Skip to main content
SpringerLink
Log in

Prediction of Unconfined Compressive Strength of Stabilized Sand Using Machine Learning Methods

  • Original Paper
  • Published:
Indian Geotechnical Journal Aims and scope Submit manuscript

Abstract

This article introduces a novel method using the K-nearest neighbors (KNN) model to predict the unconfined compressive strength (UCS) of soil-stabilizer combinations. To ensure accurate UCS estimation, customized KNN prediction models are created. The research combines two meta-heuristic algorithms, the giant trevally optimizer (GTO) and the tunicate swarm algorithm (TSA), for improved accuracy. These algorithms examine UCS samples made from various soil types and validate the models using the results of previous stabilization tests. The study’s findings point to three distinct models: KNGT, KNTS, and an independent KNN model. Each of these models offers insightful information that helps with the precise prediction of UCS. As a result, the KNGT model performs best by displaying suitable statistics like a high R2 value of 0.995 and a low RMSE value of 84.5 (kN/m2). In addition, KNTS obtained the second suitable performance with R2 and RMSE equal to 0.989 and 121.2 (kN/m2), alternatively. These results highlight the accuracy, dependability, and prognostication capabilities of the KNGT model. This study describes a unique machine-learning approach for the exact prediction of critical soil parameters, with an emphasis on UCS in civil engineering. In order to overcome data difficulties, it leverages the KNN model and presents a novel dual-algorithm technique that combines the GTO and TSA. This integration improves KNN’s accuracy and efficiency while also easing UCS structure planning in civil engineering. The work not only improves UCS prediction accuracy but also opens up a viable route for future research and practical applications in civil engineering undertakings, giving significant insights and a fresh method for UCS performance prediction.

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

Abbreviations

UCS:

Unconfined compressive strength

LL:

Liquid limit

KNN:

K-nearest neighbors

TSA:

Tunicate swarm algorithm

RMSE:

Root mean square error

MDAPE:

Median absolute percentage error

R2:

Coefficient of determination

PL:

Plastic limit

PI:

Plasticity index

GTO:

Giant trevally optimizer

ML:

Machine learning

SI:

Scatter index

MSE:

Mean square error

References

  1. Zhang F, O’Donnell LJ (2020) Support vector regression In: Machine learning, Elsevier, 2020, pp. 123–140

  2. Akbulut S, Kalkan E, Celik S (2003) Artificial Neural Networks to estimate the shear strength of compacted soil samples. Int Conf New Dev Soil Mech Geotech Eng 1:285–290

    Google Scholar 

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

    Article  Google Scholar 

  4. Sivrikaya O, Togrol E, Komur M (2004) Determination of unconfined compressive strength by Artificial Neural Network. In: 10th national congress of soil mechanics and foundation engineering, Istanbul, Turkey

  5. Majdi A, Rezaei M (2013) Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network. Neural Comput Appl 23:381–389

    Article  Google Scholar 

  6. Sathyapriya S, Arumairaj PD, Ranjini D (2017) Prediction of unconfined compressive strength of a stabilised expansive clay soil using ANN and regression analysis (SPSS). Asian J Res Soc Sci Humanit 7(2):109–123

    Google Scholar 

  7. Shah HA et al (2022) Application of machine learning techniques for predicting compressive, splitting tensile, and flexural strengths of concrete with metakaolin. Materials (Basel) 15(15):5435. https://doi.org/10.3390/ma15155435

    Article  Google Scholar 

  8. Wang H, Lei Z, Zhang X, Zhou B, Peng J (2016) Machine learning basics. Deep Learn., pp. 98–164

  9. Pham BT (2018) A novel classifier based on composite hyper-cubes on iterated random projections for assessment of landslide susceptibility. J Geol Soc India 91(3):355–362

    Article  Google Scholar 

  10. Pham BT, Hoang T-A, Nguyen D-M, Bui DT (2018) Prediction of shear strength of soft soil using machine learning methods. CATENA 166:181–191

    Article  Google Scholar 

  11. 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 Gr Eng 2:1–11

    Google Scholar 

  12. Nazir R, Momeni E, Armaghani DJ, Amin MFM (2013) Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples. Electron J Geotech Eng 18(1):1737–1746

    Google Scholar 

  13. Ceryan N, Okkan U, Kesimal A (2013) Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environ earth Sci 68:807–819

    Article  Google Scholar 

  14. Ruffolo RM, Shakoor A (2009) Variability of unconfined compressive strength in relation to number of test samples. Eng Geol 108(1–2):16–23

