RRHC:

Random restart hill-climbing optimization algorithm

RSR:

Root mean square error to observation's standard deviation ratio

RSS:

Random subsurface-based

RSS-ET:

RSS-based extra tree

RSS-REPT:

Random subsurface-based reduced error pruning trees

RVM:

Relevance vector machine

RW_GWO:

Random walk grey wolf optimization algorithm

S:

Sand content

SAO:

Sailfish optimization algorithm

SC:

Clayey sand

SCA:

Statistical conventional algorithms

SCG:

Scaled conjugate gradient algorithm

SG:

Specific gravity

Si/Al:

Atomic proportion of Si to Al

SL:

Position of subsequent layers

SMO:

Slim mould optimization algorithm

SOA:

Sandpiper optimization algorithm

SP:

Swelling pressure

SPT:

Standard penetration test

SPT-N:

Number of SPT blows

SRO:

Search and rescue operations optimization algorithm

STI:

Soil type index

SVM:

Support vector machine

UCS:

Unconfined compressive strength

VAF:

Variance accounted for

VIF:

Variance inflation factor

VP:

Measured primary ultrasonic wave velocity

WC:

Water content

WMAPE:

Weighted mean absolute percentage error

XGB:

Xtreme gradient boosting

γd:

Dry density

References

  1. Arora KR (2008) Soil Mechanics and Foundation Engineering (Geotechnical Engineering). Standard publishers, In SI Units

    Google Scholar 

  2. Venkatramaiah C (1995) Geotechnical engineering. New Age International, New York

    Google Scholar 

  3. Whitlow R (1990) Basic soil mechanics. Wiley, New York

    Google Scholar 

  4. Russell SJ (2010) Artificial intelligence a modern approach. Pearson Education Inc., Upper Saddle River

    Google Scholar 

  5. Khatti J, Grover KS (2023) Prediction of compaction parameters of compacted soil using LSSVM, LSTM, LSBoostRF, and ANN. Innovative Infrastructure Solutions 8(2):76. https://doi.org/10.1007/s41062-023-01048-2

    Article  Google Scholar 

  6. Ebid AM (2021) 35 Years of (AI) in geotechnical engineering: state of the art. Geotech Geol Eng 39(2):637–690. https://doi.org/10.1007/s10706-020-01536-7

    Article  Google Scholar 

  7. Samui P (2008) Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput Geotech 35(3):419–427. https://doi.org/10.1016/j.compgeo.2007.06.014

    Article  Google Scholar 

  8. Khatti J, Grover KS (2022) Determination of the optimum performance AI model and methodology to predict the compaction parameters of soils. ICTACT Journal of Soft Computing 12(3):2640–2650. https://doi.org/10.21917/ijsc.2022.0368

    Article  Google Scholar 

  9. Vapnik V (1999) The nature of statistical learning theory. Springer, New York

    Google Scholar 

  10. Kamiński B, Jakubczyk M, Szufel P (2018) A framework for sensitivity analysis of decision trees. CEJOR 26:135–159. https://doi.org/10.1007/s10100-017-0479-6

    Article  MathSciNet  Google Scholar 

  11. Bostrom, N., Superintelligence: Paths, dangers, strategies., 2014. Google Scholar Google Scholar Digital Library Digital Library.

  12. Bardhan A, Samui P, Ghosh K, Gandomi AH, Bhattacharyya S (2021) ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions. Appl Soft Comput 110:107595. https://doi.org/10.1016/j.asoc.2021.107595

    Article  Google Scholar 

  13. Kelleher JD (2019) Deep learning. MIT Press, Cambridge

    Google Scholar 

  14. Ibrahim AS, Musa AA, Abdulfatah AY, Idris A (2023) Developing soft-computing regression model for predicting soil bearing capacity using soil index properties. Model Earth Syst Environ 9(1):1223–1232. https://doi.org/10.1007/s40808-022-01541-0

    Article  Google Scholar 

  15. Lawal AI, Kwon S (2023) Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations. J Rock Mech Geotech Eng 15(3):747–759. https://doi.org/10.1016/j.jrmge.2022.04.005

    Article  Google Scholar 

  16. Ghanizadeh AR, Ghanizadeh A, Asteris PG, Fakharian P, Armaghani DJ (2023) Developing bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method. Transp Geotech 38:100906. https://doi.org/10.1016/j.trgeo.2022.100906

    Article  Google Scholar 

  17. Nguyen DK, Nguyen TP, Ngamkhanong C, Keawsawasvong S, Lai VQ (2023) Bearing capacity of ring footings in anisotropic clays: FELA and ANN. Neural Comput Appl. https://doi.org/10.1007/s00521-023-08278-6

    Article  Google Scholar 

  18. Van Nguyen C, Keawsawasvong S, Nguyen DK, Lai VQ (2023) Machine learning regression approach for analysis of bearing capacity of conical foundations in heterogenous and anisotropic clays. Neural Comput Appl 35(5):3955–3976. https://doi.org/10.1007/s00521-022-07893-z

    Article  Google Scholar 

  19. Leetsaar L, Korkiala-Tanttu L, Kurnitski J (2023) CPT, CPTu and DCPT methods for predicting the ultimate bearing capacity of cast in situ displacement piles in silty soils. Geotech Geol Eng 41(2):631–652. https://doi.org/10.1007/s10706-022-02292-6

    Article  Google Scholar 

  20. Pham TA, Sutman M (2023) A simplified method for bearing-capacity analysis of energy piles integrating temperature-dependent model of soil-water characteristic curve. J Geotech Geoenviron Eng 149(9):04023080. https://doi.org/10.1061/JGGEFK.GTENG-11095

    Article  Google Scholar 

  21. Tavakoli MA, Fathipour H, Payan M, Chenari RJ, Ahmadi H (2023) Seismic bearing capacity of shallow foundations subjected to inclined and eccentric loading using modified pseudo-dynamic method. Transp Geotech 40:100979. https://doi.org/10.1016/j.trgeo.2023.100979

    Article  Google Scholar 

  22. Khatti J, Samadi H, Grover KS (2023) Estimation of settlement of pile group in clay using soft computing techniques. Geotech Geol Eng. https://doi.org/10.1007/s10706-023-02643-x

    Article  Google Scholar 

  23. Egbueri JC, Igwe O, Omeka ME, Agbasi JC (2023) Development of MLR and variedly optimized ANN models for forecasting the detachability and liquefaction potential index of erodible soils. Geosyst Geoenviron 2(1):100104. https://doi.org/10.1016/j.geogeo.2022.100104

    Article  Google Scholar 

  24. Kumar DR, Samui P, Burman A, Kumar S (2023) Seismically induced liquefaction potential assessment by different artificial intelligence procedures. Transp Infrastructure Geotechnol. https://doi.org/10.1007/s40515-023-00327-w

    Article  Google Scholar 

  25. Jas K, Dodagoudar GR (2023) Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP. Soil Dyn Earthq Eng 165:107662. https://doi.org/10.1016/j.soildyn.2022.107662

    Article  Google Scholar 

  26. Sahin EK, Demir S (2023) Greedy-AutoML: a novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential. Eng Appl Artif Intell 119:105732. https://doi.org/10.1016/j.engappai.2022.105732

    Article  Google Scholar 

  27. Jas K, Dodagoudar GR (2023) Liquefaction potential assessment of soils using machine learning techniques: a state-of-the-art review from 1994–2021. Int J Geomech 23(7):03123002. https://doi.org/10.1061/IJGNAI.GMENG-7788

    Article  Google Scholar 

  28. Kumar DR, Samui P, Wipulanusat W, Keawsawasvong S, Sangjinda K, Jitchaijaroen W (2023) Soft-computing techniques for predicting seismic bearing capacity of strip footings in slopes. Buildings 13(6):1371. https://doi.org/10.3390/buildings13061371

    Article  Google Scholar 

  29. Kurnaz TF, Erden C, Kökçam AH, Dağdeviren U, Demir AS (2023) A hyper parameterized artificial neural network approach for prediction of the factor of safety against liquefaction. Eng Geol 319:107109. https://doi.org/10.1016/j.enggeo.2023.107109

