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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines

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Abstract

This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

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References

  1. Cristelo N, Glendinning S, Fernandes L, Pinto T A. Effects of alkaline activated fly ash and Portland cement on soft soil stabilization. Acta Geotechnica, 2013, 8(4): 395–405

    Article  Google Scholar 

  2. Anwar Hossain K M. Stabilized soils incorporating combinations of rice husk ash and cement kiln dust. Journal of Materials in Civil Engineering, 2011, 23(9): 1320–1327

    Article  Google Scholar 

  3. Muthadhi A, Kothandaraman S. Optimum production conditions for reactive rice husk ash. Materials and Structures, 2010, 43(9): 1303–1315

    Article  Google Scholar 

  4. Bagheri Y, Ahmad F, Ismail M A M. Strength and mechanical behavior of soil-cement-lime-rice husk ash (Soil-CLR) mixture. Materials and Structures, 2014, 47(1–2): 55–66

    Article  Google Scholar 

  5. Shahin M A, Maier H R, Jaksa M B. Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering, 2004, 18(2): 105–114

    Article  Google Scholar 

  6. Narendra B S, Sivapullaiah P V, Suresh S, Omkar S N. Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: A comparative study. Computers and Geotechnics, 2006, 33(3): 196–208

    Article  Google Scholar 

  7. Kalkan E, Akbulut S, Tortum A, Celik A. Prediction of the unconfined compressive strength of compacted granular soils by using inference systems. Environmental Geology (Berlin), 2009, 58(7): 1429–1440

    Article  Google Scholar 

  8. Suman S, Mahamaya M, Das S K. Prediction of maximum dry density and unconfined compressive strength of cement stabilized soil using artificial intelligence techniques. International Journal of Geosynthetics and Ground Engineering, 2016, 2: 1–11

    Article  Google Scholar 

  9. He S, Li J. Modeling nonlinear elastic behavior of reinforced soil using artificial neural networks. Applied Soft Computing, 2009, 9(3): 954–961

    Article  Google Scholar 

  10. Gunaydin O, Gokoglu A, Fener M. Prediction of artificial soil’s unconfined compression strength test using statistical analyses and artificial neural networks. Advances in Engineering Software, 2010, 41(9): 1115–1123

    Article  Google Scholar 

  11. Shrestha R. Deep soil mixing and predictive neural network models for strength prediction. Dissertation for the Doctoral Degree. Cambridge: University of Cambridge, 2012

    Google Scholar 

  12. Wang O, Al-Tabbaa A. Preliminary model development for predicting strength and stiffness of cement-stabilized soils using artificial neural networks. In: ASCE International Workshop on Computing in Civil Engineering. Los Angeles, CA, 2013, 299–306

  13. Park H I, Kim Y T. Prediction of strength of reinforced lightweight soil using an artificial neural network. Engineering Computations, 2011, 28(5): 600–615

    Article  Google Scholar 

  14. Mozumder R, Laskar A I. Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network. Computers and Geotechnics, 2015, 69: 291–300

    Article  Google Scholar 

  15. Güllü H, Fedakar H I. On the prediction of unconfined compressive strength of silty soil stabilized with bottom ash, jute and steel fibers via artificial intelligence. Geomechanics and Engineering, 2017, 12(3): 441–464

    Article  Google Scholar 

  16. Ghorbani A, Hasanzadehshooiili H. Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models: Application to the deep soil mixing. Soil and Foundation, 2018, 58(1): 34–49

    Article  Google Scholar 

  17. Erzin Y, Ecemis N. The use of neural networks for CPT-based liquefaction screening. Bulletin of Engineering Geology and the Environment, 2015, 74(1): 103–116

    Article  Google Scholar 

  18. Mozumder R A, Laskar A I, Hussain M. Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines. Construction & Building Materials, 2017, 132: 412–424

    Article  Google Scholar 

  19. Wang J, Xing Y, Cheng L, Qin F, Ma T. The prediction of mechanical properties of cement soil based on PSO-SVM. In: International Conference on Computational Intelligence and Software Engineering. Wuhan: IEEE, 2010, 10: 1–4

    Google Scholar 

  20. Shi X, Liu Q, Lv X. Application of SVM In prediction the strength of cement stabilized soil. Applied Mechanics and Materials, 2012, 160: 313–317

    Article  Google Scholar 

  21. Tinoco J, Gomes Correia A, Cortez P. Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Computers and Geotechnics, 2014, 55: 132–140

