Skip to main content
SpringerLink
Log in

A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

This research proposes an alternative for estimating shear strength of soil based on a hybridization of Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). SVR is used as a function approximation method for making prediction of the soil shear strength based on a set of twelve variables including sample depth, sand content, loam content clay content, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic index, and liquid index. The hybrid framework, named as PSO–SVR, relies on PSO, as a metaheuristic, to optimize the training phase of the employed function approximator. A data set consisting of 443 soil samples associated with the experimental results of shear strength has been collected from a housing project in Vietnam. This data set is then used to train and verify the performance of the PSO–SVR model specifically constructed for shear strength estimation. The hybrid model has achieved a good modeling outcome with Root Mean Square Error (RMSE) = 0.038, Mean Absolute Percentage Error (MAPE) = 9.701%, and Coefficient of Determination (R2) = 0.888. Hence, the PSO–SVR model can be a potential alternative to be participated in the design phase of high-rise housing projects.

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

Similar content being viewed by others

References

  1. Vanapalli SK, Fredlund DG (2000) Comparison of different procedures to predict unsaturated soil shear strength. Adv Unsatur Geotech. https://doi.org/10.1061/40510(287)13

    Article  Google Scholar 

  2. Tien Bui D, Hoang N-D, Nhu V-H (2018) A swarm intelligence-based machine learning approach for predicting soil shear strength for road construction: a case study at Trung Luong National Expressway Project (Vietnam). Eng Comput. https://doi.org/10.1007/s00366-018-0643-1

    Article  Google Scholar 

  3. Das BM, Sobhan K (2013) Principles of geotechnical engineering. Cengage Learning, Boston (ISBN-10:1133108660)

    Google Scholar 

  4. Nam S, Gutierrez M, Diplas P, Petrie J (2011) Determination of the shear strength of unsaturated soils using the multistage direct shear test. Eng Geol 122(3):272–280. https://doi.org/10.1016/j.enggeo.2011.06.003

    Article  Google Scholar 

  5. Rassam DW, Williams DJ (1999) A relationship describing the shear strength of unsaturated soils. Can Geotech J 36(2):363–368. https://doi.org/10.1139/t98-102

    Article  Google Scholar 

  6. Gan JKM, Fredlund DG, Rahardjo H (1988) Determination of the shear strength parameters of an unsaturated soil using the direct shear test. Can Geotech J 25(3):500–510. https://doi.org/10.1139/t88-055

    Article  Google Scholar 

  7. Hoang N-D, Tien Bui D, Liao K-W (2016) Groutability estimation of grouting processes with cement grouts using differential flower pollination optimized support vector machine. Appl Soft Comput 45:173–186. https://doi.org/10.1016/j.asoc.2016.04.031

    Article  Google Scholar 

  8. Prayogo D, Susanto YTT (2018) Optimizing the prediction accuracy of friction capacity of driven piles in cohesive soil using a novel self-tuning least squares support vector machine. Adv Civ Eng 2018:9. https://doi.org/10.1155/2018/6490169

    Article  Google Scholar 

  9. Chou J-S, Yang K-H, Lin J-Y (2016) Peak shear strength of discrete fiber-reinforced soils computed by machine learning and metaensemble methods. J Comput Civ Eng 30(6):04016036. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000595

    Article  Google Scholar 

  10. Chou J-S, Chong WK, Bui D-K (2016) Nature-inspired metaheuristic regression system: programming and implementation for civil engineering applications. J Comput Civ Eng 30(5):04016007. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000561 doi

    Article  Google Scholar 

  11. Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN (2017) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput 33(3):497–507. https://doi.org/10.1007/s00366-016-0486-6

    Article  Google Scholar 

  12. Mozumder RA, Laskar AI, Hussain M (2018) Penetrability prediction of microfine cement grout in granular soil using artificial intelligence techniques. Tunn Undergr Space Technol 72:131–144. https://doi.org/10.1016/j.tust.2017.11.023

    Article  Google Scholar 

  13. 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):11. https://doi.org/10.1007/s40891-016-0051-9

    Article  Google Scholar 

  14. Hossein Alavi A, Hossein Gandomi A (2011) A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput 28(3):242–274. https://doi.org/10.1108/02644401111118132 doi

    Article  MATH  Google Scholar 

  15. Hoang N-D, Bui DT (2018) 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. Bull Eng Geol Environ 77(1):191–204. https://doi.org/10.1007/s10064-016-0924-0

    Article  Google Scholar 

  16. Shahin MA (2015) A review of artificial intelligence applications in shallow foundations. Int J Geotech Eng 9(1):49–60. https://doi.org/10.1179/1939787914Y.0000000058