    Article  Google Scholar 

  30. Ray R, Choudhary SS, Roy LB, Kaloop MR, Samui P, Kurup PU, Ahn J, Hu JW (2023) Reliability analysis of reinforced soil slope stability using GA-ANFIS, RFC, and GMDH soft computing techniques. Case Stud Constr Mater 18:e01898. https://doi.org/10.1016/j.cscm.2023.e01898

    Article  Google Scholar 

  31. Huang F, Xiong H, Chen S, Lv Z, Huang J, Chang Z, Catani F (2023) Slope stability prediction based on a long short-term memory neural network: comparisons with convolutional neural networks, support vector machines and random forest models. Int J Coal Sci Technol 10(1):18. https://doi.org/10.1007/s40789-023-00579-4

    Article  Google Scholar 

  32. Nanehkaran YA, Licai Z, Chengyong J, Chen J, Anwar S, Azarafza M, Derakhshani R (2023) Comparative analysis for slope stability by using machine learning methods. Appl Sci 13(3):1555. https://doi.org/10.3390/app13031555

    Article  Google Scholar 

  33. Yang Y, Zhou W, Jiskani IM, Lu X, Wang Z, Luan B (2023) Slope stability prediction method based on intelligent optimization and machine learning algorithms. Sustainability 15(2):1169. https://doi.org/10.3390/su15021169

    Article  Google Scholar 

  34. Wang G, Zhao B, Wu B, Zhang C, Liu W (2023) Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases. Int J Miner Sci Technol 33(1):47–59. https://doi.org/10.1016/j.ijmst.2022.07.002

    Article  Google Scholar 

  35. Aminpour M, Alaie R, Khosravi S, Kardani N, Moridpour S, Nazem M (2023) Slope stability machine learning predictions on spatially variable random fields with and without factor of safety calculations. Comput Geotech 153:105094. https://doi.org/10.1016/j.compgeo.2022.105094

    Article  Google Scholar 

  36. Mu’azu MA (2023) Enhancing slope stability prediction using fuzzy and neural frameworks optimized by metaheuristic science. Math Geosci 55(2):263–285. https://doi.org/10.1007/s11004-022-10029-7

    Article  MathSciNet  Google Scholar 

  37. Samui P (2008) Slope stability analysis: a support vector machine approach. Environ Geol 56:255–267. https://doi.org/10.1007/s00254-007-1161-4

    Article  Google Scholar 

  38. Köken E, Koca TK (2023) A comparative study to estimate the mode I fracture toughness of rocks using several soft computing techniques. Turk J Eng 7(4):296–305. https://doi.org/10.31127/tuje.1120669

    Article  Google Scholar 

  39. Alizadeh SM, Iraji A (2023) Application of soft computing and statistical methods to predict rock mass permeability. Soft Comput 27(9):5831–5853. https://doi.org/10.1007/s00500-022-07586-8

    Article  Google Scholar 

  40. Zhao R, Shi S, Li S, Guo W, Zhang T, Li X, Lu J (2023) Deep learning for intelligent prediction of rock strength by adopting measurement while drilling data. Int J Geomech 23(4):04023028. https://doi.org/10.1061/IJGNAI.GMENG-8080

    Article  Google Scholar 

  41. Zhang B, Wang L, Liu J (2023) Finite element analysis and prediction of rock mass permeability based on a two-dimensional plane discrete fracture model. Processes 11(7):1962. https://doi.org/10.3390/pr11071962

    Article  Google Scholar 

  42. Asare EN, Affam M, Ziggah YY (2023) A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks. Model Earth Syst Environ. https://doi.org/10.1007/s40808-023-01717-2

    Article  Google Scholar 

  43. Fernández A, Sanchidrián JA, Segarra P, Gómez S, Li E, Navarro R (2023) Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques. Int J Min Sci Technol. https://doi.org/10.1016/j.ijmst.2023.02.004

    Article  Google Scholar 

  44. Samui P (2013) Multivariate adaptive regression spline (Mars) for prediction of elastic modulus of jointed rock mass. Geotech Geol Eng 31:249–253. https://doi.org/10.1007/s10706-012-9584-4

    Article  Google Scholar 

  45. Baghbani A, Choudhury T, Samui P, Costa S (2023) Prediction of secant shear modulus and damping ratio for an extremely dilative silica sand based on machine learning techniques. Soil Dyn Earthq Eng 165:107708. https://doi.org/10.1016/j.soildyn.2022.107708

    Article  Google Scholar 

  46. Kumar S, Singh D (2023) Prediction of UCS and CBR behavior of fiber-reinforced municipal solid waste incinerator bottom ash composites using experimental and machine learning methods. Constr Build Mater 367:130230. https://doi.org/10.1016/j.conbuildmat.2022.130230

    Article  Google Scholar 

  47. Othman K, Abdelwahab H (2023) The application of deep neural networks for the prediction of California Bearing Ratio of road subgrade soil. Ain Shams Eng J 14(7):101988. https://doi.org/10.1016/j.asej.2022.101988

    Article  Google Scholar 

  48. Bherde V, Kudlur Mallikarjunappa L, Baadiga R, Balunaini U (2023) Application of machine-learning algorithms for predicting California bearing ratio of soil. J Transp Eng Part B 149(4):04023024. https://doi.org/10.1061/JPEODX.PVENG-1290

    Article  Google Scholar 

  49. Tamassoki S, Daud NNN, Wang S, Roshan MJ (2023) CBR of stabilized and reinforced residual soils using experimental, numerical, and machine-learning approaches. Transp Geotech 42:101080. https://doi.org/10.1016/j.trgeo.2023.101080

    Article  Google Scholar 

  50. Sanyal AP, Bhattacharya SP (2023) A comparative analysis between CBR based prediction models and MRA models for high-rise construction delay prediction. Int J Constr Manag. https://doi.org/10.1080/15623599.2023.2211461

    Article  Google Scholar 

  51. Nnochiri ES, Okokpujie IP, Tartibu LK (2023) Artificial neural network models for predicting California bearing ratio of lateritic soil admixed with reinforce and rice husk ash. Rev Intell Artif 37(2):305–313

    Google Scholar 

  52. Tiwari LB, Burman A, Samui P (2023) Modelling soil compaction parameters using a hybrid soft computing technique of LSSVM and symbiotic organisms search. Innov Infrastructure Solut 8(1):2. https://doi.org/10.1007/s41062-022-00966-x

    Article  Google Scholar 

  53. Ali HFH (2023) Soft computing models to predict the compaction characteristics from physical soil properties. Eng Technol J 41(5):698–715. https://doi.org/10.30684/etj.2023.137772.1360

    Article  Google Scholar 

  54. Bardhan A, Asteris PG (2023) Application of hybrid ANN paradigms built with nature inspired meta-heuristics for modelling soil compaction parameters. Transport Geotech 41:100995. https://doi.org/10.1016/j.trgeo.2023.100995

    Article  Google Scholar 

  55. Chhabra RS, Mahadeva R, Ransinchung RN, GD (2023) Unconfined compressive strength prediction of recycled cement-treated base mixes using soft computing techniques. Road Mater Pavement Des, pp 1–15. https://doi.org/10.1080/14680629.2023.2199889

  56. Skentou AD, Bardhan A, Mamou A, Lemonis ME, Kumar G, Samui P, Armaghani DJ, Asteris PG (2023) Closed-form equation for estimating unconfined compressive strength of granite from three non-destructive tests using soft computing models. Rock Mech Rock Eng 56(1):487–514. https://doi.org/10.1007/s00603-022-03046-9

    Article  Google Scholar 

  57. Ghanizadeh AR, Naseralavi SS (2023) Intelligent prediction of unconfined compressive strength and young's modulus of lean clay stabilized with iron ore mine tailings and hydrated lime using Gaussian process regression. J Soft Comput Civil Eng 7(4)

  58. Goutham DR, Krishnaiah AJ (2024) Prediction of unconfined compressive strength of expansive soil amended with bagasse ash and lime using artificial neural network. J Soft Comput Civil Eng 8(1):33–54. https://doi.org/10.22115/scce.2023.367214.1545

  59. Das SK, Samui P, Sabat AK (2011) Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil. Geotech Geol Eng 29:329–342. https://doi.org/10.1007/s10706-010-9379-4

    Article  Google Scholar 

  60. Verma G, Kumar B (2022) Artificial neural network equations for predicting the modified proctor compaction parameters of fine-grained soil. Transp Infrastructure Geotechnol. https://doi.org/10.1007/s40515-022-00228-4