    Article  Google Scholar 

  22. Ceryan N. Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks. Journal of African Earth Sciences, 2014, 100: 634–644

    Article  Google Scholar 

  23. Zhao H. Slope reliability analysis using a support vector machine. Computers and Geotechnics, 2008, 35(3): 459–467

    Article  Google Scholar 

  24. Kordjazi A, Pooya Nejad F, Jaksa M B. Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data. Computers and Geotechnics, 2014, 55: 91–102

    Article  Google Scholar 

  25. Singh V K, Kumar D, Kashyap P S, Singh P K, Kumar A, Singh S K. Modelling of soil permeability using different data driven algorithms based on physical properties of soil. Journal of Hydrology (Amsterdam), 2019, 580: 124223

    Article  Google Scholar 

  26. Xue X, Yang X. Seismic liquefaction potential assessed by support vector machines approaches. Bulletin of Engineering Geology and the Environment, 2016, 75(1): 153–162

    Article  Google Scholar 

  27. Hoang N C, Bui D T. Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: A multi-dataset study. Bulletin of Engineering Geology and the Environment, 2018, 77(1): 191–204

    Article  Google Scholar 

  28. Rahbarzare A, Azadi M. Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine. Bulletin of Engineering Geology and the Environment, 2019, 78(7): 4977–4987

    Article  Google Scholar 

  29. Haykin S S. Neural Networks: A Comprehensive Foundation. New York: Prentice Hall, ISBN 0132733501. OCLC 38908586. 1999

    MATH  Google Scholar 

  30. Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial Neural Network Methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359

    Article  Google Scholar 

  31. Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59(2): 433–456

    Article  Google Scholar 

  32. Aladag C H, Egrioglu E, Gunay S, Basaran M A. Improving weighted information criterion by using optimization. Journal of Computational and Applied Mathematics, 2010, 233(10): 2683–2687

    Article  MathSciNet  Google Scholar 

  33. Vapnik V N, Golowich S E, Smola A. Support vector method for function approximation, regression estimation and signal processing. In: NIPS’96: Proceedings of the 9th International Conference on Neural Information Processing Systems. San Mateo, CA: Morgan Kaufmann, 1996

    Google Scholar 

  34. Kohestani V R, Hassanlourad M. Modeling the mechanical behavior of carbonate sands using artificial neural networks and support vector machines. International Journal of Geomechanics, 2016, 16(1): 04015038

    Article  Google Scholar 

  35. Smola A J, Scholkopf B. A Tutorial on Support Vector Regression. Report No. NC2-TR-1998-030, Neuro COLT2 Technical Report Series. 1998

  36. Hsu C W, Chang C C, Lin C J. A Practical Guide to Support Vector Classification. Technical report. Taipei, China: Taiwan University, 2016

    Google Scholar 

  37. Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel Based Learning Methods. London: Cambridge University, 2000

    Book  Google Scholar 

  38. Goh A T, Goh S. Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data. Computers and Geotechnics, 2007, 34(5): 410–421

    Article  Google Scholar 

  39. Samui P. Support vector machine applied to settlement of shallow foundations on cohesionless soils. Computers and Geotechnics, 2008, 35(3): 419–427

    Article  Google Scholar 

  40. Al-Anazi A F, Gates I D. Support vector regression to predict porosity and permeability: Effect of sample size. Computers & Geosciences, 2012, 39: 64–76

    Article  Google Scholar 

  41. Tran K Q, Satomi T, Takahashi H. Tensile behaviors of natural fiber and cement reinforced soil subjected to direct tensile test. Journal of Building Engineering 2019, 24: 1–10

    Article  Google Scholar 

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Acknowledgements

The authors of this paper would like to acknowledge the support provided (No. 981861) by Golestan University.

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Authors and Affiliations

  1. Depatrment of Civil Engineering, Faculty of Engineering, Golestan University, Gorgan, 49138-15759, Iran

    Alireza Tabarsa

  2. Terracon Consultants, Inc., Nashville, TN, 37211, USA

    Nima Latifi

  3. Civil Engineering Department, Southern Illinois University, Edwardsville, IL, 62026-1800, USA

    Abdolreza Osouli

  4. Faculty of Engineering, Mirdamad Institute of Higher Education, Gorgan, 49166-53989, Iran

    Younes Bagheri

Authors
  1. Alireza Tabarsa
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  2. Nima Latifi
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  4. Younes Bagheri
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Correspondence to Alireza Tabarsa.

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

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  • DOI: https://doi.org/10.1007/s11709-021-0689-9

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