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Yılmaz I, Sendır H (2002) Correlation of Schmidt hardness with unconfined compressive strength and Young’s modulus in gypsum from Sivas (Turkey). Eng Geol 66(3–4):211–219

    Article  Google Scholar 

  17. Naeini SA, Naderinia B, Izadi E (2012) Unconfined compressive strength of clayey soils stabilized with waterborne polymer. KSCE J Civ Eng 16:943–949

    Article  Google Scholar 

  18. Park S-S (2011) Unconfined compressive strength and ductility of fiber-reinforced cemented sand. Constr Build Mater 25(2):1134–1138

    Article  Google Scholar 

  19. Ghazavi M, Roustaie M (2010) The influence of freeze–thaw cycles on the unconfined compressive strength of fiber-reinforced clay. Cold Reg Sci Technol 61(2–3):125–131

    Article  Google Scholar 

  20. Nwaiwu CMO, Mezie EO (2021) Prediction of maximum dry unit weight and optimum moisture content for coarse-grained lateritic soils. Soils and Rocks 44:e2021054120

    Article  Google Scholar 

  21. Kumar M, Biswas R, Kumar DR, Samui P, Kaloop MR, Eldessouki M (2023) Soft computing-based prediction models for compressive strength of concrete. Case Stud Constr Mater 19:e02321

    Google Scholar 

  22. Khajeh A, Ebrahimi SA, MolaAbasi H, Jamshidi Chenari R, Payan M (2021) Effect of EPS beads in lightening a typical zeolite and cement-treated sand. Bull Eng Geol Environ 80(11):8615–8632. https://doi.org/10.1007/s10064-021-02458-1

    Article  Google Scholar 

  23. Kumar M, Kumar V, Rajagopal BG, Samui P, Burman A (2023) State of art soft computing based simulation models for bearing capacity of pile foundation: a comparative study of hybrid ANNs and conventional models. Model Earth Syst Environ 9(2):2533–2551. https://doi.org/10.1007/s40808-022-01637-7

    Article  Google Scholar 

  24. Tavana Amlashi A, Mohammadi Golafshani E, Ebrahimi SA, Behnood A (2023) Estimation of the compressive strength of green concretes containing rice husk ash: a comparison of different machine learning approaches. Eur J Environ Civ Eng 27(2):961–983. https://doi.org/10.1080/19648189.2022.2068657

    Article  Google Scholar 

  25. Behnam Sedaghat, Tejani GG, Kumar S (2023) Predict the maximum dry density of soil based on individual and hybrid methods of machine learning. Adv Eng Intell Syst 2(3) https://doi.org/10.22034/aeis.2023.414188.1129.

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

    Article  Google Scholar 

  27. Riahi-Madvar H, Gholami M, Gharabaghi B, Morteza Seyedian S (2021) A predictive equation for residual strength using a hybrid of subset selection of maximum dissimilarity method with Pareto optimal multi-gene genetic programming. Geosci Front 12(5):101222. https://doi.org/10.1016/j.gsf.2021.101222

    Article  Google Scholar 

  28. Akbarzadeh MR, Ghafourian H, Anvari A, Pourhanasa R, Nehdi ML (2023) Estimating compressive strength of concrete using neural electromagnetic field optimization. Materials (Basel) 16(11):4200

    Article  Google Scholar 

  29. Hoque MI, Hasan M, Islam MS, Houda M, Abdallah M, Sobuz MHR (2023) Machine learning methods to predict and analyse unconfined compressive strength of stabilised soft soil with polypropylene columns. Cogent Eng 10(1):2220492. https://doi.org/10.1080/23311916.2023.2220492

    Article  Google Scholar 

  30. Kumar M, Biswas R, Kumar DR, Pradeep T, Samui P (2022) Metaheuristic models for the prediction of bearing capacity of pile foundation. Geomech Eng 31(2):129

    Google Scholar 

  31. Biswas R et al (2023) A novel integrated approach of RUNge Kutta optimizer and ANN for estimating compressive strength of self-compacting concrete. Case Stud Constr Mater 18:e02163

    Google Scholar 

  32. Taffese WZ, Abegaz KA (2022) Prediction of compaction and strength properties of amended soil using machine learning. Buildings 12(5):613

    Article  Google Scholar 

  33. Xiong L, Yao Y (2021) Study on an adaptive thermal comfort model with K-nearest-neighbors (KNN) algorithm. Build Environ 202:108026

    Article  Google Scholar 

  34. Abu Alfeilat HA et al (2019) Effects of distance measure choice on k-nearest neighbor classifier performance: a review. Big data 7(4):221–248