    Article  Google Scholar 

  61. Taffese WZ, Abegaz KA (2022) Prediction of compaction and strength properties of amended soil using machine learning. Buildings 12(5):613. https://doi.org/10.3390/buildings12050613

    Article  Google Scholar 

  62. Pentoś K, Mbah JT, Pieczarka K, Niedbała G, Wojciechowski T (2022) Evaluation of multiple linear regression and machine learning approaches to predict soil compaction and shear stress based on electrical parameters. Appl Sci 12(17):8791. https://doi.org/10.3390/app12178791

    Article  Google Scholar 

  63. Othman K (2022) Estimation of the compaction parameters of aggregate base course using artificial neural networks. SN Appl Sci 4(10):1–16. https://doi.org/10.1007/s42452-022-05158-x

    Article  Google Scholar 

  64. V Hohn A, Leme RF, Moura TE, Llanque AGR (2022) Empirical models to predict compaction parameters for soils in the state of ceará, northeastern Brazil. Ingeniería Invest. https://doi.org/10.15446/ing.investig.v42n1.86328

    Article  Google Scholar 

  65. Arama ZA, Gençdal HB (2022) Simple regression models to estimate the standard and modified proctor characteristics of specific compacted fine-grained soils. In: Proceedings of the 7th World Congress on Civil, Structural, and Environmental Engineering (CSEE'22). https://doi.org/10.11159/icgre22.232

  66. Yousif AA, Mohamed IA (2022) Prediction of compaction parameters from soil index properties case study: dam complex of upper Atbara project. Am J Pure Appl Sci 4(1):1–9. https://doi.org/10.34104/ajpab.022.01009

    Article  MathSciNet  Google Scholar 

  67. Verma G, Kumar B (2021) Multi-layer perceptron (MLP) neural network for predicting the modified compaction parameters of coarse-grained and fine-grained soils. Innov Infrastructure Solut 7(1):1–13. https://doi.org/10.1007/s41062-021-00679-7

    Article  Google Scholar 

  68. Soltani A, Azimi M, O’Kelly BC (2021) Modeling the compaction characteristics of fine-grained soils blended with tire-derived aggregates. Sustainability 13(14):7737. https://doi.org/10.3390/su13147737

    Article  Google Scholar 

  69. Othman K (2021) Deep neural network models for the prediction of the aggregate base course compaction parameters. Designs 5(4):78. https://doi.org/10.3390/designs5040078

    Article  MathSciNet  Google Scholar 

  70. Othman K, Abdelwahab H (2021) Prediction of the soil compaction parameters using deep neural networks. Transp Infrastructure Geotechnol. https://doi.org/10.1007/s40515-021-00213-3

    Article  Google Scholar 

  71. Nwaiwu CM, Mezie EO (2021) Prediction of maximum dry unit weight and optimum moisture content for coarse-grained lateritic soils. Soils Rocks. https://doi.org/10.28927/SR.2021.054120

    Article  Google Scholar 

  72. Jalal FE, Xu Y, Iqbal M, Jamhiri B, Javed MF (2021) Predicting the compaction characteristics of expansive soils using two genetic programming-based algorithms. Transport Geotech 30:100608. https://doi.org/10.1016/j.trgeo.2021.100608

    Article  Google Scholar 

  73. Wang HL, Yin ZY (2020) High performance prediction of soil compaction parameters using multi expression programming. Eng Geol 276:105758. https://doi.org/10.1016/j.enggeo.2020.105758

    Article  Google Scholar 

  74. Verma G, Kumar B (2020) Prediction of compaction parameters for fine-grained and coarse-grained soils: a review. Int J Geotech Eng 14(8):970–977. https://doi.org/10.1080/19386362.2019.1595301

    Article  Google Scholar 

  75. Medjo M, Atega PLE (2020) Development of a new model for predicting parameters soil compaction of the Sangmelima-Mengong section

  76. Kurnaz TF, Kaya Y (2020) The performance comparison of the soft computing methods on the prediction of soil compaction parameters. Arab J Geosci 13(4):1–13. https://doi.org/10.1007/s12517-020-5171-9

    Article  Google Scholar 

  77. Ratnam UV, Prasad KN (2019) Prediction of compaction and compressibility characteristics of compacted soils. Int J Appl Eng Res 14(3):621–632

    Google Scholar 

  78. Hussain A, Atalar C (2019) March. Estimation of compaction characteristics of soils using Atterberg limits. In IOP Conference Series: Materials Science and Engineering (Vol. 800, No. 1, p. 012024). IOP Publishing, DoI https://doi.org/10.1088/1757-899X/800/1/012024.

  79. Hasnat A, Hasan MM, Islam MR, Alim MA (2019) Prediction of compaction parameters of soil using support vector regression. Curr Trends Civil Struct Eng 4(1):1–7

    Google Scholar 

  80. Ardakani A, Kordnaeij A (2019) Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm. Eur J Environ Civ Eng 23(4):449–462. https://doi.org/10.1080/19648189.2017.1304269

    Article  Google Scholar 

  81. Karimpour-Fard M, Machado SL, Falamaki A, Carvalho MF, Tizpa P (2019) Prediction of compaction characteristics of soils from index test’s results. Iran J Sci Technol Trans Civil Eng 43(1):231–248. https://doi.org/10.1007/s40996-018-0161-9

    Article  Google Scholar 

  82. Taha OME, Majeed ZH, Ahmed SM (2018) Artificial neural network prediction models for maximum dry density and optimum moisture content of stabilized soils. Transp Infrastructure Geotechnol 5(2):146–168. https://doi.org/10.1007/s40515-018-0053-2

    Article  Google Scholar 

  83. Bunyamin SA, Ijimdiya TS, Eberemu AO, Osinubi KJ (2018) Artificial neural networks prediction of compaction characteristics of black cotton soil stabilized with cement kiln dust. J Soft Comput Civil Eng 2(3):50–71. https://doi.org/10.22115/scce.2018.128634.1059

    Article  Google Scholar 

  84. Lubis AS, Muis ZA, Hastuty IP, Siregar IM (2018) Estimation of compaction parameters based on soil classification. In: IOP Conference Series: Materials Science and Engineering (Vol 306, No. 1, p 012005). IOP Publishing. https://doi.org/10.1088/1757-899X/306/1/012005

  85. Khalid U, Rehman ZU (2018) Evaluation of compaction parameters of fine-grained soils using standard and modified efforts. Int J Geo-Eng 9(1):1–17. https://doi.org/10.1186/s40703-018-0083-1

    Article  Google Scholar 

  86. Günaydin O, Özbeyaz A, Söylemez M (2018) Regression analysis of soil compaction parameters using support vector method. Celal Bayar Univ J Sci 14(4):443–447. https://doi.org/10.18466/cbayarfbe.449644

    Article  Google Scholar 

  87. Sanuade OA, Adesina RB, Amosun JO, Fajana AO, Olaseeni OG (2017) Using artificial neural network to predict dry density of soil from thermal conductivity. In: RMZ-M&G, 64, pp.237–012. https://doi.org/10.1515/rmzmag-2017-0012

  88. Rehman AU, Farooq K, Mujtaba H (2017) Prediction of California bearing ratio (CBR) and compaction characteristics of granular soils. Acta Geotech Slovenica 14(1):63–72

    Google Scholar 

  89. Ranasinghe RATM, Jaksa MB, Kuo YL, Nejad FP (2017) Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results. J Rock Mech Geotech Eng 9(2):340–349. https://doi.org/10.1016/j.jrmge.2016.11.011

    Article  Google Scholar 

  90. Anjita, N.A., George, C.A. and Krishnankutty, S., 2017. Prediction of maximum dry density of soil using genetic algorithm. Int. J. Eng. Res, 6(10.17577).