    Article  Google Scholar 

  17. Lary DJ, Alavi AH, Gandomi AH, Walker AL (2016) Machine learning in geosciences and remote sensing. Geosci Front 7(1):3–10. https://doi.org/10.1016/j.gsf.2015.07.003

    Article  Google Scholar 

  18. Shahin MA (2016) State-of-the-art review of some artificial intelligence applications in pile foundations. Geosci Front 7(1):33–44. https://doi.org/10.1016/j.gsf.2014.10.002

    Article  Google Scholar 

  19. Fredlund DG, Morgenstern NR, Widger RA (1978) The shear strength of unsaturated soils. Can Geotech J 15(3):313–321. https://doi.org/10.1139/t78-029

    Article  Google Scholar 

  20. Gan K, Fredlund D (1988) Multistage direct shear testing of unsaturated soils. Geotechnical Test J 11(2):132–138. https://doi.org/10.1520/GTJ10959J

    Article  Google Scholar 

  21. Vanapalli SK, Fredlund DG, Pufahl DE, Clifton AW (1996) Model for the prediction of shear strength with respect to soil suction. Can Geotech J 33(3):379–392. https://doi.org/10.1139/t96-060

    Article  Google Scholar 

  22. Fredlund DG, Xing A, Fredlund MD, Barbour SL (1996) The relationship of the unsaturated soil shear to the soil–water characteristic curve. Can Geotech J 33(3):440–448. https://doi.org/10.1139/t96-065

    Article  Google Scholar 

  23. Abramento M, Carvalho CS (1989) Geotechnical parameters for the study of natural slopes instabilization at ‘Serra do Mar’ Brazil. In: Proceedings 12th international conference soil mechanics foundations engineering Rio de Janeiro, vol 3, pp 1599–1602

  24. Katte V, Blight G (2012) The roles of solute suction and surface tension in the strength of unsaturated soil. In: Mancuso C, Jommi C, D’Onza F (eds) Unsaturated soils: research and applications. Springer, Berlin, pp 431–437

    Chapter  Google Scholar 

  25. Leong EC, Nyunt TT, Rahardjo H (2013) Triaxial testing of unsaturated soils. In: Laloui L, Ferrari A (eds) Multiphysical testing of soils and shales. Springer series in geomechanics and geoengineering. Springer, Berlin, Heidelberg, pp 33–44

    Chapter  Google Scholar 

  26. Bishop CM (2011) Pattern recognition and machine learning (information science and statistics). Springer, Berlin (ISBN-10: 0387310738)

    Google Scholar 

  27. Pham BT, Son LH, Hoang T-A, Nguyen D-M, Tien Bui D (2018) Prediction of shear strength of soft soil using machine learning methods. CATENA 166:181–191. https://doi.org/10.1016/j.catena.2018.04.004

    Article  Google Scholar 

  28. Kiran S, Lal B, Tripathy SS (2016) Shear strength prediction of soil based on probabilistic neural network. Indian J Sci Technol 9(41):1–6. https://doi.org/10.17485/ijst/2016/v9i41/99188

    Article  Google Scholar 

  29. Hashemi Jokar M, Mirasi S (2017) Using adaptive neuro-fuzzy inference system for modeling unsaturated soils shear strength. Soft Comput. https://doi.org/10.1007/s00500-017-2778-1

    Article  Google Scholar 

  30. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  31. Hoang N-D, Liao K-W, Tran X-L (2018) Estimation of scour depth at bridges with complex pier foundations using support vector regression integrated with feature selection. J Civ Struct Health Monit. https://doi.org/10.1007/s13349-018-0287-2

    Article  Google Scholar 

  32. Wang H, Xu D (2017) Parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function. J Control Sci Eng 2017:12. https://doi.org/10.1155/2017/3614790

    Article  MathSciNet  MATH  Google Scholar 

  33. Çevik A, Kurtoğlu AE, Bilgehan M, Gülşan ME, Albegmprli HM (2015) Support vector machines in structural engineering: a review. J Civ Eng Manag 21(3):261–281. https://doi.org/10.3846/13923730.2015.1005021

    Article  Google Scholar 

  34. Tien Bui D, Bui Q-T, Nguyen Q-P, Pradhan B, Nampak H, Trinh PT (2017) A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agric For Meteorol 233:32–44. https://doi.org/10.1016/j.agrformet.2016.11.002