    Article  Google Scholar 

  35. Uddin S, Haque I, Lu H, Moni MA, Gide E (2022) Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci Rep 12(1):6256

    Article  Google Scholar 

  36. Sadeeq HT, Abdulazeez AM (2022) Giant trevally optimizer (GTO): a novel metaheuristic algorithm for global optimization and challenging engineering problems. IEEE Access 10:121615–121640

    Article  Google Scholar 

  37. Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell 90:103541

    Article  Google Scholar 

  38. Sharma A, Dasgotra A, Tiwari SK, Sharma A, Jately V, Azzopardi B (2021) Parameter extraction of photovoltaic module using tunicate swarm algorithm. Electronics 10(8):878

    Article  Google Scholar 

  39. Kumar M, Samui P (2022) Reliability analysis of pile foundation using GMDH, GP and MARS BT-CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure. 2022, pp. 1151–1159

  40. Kumar M, Tn DS (2023) Genetic programming based compressive strength prediction model for green concrete. Mater Today Proc. https://doi.org/10.1016/j.matpr.2023.03.024

    Article  Google Scholar 

  41. 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 

  42. Kumar M, Fathima NZ, Kumar DR (2024) A novel XGBoost and RF-based metaheuristic models for concrete compression strength BT-sustainable innovations in construction management pp. 495–503

Download references

Funding

No funding.

Author information

Authors and Affiliations

  1. School of Art, Zhengzhou Railway Vocational and Technical College, Zhengzhou, 450000, Henan, China

    Qinggang Zhao

  2. Faculty of Engineering, Huanghe S&T University, Zhengzhou, 450000, Henan, China

    Yan Shi

Authors
  1. Qinggang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qinggang Zhao.

Ethics declarations

Conflict of interest

The authors declare no competing of interests.

Additional information

Publisher's Note

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

Appendix

Appendix

Test Dataset

Soil (%)

Cement (%)

Lime (%)

LL (%)

PL (%)

PI (%)

UCS (kN/m2)

94

2

4

45

26

19

2450

94

2

4

27

16

11

2050

94

2

4

23

22

1

1100

94

2

4

89

19

70

1400

94

6

0

55

20

35

1200

96

0

4

31.78

21

10.78

1154

70

30

0

57

41.5

15.5

2076.95

94

2

4

50

15

35

3000

94

4

2

24

21

3

2300

94

6

0

55

23

32

1570

90

6

4

23

18

5

2900

96

4

0

35

27

8

4200

94

4

2

40

18

22

2990

92

6

2

37

16

21

4900

94

6

0

42

23

19

2300

92

4

4

46

16

30

1820

94

4

2

38

17

21

2300

94

0

6

66

24

42

1250

94

4

2

35

29

6

3210

85

15

0

49

40.7

8.3

2545.81

95

0

5

31

14

17

3900

95

5

0

32

24

8

2150

94

6

0

55

30

25

3300

92

2

6

40

20

20

1610

92

2

6

45

26

19

2450

96

0

4

30

18

12

3000

94

6

0

29

14

15

2200

94

6

0

35

21

14

2270

96

4

0

24

20

4

4100

70

0

30

38.4

32

6.4

461.89

70

30

0

39

32.2

6.8

3297

94

0

6

80

58

22

180

94

6

0

44

22

22

3200

94

6

0

51

25

26

4020

94

4

2

19

14

5

2540

96

4

0

21

17

4

4700

94

6

0

45

12

33

1980

94

0

6

33

12

21

2450

94

6

0

25

21

4

4100

94

6

0

25

12

13

2700

94

6

0

35

15

20

2300

94

4

2

32

13

19

1980

93

7

0

48

40.3

7.7

609.97

94

6

0

36.4

19

17.4

2020

94

4

2

72

23

49

1300

94

6

0

18

14

4

4400

91

0

9

54

32

22

1930

94

4

2

29

16

13

2000

94

0

6

29

18

11

4600

95

5

0

23

18

5

3100

97

3

0

18

14

4

4400

94

6

0

29

22

7

3250

94

4

2

45

26

19

1750

92

4

4

32

17

15

3100

94

6

0

40

20

20

1610

95

5

0

25

21

4

4100

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

Zhao, Q., Shi, Y. Prediction of Unconfined Compressive Strength of Stabilized Sand Using Machine Learning Methods. Indian Geotech J (2024). https://doi.org/10.1007/s40098-024-00924-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40098-024-00924-7

Keywords

Search

Navigation