  91. Sulewska MJ (2017) Applying artificial neural networks for analysis of geotechnical problems. Comput Assist Methods Eng Sci 18(4):231–241

    Google Scholar 

  92. Suman S, Mahamaya M, Das SK (2016) Prediction of maximum dry density and unconfined compressive strength of cement stabilised soil using artificial intelligence techniques. Int J Geosynth Ground Eng 2(2):1–11. https://doi.org/10.1007/s40891-016-0051-9

    Article  Google Scholar 

  93. Shrivastava K, Jain PK, Azad M (2016) Prediction of compaction parameters using regression and ANN tools. Int J Sci Res Dev 3(11):2321–613

    Google Scholar 

  94. Tizpa P, Jamshidi Chenari R, Karimpour Fard M, Lemos Machado S (2015) ANN prediction of some geotechnical properties of soil from their index parameters. Arab J Geosci 8(5):2911–2920. https://doi.org/10.1007/s12517-014-1304-3

    Article  Google Scholar 

  95. Tenpe A, Kaur S (2015) Artificial neural network modeling for predicting compaction parameters based on index properties of soil. Int J Sci Res 4(7):1198–1202

    Google Scholar 

  96. Ajalloeian R, Mojtaba K (2015) Predicting maximum dry density, optimum moisture content and California bearing ratio (CBR) through soil index using ordinary least squares (OLS) and artificial neural networks (ANNS). Int J Innov Technol Explor Eng 3(3):1–5

    Google Scholar 

  97. KS N, Chew YM, Osman MH, SK MG (2015) Estimating maximum dry density and optimum moisture content of compacted soils. Int Conf Adv Civil Environ Eng 2015:1–8

    Google Scholar 

  98. Khuntia S, Mujtaba H, Patra C, Farooq K, Sivakugan N, Das BM (2015) Prediction of compaction parameters of coarse grained soil using multivariate adaptive regression splines (MARS). Int J Geotech Eng 9(1):79–88. https://doi.org/10.1179/1939787914Y.0000000061

    Article  Google Scholar 

  99. Jayan J, Sankar N (2015) Prediction of compaction parameters of soils using artificial neural network. Asian J Eng Technol 3(4)

  100. Jamshidi Chenari R, Tizpa P, Ghorbani Rad MR, Machado SL, Karimpour Fard M (2015) The use of index parameters to predict soil geotechnical properties. Arab J Geosci 8(7):4907–4919. https://doi.org/10.1007/s12517-014-1538-0

    Article  Google Scholar 

  101. Farooq K, Khalid U, Mujtaba H (2015) Prediction of compaction characteristics of fine-grained soils using consistency limits. Arab J Sci Eng 41(4):1319–1328. https://doi.org/10.1007/s13369-015-1918-0

    Article  Google Scholar 

  102. Majidi A, Lashgaripour G, Shoaie Z, Nashlaji MN, Firouzei Y (2014) Estimating compaction parameters of marl soils using multi-layer perceptron neural networks. J Balkan Tribol Assoc 20(2):170–198

    Google Scholar 

  103. Viji VK, Lissy KF, Sobha C, Benny MA (2013) Predictions on compaction characteristics of fly ashes using regression analysis and artificial neural network analysis. Int J Geotech Eng 7(3):282–291. https://doi.org/10.1179/1938636213Z.00000000036

    Article  Google Scholar 

  104. Taghavifar H, Mardani A, Taghavifar L (2013) A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility. Measurement 46(8):2288–2299. https://doi.org/10.1016/j.measurement.2013.04.077

    Article  Google Scholar 

  105. Sivrikaya O, Kayadelen C, Cecen E (2013) Prediction of the compaction parameters for coarse-grained soils with fines content by MLA and GEP. Acta Geotech Slovenica 10(2):29–41

    Google Scholar 

  106. Mujtaba H, Farooq K, Sivakugan N, Das BM (2013) Correlation between gradational parameters and compaction characteristics of sandy soils. Int J Geotech Eng 7(4):395–401

    Google Scholar 

  107. Al-saffar R, Khattab S (2013) Prediction of soil’s compaction parameter using artificial neural network. Al-Rafidain Engineering Journal (AREJ) 21(3):15–27. https://doi.org/10.33899/rengj.2013.75444

    Article  Google Scholar 

  108. Isik F, Ozden G (2013) Estimating compaction parameters of fine-and coarse-grained soils by means of artificial neural networks. Environ Earth Sci 69(7):2287–2297. https://doi.org/10.1007/s12665-012-2057-5

    Article  Google Scholar 

  109. Sivrikaya O, Soycan TY (2011) Estimation of compaction parameters of fine-grained soils in terms of compaction energy using artificial neural networks. Int J Numer Anal Methods Geomech 35(17):1830–1841. https://doi.org/10.1002/nag.981

    Article  Google Scholar 

  110. Patra C, Sivakugan N, Das B, Rout S (2010) Correlations for relative density of clean sand with median grain size and compaction energy. Int J Geotech Eng 4(2):195–203. https://doi.org/10.3328/IJGE.2010.04.02.195-203

    Article  Google Scholar 

  111. Hossein Alavi A, Hossein Gandomi A, Mollahassani A, Akbar Heshmati A, Rashed A (2010) Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci 173(3):368–379. https://doi.org/10.1002/jpln.200800233

    Article  Google Scholar 

  112. Günaydın OJEG (2009) Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. Environ Geol 57(1):203–215. https://doi.org/10.1007/s00254-008-1300-6

    Article  Google Scholar 

  113. Di Matteo L, Bigotti F, Ricco R (2009) Best-fit models to estimate modified proctor properties of compacted soil. J Geotech Geoenviron Eng 135(7):992–996. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000022

    Article  Google Scholar 

  114. Alavi AH, Gandomi AH, Gandomi M, Sadat Hosseini SS (2009) Prediction of maximum dry density and optimum moisture content of stabilised soil using RBF neural networks. IES J Part A Civil Struct Eng 2(2):98–106. https://doi.org/10.1080/19373260802659226

    Article  Google Scholar 

  115. 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 

  116. Sani JE, Moses G, Etim RK, Adebayo K, Kevin OK, Mbogu OC (2022) Modeling of California bearing ratio with basic engineering properties along East-West Road-Section II (Port Harcourt to Kaiama). ATBU J Sci, Technol Educ 9(4):138–151

    Google Scholar 

  117. Othman K, Abdelwahab H (2022) The application of deep neural networks for the prediction of California Bearing FRatio of road subgrade soil. Ain Shams Eng J. https://doi.org/10.1016/j.asej.2022.101988

    Article  Google Scholar 

  118. Okonkwo UN, Ekeoma EC, Ndem HE (2022) Exponential logarithmic models for strength properties of lateritic soil treated with cement and rice husk ash as pavement of low-cost roads. Int J Pavement Res Technol. https://doi.org/10.1007/s42947-021-00134-x

    Article  Google Scholar 

  119. Xiao-xia L (2022) Predicting California-bearing capacity value of stabilized pond ash with lime and lime sludge applying hybrid optimization algorithms. Multiscale Multidisc Model Exp Des 5(2):157–166. https://doi.org/10.1007/s41939-021-00109-2

    Article  Google Scholar 

  120. Khasawneh MA, Al-Akhrass HI, Rabab’ah SR, Al-sugaier AO (2022) Prediction of California bearing ratio using soil index properties by regression and machine-learning techniques. Int J Pavement Res Technol. https://doi.org/10.1007/s42947-022-00237-z

    Article  Google Scholar 

  121. Ho LS, Tran VQ (2022) Machine learning approach for predicting and evaluating California bearing ratio of stabilized soil containing industrial waste. J Clean Prod 370:133587. https://doi.org/10.1016/j.jclepro.2022.133587

    Article  Google Scholar 

  122. Hassan J, Alshameri B, Iqbal F (2022) Prediction of California bearing ratio (CBR) using index soil properties and compaction parameters of low plastic fine-grained soil. Transp Infrastructure Geotechnol 9(6):764–776. https://doi.org/10.1007/s40515-021-00197-0

    Article  Google Scholar 

  123. Gökova S (2022) Investigation of the correlation between California bearing ratio and shear strength of pavement subgrade material with different water contents. J Innov Transp 3(1):1–7. https://doi.org/10.53635/jit.1097757

    Article  Google Scholar 

  124. Encinares ES, Krizzia J, Encela D (2022) Prediction of California bearing ratio (CBR) using dynamic cone penetrometer (DCP) for soils from second district in the province of Sorsogon. United Int J Res Technol 3(5):12–16

    Google Scholar 

  125. Bakri, M., Aldhari, I. and Alfawzan, M.S., 2022. Prediction of California Bearing Ratio of Granular Soil by Multivariate Regression and Gene Expression Programming. Advances in Civil Engineering, 2022, https://doi.org/10.1155/2022/7426962.