    Article  Google Scholar 

  35. Sachdeva S, Bhatia T, Verma AK (2017) Flood susceptibility mapping using GIS-based support vector machine and particle swarm optimization: a case study in Uttarakhand (India). In: 2017 8th International conference on computing, communication and networking technologies (ICCCNT), 3–5 July 2017, pp 1–7. https://doi.org/10.1109/ICCCNT.2017.8204182

  36. Hacibeyoglu M, Ibrahim MH (2018) A novel multimean particle swarm optimization algorithm for nonlinear continuous optimization: application to feed-forward neural network training. Sci Program 2018:9. https://doi.org/10.1155/2018/1435810

    Article  Google Scholar 

  37. El-Ghandour HA, Elbeltagi E (2018) Comparison of five evolutionary algorithms for optimization of water distribution networks. J Comput Civ Eng 32(1):04017066. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000717 doi

    Article  Google Scholar 

  38. Jain NK, Nangia U, Jain J (2018) A review of particle swarm optimization. J Inst Eng (India) Ser B 99(4):407–411. https://doi.org/10.1007/s40031-018-0323-y

    Article  Google Scholar 

  39. Vapnik VN (1998) Statistical learning theory. Wiley, Hoboken (printed in the United States of America)

    MATH  Google Scholar 

  40. Kang F, Li J (2016) Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. J Comput Civ Eng 30(3):04015040. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000514

    Article  Google Scholar 

  41. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222. https://doi.org/10.1023/b:stco.0000035301.49549.88

    Article  MathSciNet  Google Scholar 

  42. Wu Y-H, Shen H (2018) Grey-related least squares support vector machine optimization model and its application in predicting natural gas consumption demand. J Comput Appl Math 338:212–220. https://doi.org/10.1016/j.cam.2018.01.033

    Article  MathSciNet  MATH  Google Scholar 

  43. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, New York, NY, pp 39–43

  44. Clayton CR (1995) The standard penetration test (SPT): methods and use. Construction Industry Research and Information Association, London

    Google Scholar 

  45. Schmertmann JH (1978) Guidelines for cone penetration test: performance and design. Federal Highway Administration, Washington, DC

    Google Scholar 

  46. (ASTM) ASfTaM (2005) ASTM D4648/D4648M-16, standard test methods for laboratory miniature vane shear test for saturated fine-grained clayey soil. Active Standard ASTM D4648, vol ASTM International, West Conshohocken, PA, 2016. http://www.astm.org

  47. Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993. https://doi.org/10.1109/72.329697

    Article  Google Scholar 

  48. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth and Brooks, Montery (ISBN-13: 978-0412048418$4)

    MATH  Google Scholar 

  49. Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Pearson, London

    Google Scholar 

  50. Friedman JH, Roosen CB (1995) An introduction to multivariate adaptive regression splines. Stat Methods Med Res 4(3):197–217. https://doi.org/10.1177/096228029500400303

    Article  Google Scholar 

  51. Matwork (2017) Statistics and machine learning toolbox User’s Guide. Matwork Inc. https://www.mathworks.com/help/pdf_doc/stats/stats.pdf. Accessed 28 Apr 2018

  52. Jekabsons G (2016) ARESLab: adaptive regression splines toolbox for Matlab/Octave. Technical report, Riga Technical University. http://www.csrtulv/jekabsons/. Accessed 15 July 2018

  53. Mendenhall W, Sincich TT (2011) A second course in statistics: regression analysis, 7th edn. Pearson, London

    MATH  Google Scholar 

Download references

Acknowledgements

This research was funded by the Geographic Information Science Research group, Ton Duc Thang University, Ho Chi Minh city, Vietnam. We would like to thank the Topgeo construction and Investigation Joint Stock Company (Hanoi) and Vingroup Joint Stock Company (Vietnam) for providing the data for this research.

Author information

Authors and Affiliations

  1. Department of Geological-Geotechnical Engineering, Hanoi University of Mining and Geology, No. 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi, Vietnam

    Viet-Ha Nhu & Van-Binh Duong

  2. Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809-03, Quang Trung, Da Nang, 550000, Vietnam

    Nhat-Duc Hoang

  3. Department of Hydrogeology and Engineering Geology, Vietnam Institute of Geosciences and Mineral Resources, Thanh Xuan, Hanoi, Vietnam

    Hong-Dang Vu

  4. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  5. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

Authors
  1. Viet-Ha Nhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nhat-Duc Hoang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Van-Binh Duong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong-Dang Vu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dieu Tien Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dieu Tien Bui.

Ethics declarations

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nhu, VH., Hoang, ND., Duong, VB. et al. A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam). Engineering with Computers 36, 603–616 (2020). https://doi.org/10.1007/s00366-019-00718-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-019-00718-z

Keywords

Search

Navigation