Skip to main content
SpringerLink
Log in

Multi-layer perceptron (MLP) neural network for predicting the modified compaction parameters of coarse-grained and fine-grained soils

  • Technical paper
  • Published:
Innovative Infrastructure Solutions Aims and scope Submit manuscript

Abstract

The present study focused on developing the multi-layer perceptron neural network prediction model for the modified compaction parameters of coarse-grained and fine-grained soil. A total of 248 in situ collected soil samples were taken from the ongoing highways construction project work site for their quality control purposes. The collected soil samples were tested in the laboratory using Bureau of Indian Standard specification. Among 248 datasets, 179 datasets belong to coarse-grained soil, and the remaining 69 datasets are fit for fine-grained soil. The artificial neural network (ANN) algorithm, written in Python V3.7.9 platform, was adopted for the model development. The developed model exhibits the correlation coefficient (R) value more than 0.80 and 0.90 for coarse-grained and fine-grained soil, respectively. Additionally, the selected ANN models can predict MDD within ± 4% and ± 2% variations for coarse-grained and fine-grained soil, respectively. In contrast, OMC for both the soil can be predicted within ± 8% variations.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Ardakani A, Kordnaeij A (2017) Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm. Eur J Environ Civ Eng 23:1–14

    Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. Bera A, Ghosh A (2011) Regression model for prediction of optimum moisture content and maximum dry unit weight of fine grained soil. Int J Geotech Eng 5(3):297–305

    Article  Google Scholar 

  5. Das SK (2005) Application of genetic algorithm and artificial neural network to some geotechnical engineering problems. Ph.D Thesis, IIT Kanpur, India

  6. Das SK (2013) Artificial neural networks in geotechnical engineering: modeling and application issues. Metaheuristics Water Geotech Transp Eng 45:231–267

    Article  Google Scholar 

  7. Das SK, Basudhar PK (2006) Undrained lateral load capacity of piles in clay using artificial neural network. Comput Geotech 33(8):454–459

    Article  Google Scholar 

  8. 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(3):329–342

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. Erzin Y, Turkoz D (2016) Use of neural networks for the prediction of the CBR value of some Aegean sands. Neural Comput Appl 27(5):1415–1426

    Article  Google Scholar 

  11. Farooq K, Khalid U, Mujtaba H (2016) Prediction of compaction characteristics of fine-grained soils using consistency limits. Arab J Sci Eng 41(4):1319–1328

    Article  Google Scholar 

  12. Günaydın O (2009) Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. Environ Geol 57(1):203

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. Gurtug Y, Sridharan A (2004) Compaction behaviour and prediction of its characteristics of fine grained soils with particular reference to compaction energy. Soils Found 44(5):27–36

    Article  Google Scholar 

  15. Gurtug Y, Sridharan A, Ikizler SB (2018) Simplified method to predict compaction curves and characteristics of soils. Iran J Sci Technol Trans Civ Eng 42(3):207–216

    Article  Google Scholar 

  16. Jalal FE, Xu Y, Iqbal M, Jamhiri B, Javed MF (2021) Predicting the compaction characteristics of expansive soils using two genetic programming-based algorithms. Transp Geotech 30:100608

    Article  Google Scholar 

  17. 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 Civ Eng 43(1):231–248

    Article  Google Scholar 

  18. Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46(7):1214–1222

    Article  Google Scholar 

  19. 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

    Article  Google Scholar 

  20. Kolay E, Baser T (2014) Estimating of the dry unit weight of compacted soils using general linear model and multi-layer perceptron neural networks. Appl Soft Comput 18:223–231

    Article  Google Scholar 

  21. 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):159

    Article  Google Scholar 

  22. Mishra AK, Kumar B, Vadlamudi S (2017) Prediction of hydraulic conductivity for soil–bentonite mixture. Int J Environ Sci Technol 14(8):1625–1634

    Article  Google Scholar 

  23. Omar M, Shanableh A, Basma A, Barakat S (2003) Compaction characteristics of granular soils in United Arab Emirates. Geotech Geol Eng 21(3):283–295

    Article  Google Scholar 

  24. Patra C, Sivakugan N, Das B (2010) Relative density and median grain-size correlation from laboratory compaction tests on granular soil. Int J Geotech Eng 4(1):55–62

    Article  Google Scholar 

  25. Proctor RR (1933) Fundamental principles of soil compaction. Eng News Rec 111(13)

  26. Rafiq MY, Bugmann G, Easterbrook DJ (2001) Neural network design for engineering applications. Comput Struct 79(17):1541–1552

    Article  Google Scholar 

  27. Sabat AK (2015) Prediction of California bearing ratio of a stabilized expansive soil using artificial neural network and support vector machine. Electron J Geotech Eng 20(3):981–991

    Google Scholar 

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

    Google Scholar 

  29. Shahin MA, Jaksa MB, Maier HR (2008) State of the art of artificial neural networks in geotechnical engineering. Electron J Geotech Eng 8:1–26

    Google Scholar 

  30. Shahin MA, Maier HR, Jaksa MB (2004) Data division for developing neural networks applied to geotechnical engineering. J Comput Civ Eng 18(2):105–114

    Article  Google Scholar 

  31. Shukla D, Solanki CH (2020) Estimated empirical correlations between shear wave velocity and SPT-N value for indore City using NLR and ANN. Indian Geotech J 50:1–17

    Article  Google Scholar 

  32. Taha OME, Majeed ZH, Ahmed SM (2018) Artificial neural network prediction models for maximum dry density and optimum moisture content of stabilized soils. Transp Infrastruct Geotechnol 5(2):146–168

    Article  Google Scholar 

  33. Tenpe AR, Patel A (2018) Application of genetic expression programming and artificial neural network for prediction of CBR. Road Mater Pavement Des 21(5):1183–1200

    Article  Google Scholar 

  34. Tenpe AR, Patel A (2020) Utilization of support vector models and gene expression programming for soil strength modeling. Arab J Sci Eng 45:1–19

    Article  Google Scholar 

  35. Verma G, Kumar B (2019) Prediction of compaction parameters for fine-grained and coarse-grained soils: a review. Int J Geotech Eng. https://doi.org/10.1080/19386362.2019.1595301

    Article  Google Scholar 

  36. Wang H-L, Yin Z-Y (2020) High performance prediction of soil compaction parameters using multi expression programming. Eng Geol 276:105758

    Article  Google Scholar 

  37. Yang X-S, Gandomi AH, Talatahari S, Alavi AH (2012) Metaheuristics in water, geotechnical and transport engineering. Newnes, London

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, 221005, India

    Gaurav Verma & Brind Kumar

Authors
  1. Gaurav Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brind Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gaurav Verma.

Ethics declarations

Conflict of interest

No potential conflict of interest was reported by the authors.

Appendix

Appendix

See Tables 10 and 11.

Table 10 A list of sample data for the coarse-grained soil
Full size table
Table 11 A list of sample data for the fine-grained soil
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Verma, G., Kumar, B. Multi-layer perceptron (MLP) neural network for predicting the modified compaction parameters of coarse-grained and fine-grained soils. Innov. Infrastruct. Solut. 7, 78 (2022). https://doi.org/10.1007/s41062-021-00679-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41062-021-00679-7

Keywords

Search

Navigation