  126. Akinwamide OG, Igo JA, Akenwamidi JT (2022) Study on the relationship between California bearing ratio (CBR) and resilient modulus (MR). J Prog Civil Eng ISSN 2322:0856. https://doi.org/10.53469/jpce.2022.04(02).02

    Article  Google Scholar 

  127. Bardhan A, Gokceoglu C, Burman A, Samui P, Asteris PG (2021) Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions. Eng Geol 291:106239. https://doi.org/10.1016/j.enggeo.2021.106239

    Article  Google Scholar 

  128. Vu DQ, Nguyen DD, Bui QAT, Trong DK, Prakash I, Pham BT (2021) Estimation of California bearing ratio of soils using random forest based machine learning. J Science Transport Technol 48–61

  129. Trong DK, Pham BT, Jalal FE, Iqbal M, Roussis PC, Mamou A, Ferentinou M, Vu DQ, Duc Dam N, Tran QA, Asteris PG (2021) On random subspace optimization-based hybrid computing models predicting the california bearing ratio of soils. Materials 14(21):6516. https://doi.org/10.3390/ma14216516

    Article  Google Scholar 

  130. Quan, V. and Do, H.Q., 2021. Prediction of California bearing ratio (CBR) of stabilized expansive soils with agricultural and industrial waste using light gradient boosting machine. Journal of Science and Transport Technology, pp.1–9.

  131. Timani KL, Jain RK (2021) Assessment of soaked california bearing ratio of clay-gravel mixtures using artificial neural network modeling. In: Proceedings of the Indian Geotechnical Conference 2019, pp 437–445. Springer, Singapore. https://doi.org/10.1007/978-981-33-6466-0_40

  132. Rashed KA, Salih NB, Abdalla TA (2021) Prediction of California bearing ratio from consistency and compaction characteristics of fine-grained soils. Al-Nahrain J Eng Sci 24(2):123–129. https://doi.org/10.29194/NJES.24020123

    Article  Google Scholar 

  133. Rajakumar C, Rao PKR, Babu GR, Sreenivasulu A (2021) Experimental and numerical prediction of California bearing ratio of expansive soil stabilized by bagasse ash and geotextile reinforcement. In: IOP Conference Series: Earth and Environmental Science (vol 796, No. 1, p 012057). IOP Publishing. https://doi.org/10.1088/1755-1315/796/1/012057.

  134. Raja MNA, Shukla SK, Khan MUA (2021) An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil. Int J Pavement Eng. https://doi.org/10.1080/10298436.2021.1904237

    Article  Google Scholar 

  135. Mohammed Y, Paulmakesh A, Admasu B, Shukri S (2021) Relationship between California Bearing Ratio and Other Geotechnical Properties of Sub grade Soils. In: Journal of Physics: Conference Series (Vol 2040, No. 1, p 012029). IOP Publishing. https://doi.org/10.1088/1742-6596/2040/1/012029.

  136. Haupt FJ, Netterberg F (2021) Prediction of California Bearing Ratio and compaction characteristics of Transvaal soils from indicator properties. Journal of the South African Institution of Civil Engineering 63(2):47–56. https://doi.org/10.17159/2309-8775/2021/v63n2a6

    Article  Google Scholar 

  137. Gül, Y. and Çayir, H.M., 2021, December. Prediction of the California bearing ratio from some field measurements of soils. In Proceedings of the Institution of Civil Engineers-Municipal Engineer (Vol. 174, No. 4, pp. 241–250). Thomas Telford Ltd, https://doi.org/10.1680/jmuen.19.00020.

  138. Ambrose P, Rimoy S (2021) Prediction of four-days Soaked California Bearing Ratio (CBR) Values from Soil Index Properties. Tanzania J Eng Technol 40(1)

  139. Alzabeebee S, Mohamad SA, Al-Hamd RKS (2021) Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve. Road Mater Pavement Des. https://doi.org/10.1080/14680629.2021.1995471

    Article  Google Scholar 

  140. Ai X, Yi J, Zhao H, Chen S, Luan H, Zhang L, Feng D (2021) An empirical predictive model for the dynamic resilient modulus based on the static resilient modulus and California bearing ratio of cement-and lime-stabilised subgrade soils. Road Mater Pavement Des 22(12):2818–2837. https://doi.org/10.1080/14680629.2020.1808519

    Article  Google Scholar 

  141. Lakshmi SM, Geetha S, Selvakumar M (2021) Predicting soaked CBR of SC subgrade from dry density for light and heavy compaction. Mater Today 45:1664–1670. https://doi.org/10.1016/j.matpr.2020.08.558

    Article  Google Scholar 

  142. Lakshmi SM, Gani MA, Kamalesh V, Mahalakshmi V, Padmesh PM (2021) Correlating unsoaked CBR with UCC strength for SC and SP soil. Mater Today 43:1293–1303. https://doi.org/10.1016/j.matpr.2020.09.029

    Article  Google Scholar 

  143. Attah IC, Okafor FO, Ugwu OO (2021) Optimization of California bearing ratio of tropical black clay soil treated with cement kiln dust and metakaolin blend. International Journal of Pavement Research and Technology 14(6):655–667. https://doi.org/10.1007/s42947-020-0003-6

    Article  Google Scholar 

  144. Tesfaye B, Potdar AM (2020) Prediction of California bearing ratio of a black cotton soil stabilized with waste glass and eggshell powder using artificial neural network (Doctoral dissertation, Addis Ababa Science and Technology University)

  145. Tenpe AR, Patel A (2020) Utilization of support vector models and gene expression programming for soil strength modeling. Arab J Sci Eng 45(5):4301–4319. https://doi.org/10.1007/s13369-020-04441-6

    Article  Google Scholar 

  146. Islam, M.R. and Roy, A.C., 2020. Prediction of California bearing ratio of fine-grained soil stabilized with admixtures using soft computing systems. Journal of Civil Engineering, Science and Technology, 11(1), pp.28–44, https://doi.org/10.33736/jcest.2035.2020.

  147. Nagaraju TV, Prasad C, Raju MJ (2020) Prediction of California bearing ratio using particle swarm optimization. In: Soft Computing for Problem Solving, pp 795–803. Springer, Singapore, https://doi.org/10.1007/978-981-15-0035-0_65

  148. Alam SK, Mondal A, Shiuly A (2020) Prediction of CBR value of fine grained soils of Bengal Basin by genetic expression programming, artificial neural network and krigging method. J Geol Soc India 95(2):190–196. https://doi.org/10.1007/s12594-020-1409-0

    Article  Google Scholar 

  149. Taha S, Gabr A, El-Badawy S (2019) Regression and neural network models for California bearing ratio prediction of typical granular materials in Egypt. Arab J Sci Eng 44(10):8691–8705. https://doi.org/10.1007/s13369-019-03803-z

    Article  Google Scholar 

  150. Sagar KA, Satyanarayana PVV, Vaidyanathan GK. Prediction of California bearing ratio values from gradation and plasticity features of red gravel soils. IOSR J Eng 9(2):51–54

  151. Reddy S, Ruchita N, Sharma P, Satyanarayana SV (2019) Prediction of California bearing ratio through empirical correlations of index properties for tropical indian soils. Int J Innov Eng Technol 15(1):67–77

    Google Scholar 

  152. Ravichandra AH, Shivakumar K, Vinaykumar H, Khalid M, Basavarah B (2019) Prediction of CBR value by using index properties of soil. Int Res J Eng Technol 6(7):3740–3747

    Google Scholar 

  153. Onyelowe K, Alaneme G, Igboayaka C, Orji F, Ugwuanyi H, Van DB, Van MN (2019) Scheffe optimization of swelling, California bearing ratio, compressive strength, and durability potentials of quarry dust stabilized soft clay soil. Mater Sci Energy Technol 2(1):67–77. https://doi.org/10.1016/j.mset.2018.10.005

    Article  Google Scholar 

  154. Nujid MM, Idrus J, Azam NA, Tholibon DA, Jamaluddin D (2019) Correlation between california bearing ratio (CBR) with plasticity index of marine stabilizes soil with cockle shell powder. In: Journal of Physics: Conference Series (Vol. 1349, No. 1, p. 012036). IOP Publishing, https://doi.org/10.1088/1742-6596/1349/1/012036

  155. Kurnaz TF, Kaya Y (2019) Prediction of the California bearing ratio (CBR) of compacted soils by using GMDH-type neural network. Eur Phys J Plus 134(7):326. https://doi.org/10.1140/epjp/i2019-12692-0

    Article  Google Scholar 

  156. Khatri, D.P., Acharya, I.P. and Dhakal, B.B., 2019. Correlation of California Bearing Ratio with Index Properties of Sub-Grade Soil: A Case Study on Thankot Chitlang Road Section. In Proceedings of IOE Graduate Conference (Vol. 7, pp. 85–89).

  157. Kayode-Ojo N (2019) Regression Modelling of California Bearing Ratio (CBR) Predicted from Index Properties for Lateritic Soils. Global Journals of Research in Engineering 19(E4):39–55

    Google Scholar 

  158. Katte VY, Mfoyet SM, Manefouet B, Wouatong ASL, Bezeng LA (2019) Correlation of California bearing ratio (CBR) value with soil properties of road subgrade soil. Geotech Geol Eng 37(1):217–234. https://doi.org/10.1007/s10706-018-0604-x

    Article  Google Scholar 

  159. Günaydin, O., Ozbeyaz, A. and Soylemez, M., 2019. Estimating California Bearing Ratio Using Decision Tree Regression Analysis Using Soil Index and Compaction Parameters. International Journal of Intelligent Systems and Applications in Engineering, 7(1), pp.30–33, https://doi.org/10.18201/ijisae.2019151249.

  160. Roy AC (2018) Prediction of California bearing ratio of fine-grained soil stabilized with admixtures (Doctoral dissertation, Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh)

  161. Narzary BK, Ahamad KU (2018) Estimating elastic modulus of California bearing ratio test sample using finite element model. Constr Build Mater 175:601–609. https://doi.org/10.1016/j.conbuildmat.2018.04.228

    Article  Google Scholar 

  162. González Farias I, Araujo W, Ruiz G (2018) Prediction of California bearing ratio from index properties of soils using parametric and non-parametric models. Geotech Geol Eng 36(6):3485–3498. https://doi.org/10.1007/s10706-018-0548-1

    Article  Google Scholar 

  163. Shaban A, Cosentino P (2017) Characterizing structural performance of unbound pavement materials using miniaturized pressuremeter and California bearing ratio tests. J Test Eval 45(13):1–18

    Google Scholar 

  164. Rehman ZU, Khalid U, Farooq K, Mujtaba H (2017) Prediction of CBR value from index properties of different soils. Technol J Univ Eng Technol (UET) 22:17–26

    Google Scholar 

  165. El Amin, B.M., Abdeldjalil, Z., Abedlkader, D. and Abderrahmen, B., 2017. Design of neural networks by using genetic algorithm for the prediction of immersed CBR index, Long-Term Behaviour and Environmentally Friendly Rehabilitation Technologies of Dams. doi, 10, pp.978–3.

  166. Egbe JG, Ewa DE, Ubi SE, Ikwa GB, Tumenayo OO (2017) Application of multilinear regression analysis in modeling of soil properties for geotechnical civil engineering works in Calabar South. Niger J Technol 36(4):1059–1065. https://doi.org/10.4314/njt.v36i4.10

    Article  Google Scholar 

  167. Abdella D, Abebe T, Quezon ET (2017) Regression analysis of index properties of soil as strength determinant for California bearing ratio (CBR). GSJ 5(6):1

    Google Scholar 

  168. Roy S (2016) Assessment of soaked California bearing ratio value using geotechnical properties of soils. Resour Environ 6(4):80–87

    Google Scholar 

  169. Pradeep KKJ, Harish PYM (2016) Soft computing technique for prediction of CBR from index properties of subgrade soil. Int J Innov Res Sci Eng Technol 5(7):13852–13860

    Google Scholar 

  170. Mason GL, Baylot EA (2016) Predicting soil strength in terms of cone index and california bearing ratio for trafficability

  171. Janjua ZS, Chand J (2016) Correlation of CBR with index properties of soil. International Journal of Civil Engineering and Technology 7(5):57–62

    Google Scholar 

  172. Chandrakar, V. and Yadav, R.K., 2016. Study of correlation of CBR value with engineering properties and index properties of coarse grained soil. Internat. Res. Jour. Engg. Tech (IRJET), v. ol, 3, pp.772–778.

  173. Ali B, Rahman MA, Rafizul IM (2016) Prediction of California bearing ratio of stabilized soil using artificial neural network. In: Proceedings of the 3rd International Conference on Civil Engineering for Sustainable Development (ICCESD 2016)

  174. Vadi PK, Manjula C, Poornima P (2015) Artificial neural networks (ANNS) for prediction of California bearing ratio of soils. Int J Mod Eng Res 5(1):15–21

    Google Scholar 

  175. Ul-Rehman A, Farooq K, Mujtaba H, Altaf O (2015) Estimation of California bearing ratio (CBR) from index properties and compaction characteristics of coarse grained soil. Sci Int (Lahore) 27(6):6207–6210

    Google Scholar 

  176. Puri N, Jain A (2015) Correlation between California bearing ratio and index properties of silt and clay of low compressibility. In: Proceedings of Fifth Indian Young Geotechnical Engineers Conference, Vadodara

  177. Jiang, Y., Wong, L.N.Y. and Ren, J., 2015. A numerical test method of California bearing ratio on graded crushed rocks using particle flow modeling. journal of traffic and transportation engineering (english edition), 2(2), pp.107–115. https://doi.org/10.1016/j.jtte.2015.02.004.

  178. Harini H, Naagesh S (2014) Predicting CBR of fine grained soils by artificial neural network and multiple linear regression. International Journal of Civil Engineering 5(2):119–126

    Google Scholar 

  179. Bhatt S, Jain PK, Pradesh M (2014) Prediction of California bearing ratio of soils using artificial neural network. American International Journal of Research in Science, Technology, Engineering & Mathematics 8(2):156–161

    Google Scholar 

  180. Sabat AK (2013) Prediction of California bearing ratio of a soil stabilized with lime and quarry dust using artificial neural network. Electron J Geotech Eng 18:3261–3272

    Google Scholar 

  181. Alawi M, Rajab M (2013) Prediction of California bearing ratio of subbase layer using multiple linear regression models. Road Mater Pavement Des 14(1):211–219. https://doi.org/10.1080/14680629.2012.757557

    Article  Google Scholar 

  182. Al-Refeai T, Al-Suhaibani A (1997) Prediction of CBR using dynamic cone penetrometer. J King Saud Univ 9(2):191–203. https://doi.org/10.1016/S1018-3639(18)30676-7

    Article  Google Scholar 

  183. Black WPM (1962) A method of estimating the California bearing ratio of cohesive soils from plasticity data. Geotechnique 12(4):271–282. https://doi.org/10.1680/geot.1962.12.4.271

    Article  Google Scholar 

  184. Datta, T. and Chottopadhyay, B.C., 2011, December. Correlation between CBR and index properties of soil. In Proceedings of Indian Geotechnical Conference, Kochi (pp. 131–133).

  185. Divinsky M, Ishai I, Livneh M (1998) Probabilistic approach to pavement design based on generalized California bearing ratio equation. J Transp Eng 124(6):582–588. https://doi.org/10.1061/(ASCE)0733-947X(1998)124:6(582)

    Article  Google Scholar 

  186. Joseph, D. and Vipulanandan, C., 2011. Characterization of field compacted soils (unsoaked) using the California Bearing Ratio (CBR) test. In Geo-Frontiers 2011: Advances in Geotechnical Engineering (pp. 2719–2728), https://doi.org/10.1061/41165(397)278.

  187. Roy TK, Chattopadhyay BC, Roy SK (2010) California bearing ratio, evaluation and estimation: a study of comparison. In: Proceedings of the Indian Geotechnical Conference, Geotrendz, Mumbai (pp 19–22)

  188. Taskiran T (2010) Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv Eng Softw 41(6):886–892. https://doi.org/10.1016/j.advengsoft.2010.01.003

    Article  Google Scholar 

  189. Venkatasubramanian C, Dhinakaran G (2011) ANN model for predicting CBR from index properties of soils. Int J Civ Struct Eng 2(2):614–620

    Google Scholar 

  190. Yildirim B, Gunaydin O (2011) Estimation of California bearing ratio by using soft computing systems. Expert Syst Appl 38(5):6381–6391. https://doi.org/10.1016/j.eswa.2010.12.054

    Article  Google Scholar 

  191. Khatti, J. and Grover, K.S., 2022c. Prediction of soaked CBR of fine-grained soils using soft computing techniques. Multiscale and Multidisciplinary Modeling, Experiments and Design, pp.1–25, https://doi.org/10.1007/s41939-022-00131-y.

  192. Chakraborty A, Goswami A (2021) Prediction of California bearing ratio (CBR) from index properties of fine-grained soil. Geotech Eng J SEAGS & AGSSEA 52(4):57–64

    Google Scholar 

  193. Bourouis Mohammed el Amin, Zadjaoui A., Djedid A., Bensenouci A., 2017, Design of neural networks by using genetic algorithm for the prediction of immersed CBR index, Long-Term Behaviour and Environmentally Friendly Rehabilitation Technologies of Dams, 331–338, DoI:https://doi.org/10.3217/978-3-85125-564-5-046.

  194. Saadat M, Bayat M (2022) Prediction of the unconfined compressive strength of stabilised soil by adaptive neuro fuzzy inference system (ANFIS) and non-linear regression (NLR). Geomech Geoeng 17(1):80–91. https://doi.org/10.1080/17486025.2019.1699668

    Article  Google Scholar 

  195. Yildirim E, Avci E, Tanbay NA (2021) Prediction of unconfined compressive strength of microfine cement injected sands using fuzzy logic method. https://doi.org/10.21203/rs.3.rs-232296/v1.

  196. Udeala RC, Onyelowe KC, Uranta JDC, Keke EO, Alaneme GU (2021) ANFIS model of the UCS of modified soil for construction purposes In: Laryea S, Essah E (eds) Procs West Africa Built Environment Research (WABER) Conference, 9–11 August 2021, Accra, Ghana, pp 163–176

  197. Tabarsa A, Latifi N, Osouli A, Bagheri Y (2021) Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines. Front Struct Civ Eng 15(2):520–536. https://doi.org/10.1007/s11709-021-0689-9

    Article  Google Scholar 

  198. Premarathne RPPK, Sawangsuriya A (2021) Prediction of unconfined compressive strength of cement stabilized pavement materials. In: IOP Conference Series: Materials Science and Engineering (Vol 1075, No. 1, p 012008). IOP Publishing. https://doi.org/10.1088/1757-899X/1075/1/012008.

  199. Oljira, S.A., Tsige, D. and Quezon, E.T., 2021. Modeling Unconfined Compressive Strength of fine grained soils: Application of Dynamic Cone Penetration to predict foundation soil strength. Applied Journal of Environmental Engineering Science, 7(4), pp.7–4, https://doi.org/10.48422/IMIST.PRSM/ajees-v7i4.28114.

  200. Ngo HTT, Pham TA, Vu HLT, Giap LV (2021) Application of artificial intelligence to determined unconfined compressive strength of cement-stabilized soil in vietnam. Appl Sci 11(4):1949. https://doi.org/10.3390/app11041949

    Article  Google Scholar 

  201. Kardani N, Zhou A, Shen SL, Nazem M (2021) Estimating unconfined compressive strength of unsaturated cemented soils using alternative evolutionary approaches. Transportation Geotechnics 29:100591. https://doi.org/10.1016/j.trgeo.2021.100591

    Article  Google Scholar 

  202. Iqbal M, Onyelowe KC, Jalal FE (2021) Smart computing models of California bearing ratio, unconfined compressive strength, and resistance value of activated ash-modified soft clay soil with adaptive neuro-fuzzy inference system and ensemble random forest regression techniques. Multiscale and Multidisciplinary Modeling, Experiments and Design 4(3):207–225. https://doi.org/10.1007/s41939-021-00092-8

    Article  Google Scholar 

  203. Do HD, Pham VN, Nguyen HH, Huynh PN, Han J (2021) Prediction of unconfined compressive strength and flexural strength of cement-stabilized sandy soils: a case study in Vietnam. Geotech Geol Eng 39(7):4947–4962. https://doi.org/10.1007/s10706-021-01805-z

    Article  Google Scholar 

  204. Saputra NA, Putra R (2020) The correlation between CBR (California Bearing Ratio) and UCS (Unconfined Compression Strength) laterite soils in palangka raya as heap material. In: IOP Conference Series: Earth and Environmental Science, vol 469(1), p 012093. IOP Publishing. https://doi.org/10.1088/1755-1315/469/1/012093

  205. Salahudeen AB, Sadeeq JA, Badamasi A, Onyelowe KC (2020) Prediction of unconfined compressive strength of treated expansive clay using back-propagation artificial neural networks. Nigerian J Eng 27(1):ISSN0794-4756

    Google Scholar 

  206. Priyadarshee A, Chandra S, Gupta D, Kumar V (2020) Neural Models for Unconfined Compressive Strength of Kaolin clay mixed with pond ash, rice husk ash and cement. J Soft Comput Civil Eng 4(2):85–102. https://doi.org/10.22115/SCCE.2020.223774.1189

    Article  Google Scholar 

  207. Ly HB, Thai Pham B (2020) Soil unconfined compressive strength prediction using random forest (RF) machine learning model. Open Constr Build Technol J. https://doi.org/10.2174/1874836802014010278

    Article  Google Scholar 

  208. Le HA, Nguyen TA, Nguyen DD, Prakash I (2020) Prediction of soil unconfined compressive strength using artificial neural network model. Vietnam J Earth Sci 42(3):255–264. https://doi.org/10.15625/0866-7187/42/3/15342

    Article  Google Scholar 

  209. Alshkane YM, Rashed KA, Daoud HS (2020) Unconfined compressive strength (UCS) and compressibility indices predictions from dynamic cone penetrometer index (DCP) for cohesive soil in Kurdistan Region/Iraq. Geotech Geol Eng 38(4):3683–3695. https://doi.org/10.1007/s10706-020-01245-1

    Article  Google Scholar 

  210. Tinoco J, Alberto A, da Venda P, Gomes Correia A, Lemos L (2020) A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures. Neural Comput Appl 32(13):8985–8991. https://doi.org/10.1007/s00521-019-04399-z

    Article  Google Scholar 

  211. Javdanian H, Lee S (2019) Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: a computational intelligence approach. Engineering with Computers 35(1):191–199. https://doi.org/10.1007/s00366-018-0592-8

    Article  Google Scholar 

  212. González J, Saldaña M, Arzúa J (2019) Analytical model for predicting the UCS from P-wave velocity, density, and porosity on saturated limestone. Appl Sci 9(23):5265. https://doi.org/10.3390/app9235265

    Article  Google Scholar 

  213. Soleimani S, Rajaei S, Jiao P, Sabz A, Soheilinia S (2018) New prediction models for unconfined compressive strength of geopolymer stabilized soil using multi-gen genetic programming. Measurement 113:99–107. https://doi.org/10.1016/j.measurement.2017.08.043

    Article  Google Scholar 

  214. Senoon AAA, Hussein MM (2018) Correlation between unconfined compression strength (UCS) and index properties of soil in Assiut Governorate, Egypt. In: Fifteenth International Conference on Structural and Geotechnical Engineering

  215. Ghorbani A, Hasanzadehshooiili H (2018) Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing. Soils Found 58(1):34–49. https://doi.org/10.1016/j.sandf.2017.11.002

    Article  Google Scholar 

  216. Sharma LK, Singh TN (2018) Regression-based models for the prediction of unconfined compressive strength of artificially structured soil. Eng Comput 34(1):175–186. https://doi.org/10.1007/s00366-017-0528-8

    Article  Google Scholar 

  217. Dirriba, A., 2017. Developing Correlation Between Dynamic Cone Penetration Index (DCPI) and Unconfined Compression Strength (UCS) of the Soils in Alem Gena Town. Civil Engineering (Geotechnical Engineering).

  218. Adroja PB, Solanki RV, Shah YU (2017) Development of correlation between different engineering properties of subgrade soil. J Emerg Technol Innov Res 4(5):177–180

    Google Scholar 

  219. Mozumder RA, Laskar AI (2015) Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network. Comput Geotech 69:291–300. https://doi.org/10.1016/j.compgeo.2015.05.021

    Article  Google Scholar 

  220. Mahamaya M, Suman S, Anand A, Das SK (2015) Prediction of UCS and CBR values of cement stabilised mine overburden and fly ash mixture. Procedia Earth Planet Sci 11:294–302. https://doi.org/10.1016/j.proeps.2015.06.064

    Article  Google Scholar 

  221. Khalid U, Rehman Z, Farooq K, Mujtaba H (2015) Prediction of unconfined compressive strength from index properties of soils. Sci Int (Lahore) 27(5):4071–4075

    Google Scholar 

  222. Al-Neami MAM, Ami M (2015) Prediction of unconfined compressive strength of soil using artificial neural network. In: The 2nd International Conference of Buildings, Construction and Environmental Engineering (BCEE2-2015)

  223. Motamedi S, Song KI, Hashim R (2015) Prediction of unconfined compressive strength of pulverized fuel ash–cement–sand mixture. Mater Struct 48(4):1061–1073. https://doi.org/10.1617/s11527-013-0215-1

    Article  Google Scholar 

  224. Udo E, Udoh N, Kennedy C (2014) Composite stabilization and model prediction of CBR and UCS parameters of Unyeghe residual soils, AkwaIbom State, Nigeria

  225. Correia, A.A.S., Venda Oliveira, P.J. and Lemos, L.J.L., 2013, September. Prediction of the unconfined compressive strength in soft soil chemically stabilized. In Proc., 18th Int. Conf. on Soil Mechanics and Geotechnical Engineering (pp. 2457–2460). Paris: Presses des Ponts.

  226. Arumugam, R., 2013. Correlation Between Liquidity Index (LI) and Unconfined Compressive Strength of Stabilized Silty Clay as Subgrade (Doctoral dissertation, Universiti Teknologi Malaysia).

  227. Gunaydin O, Gokoglu A, Fener M (2010) Prediction of artificial soil’s unconfined compression strength test using statistical analyses and artificial neural networks. Adv Eng Softw 41(9):1115–1123. https://doi.org/10.1016/j.advengsoft.2010.06.008

    Article  Google Scholar 

  228. Kalkan E, Akbulut S, Tortum A, Celik S (2009) Prediction of the unconfined compressive strength of compacted granular soils by using inference systems. Environ Geol 58(7):1429–1440. https://doi.org/10.1007/s00254-008-1645-x

    Article  Google Scholar 

  229. Liu SY, Zhang DW, Liu ZB, Deng YF (2008) Assessment of unconfined compressive strength of cement stabilized marine clay. Mar Georesour Geotechnol 26(1):19–35. https://doi.org/10.1080/10641190801937916

    Article  Google Scholar 

  230. Narendra BS, Sivapullaiah PV, Suresh S, Omkar SN (2006) Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: a comparative study. Comput Geotech 33(3):196–208. https://doi.org/10.1016/j.compgeo.2006.03.006

    Article  Google Scholar 

  231. Kardani N, Bardhan A, Kim D, Samui P, Zhou A (2021) Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO. Journal of Building Engineering 35:102105. https://doi.org/10.1016/j.jobe.2020.102105

    Article  Google Scholar 

  232. Khatti J, Grover KS (2021) Computation of Permeability of Soil using Artificial Intelligence Approaches. International Journal of Engineering and Advanced Technology 11(1):257–266. https://doi.org/10.35940/ijeat.A3220.1011121

    Article  Google Scholar 

  233. Khatti, J. and Grover, K.S., 2022a, Determination of Suitable Hyperparameters of Artificial Neural Network for the Best Prediction of Geotechnical Properties of Soil, International Journal for Research in Applied Science and Engineering Technology, 10(5), p.4934–4961, https://doi.org/10.22214/ijraset.2022.43662.

  234. Hair, J.F., Ortinau, D.J. and Harrison, D.E., 2013. Essentials of marketing research (Vol. 2). New York, NY: McGraw-Hill/Irwin.

  235. Bardhan A, Samui P (2022) Probabilistic slope stability analysis of Heavy-haul freight corridor using a hybrid machine learning paradigm. Transportation Geotechnics 37:100815. https://doi.org/10.1016/j.trgeo.2022.100815

    Article  Google Scholar 

  236. Khatti J, Grover KS (2023) CBR prediction of pavement materials in unsoaked condition using LSSVM, LSTM-RNN, and ANN approaches. Int J Pavement Res Technol. https://doi.org/10.1007/s42947-022-00268-6

    Article  Google Scholar 

  237. Khatti J, Grover KS (2023) Prediction of UCS of fine-grained soil based on machine learning part 2: comparison between hybrid relevance vector machine and Gaussian process regression. Multiscale Multidisc Model Exp Des. https://doi.org/10.1007/s41939-023-00191-8

    Article  Google Scholar 

  238. Kumar M, Kumar V, Biswas R, Samui P, Kaloop MR, Alzara M, Yosri AM (2022) Hybrid ELM and MARS-based prediction model for bearing capacity of shallow foundation. Processes 10(5):1013. https://doi.org/10.3390/pr10051013

    Article  Google Scholar 

  239. Kumar V, Samui P, Himanshu N, Burman A (2019) Reliability-based slope stability analysis of Durgawati earthen dam considering steady and transient state seepage conditions using MARS and RVM. Indian Geotech J 49(6):650–666. https://doi.org/10.1007/s40098-019-00373-7

    Article  Google Scholar 

  240. Samui P, Kurup P (2012) Multivariate adaptive regression spline and least square support vector machine for prediction of undrained shear strength of clay. Int J Appl Metaheuristic Comput 3(2):33–42

    Google Scholar 

  241. Samui P (2013) Applicability of Data Mining Techniques for Predicting Electrical Resistivity of Soils Based on Thermal Resistivity. Int J Geomech 13(5):692–697. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000253

    Article  Google Scholar 

  242. Smith GN (1986) Probability and statistics in civil engineering—an introduction. Collins, London

    Google Scholar 

  243. Asteris PG, Skentou AD, Bardhan A, Samui P, Lourenço PB (2021) Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests. Constr Build Mater 303:124450. https://doi.org/10.1016/j.conbuildmat.2021.124450

    Article  Google Scholar 

  244. Asteris PG, Skentou AD, Bardhan A, Samui P, Pilakoutas K (2021) Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem Concr Res 145:106449. https://doi.org/10.1016/j.cemconres.2021.106449

    Article  Google Scholar 

  245. Khatti J, Grover KS (2023) Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models. J Rock Mech Geotech Eng. https://doi.org/10.1016/j.jrmge.2022.12.034

    Article  Google Scholar 

  246. Khatti J, Grover KS (2023) Assessment of fine-grained soil compaction parameters using advanced soft computing techniques. Arab J Geosci 16(3):208. https://doi.org/10.1007/s12517-023-11268-6

    Article  Google Scholar 

  247. Kumar DR, Samui P, Wipulanusat W, Keawsawasvong S, Sangjinda K, Jitchaijaroen W (2023) Soft computing techniques for predicting penetration and uplift resistances of dual pipelines in cohesive soils. Eng Sci 24:897. https://doi.org/10.30919/es897

    Article  Google Scholar 

  248. Khatti J, Grover KS (2023) Prediction of UCS of fine-grained soil based on machine learning part 1: multivariable regression analysis, Gaussian process regression, and gene expression programming. Multiscale Multidisc Model Exp Des. https://doi.org/10.1007/s41939-022-00137-6

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Rajasthan Technical University, Kota, Rajasthan, 324010, India

    Jitendra Khatti & Kamaldeep Singh Grover

Authors
  1. Jitendra Khatti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamaldeep Singh Grover
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JK and KSG: main author, conceptualization, literature review, manuscript preparation, detailing, overall analysis, manuscript finalization, detailed review, and editing.

Corresponding author

Correspondence to Jitendra Khatti.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khatti, J., Grover, K.S. A Scientometrics Review of Soil Properties Prediction Using Soft Computing Approaches. Arch Computat Methods Eng 31, 1519–1553 (2024). https://doi.org/10.1007/s11831-023-10024-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-023-10024-z

Search

Navigation