[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Prediction of seismic slope stability through combination of particle swarm optimization and neural network

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

Abstract

One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)–ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R 2) and value account for (VAF) and using simple ranking method, the best ANN and PSO–ANN models were selected. It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN. R 2 values of testing datasets equal to 0.915 and 0.986 for ANN and PSO–ANN techniques, respectively, suggest the superiority of the PSO–ANN technique.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

References

  1. Bishop AW (1955) The use of the slip circle in the stability analysis of slopes. Institution of civil engineers

  2. Spencer E (1967) A method of analysis of the stability of embankments assuming parallel inter-slice forces. Geotechnique 17:11–26

    Article  Google Scholar 

  3. Morgenstern NR, Price VE (1965) The analysis of the stability of general slip surfaces. Geotechnique 15:79–93

    Article  Google Scholar 

  4. Sarma SK (1973) Stability analysis of embankments and slopes. Geotechnique 23(3):423–433

    Article  MathSciNet  Google Scholar 

  5. Sarma SK (1979) Stability analysis of embankments and slopes. J Geotech Eng Division 105(12):1511–1524

    Google Scholar 

  6. Baker R (2006) A relation between safety factors with respect to strength and height of slopes. Comput Geotech 33(4):275–277

    Article  Google Scholar 

  7. Huang M, Jia CQ (2009) Strength reduction FEM in stability analysis of soil slopes subjected to transient unsaturated seepage. Comput Geotech 36(1):93–101

    Article  Google Scholar 

  8. El-Ramly H, Morgenstern NR, Cruden DM (2002) Probabilistic slope stability analysis for practice. Can Geotech J 39(3):665–683

    Article  Google Scholar 

  9. Tobutt DC (1982) Monte Carlo simulation methods for slope stability. Comput Geosci 8(2):199–208

    Article  Google Scholar 

  10. Wang JP, Huang D (2012) RosenPoint: a Microsoft Excel-based program for the Rosenblueth point estimate method and an application in slope stability analysis. Comput Geosci 48:239–243

    Article  Google Scholar 

  11. Lee S, Pradhan B (2006) Probabilistic landslide risk mapping at Penang Island, Malaysia. J Earth Syst Sci 115(6):661–672

    Article  Google Scholar 

  12. Irigaray C, El Hamdouni R, Jiménez-Perálvarez JD, Fernández P, Chacón J (2012) Spatial stability of slope cuts in rock massifs using GIS technology and probabilistic analysis. Bull Eng Geol Environ 71:569–578

    Article  Google Scholar 

  13. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

    Article  Google Scholar 

  14. Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional. ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas, EngGeol 85:347–366

    Google Scholar 

  15. Yilmaz I (2009) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68:297–306

    Article  Google Scholar 

  16. Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide susceptiblity mapping for shallow landslides in tropical hilly area. Comput Geosci 37:1264–1276

    Article  Google Scholar 

  17. Rezaei M, Monjezi M, Yazdian Varjani A (2011) Development of a fuzzy model to predict flyrock in surface mining. Safety Sci 49:298–305

    Article  Google Scholar 

  18. Khandelwal M, Kumar DL, Yellishetty M (2011) Application of soft computing to predict blast-induced ground vibration. Eng Comput 27(2):117–125

    Article  Google Scholar 

  19. Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181

    Article  Google Scholar 

  20. Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Narayanasamy MS, Mohd Amin MF (2014) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull Eng Geol Environ. doi:10.1007/s10064-014-0687-4

    Google Scholar 

  21. Momeni E, Nazir R, Armaghani DJ, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131

    Article  Google Scholar 

  22. Tonnizam Mohamad E, Jahed Armaghani D, Hajihassani M, Faizi K, Marto A (2013) A simulation approach to predict blasting-induced flyrock and size of thrown rocks. Electron J Geotech Eng 18:365–374

    Google Scholar 

  23. Tonnizam Mohamad E, Noorani SA, Jahed Armaghani D, Saad R (2012) Simulation of blasting induced ground vibration by using artificial neural network. Electron J Geotech Eng 17:2571–2584

    Google Scholar 

  24. Sakellariou M, Ferentinou M (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 24(3):419–445

    Article  Google Scholar 

  25. Das SK, Biswal RK, Sivakugan N, Das B (2011) Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environ Earth Sci 64(1):201–210

    Article  Google Scholar 

  26. Sah NK, Sheorey PR, Upadhyama LW (1994) Maximum likelihood estimation of slope stability. Int J Rock Mech Min Sci 31:47–53

    Article  Google Scholar 

  27. Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2(4):311–319

    Article  Google Scholar 

  28. Samui P, Kothari DP (2011) Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Scientia Iranica 18(1):53–58

    Article  MATH  Google Scholar 

  29. Erzin Y, Cetin T (2013) The predıctıon of the crıtıcal factor of safety of homogeneous fınıte slopes usıng neural networks and multıple regressıons. Comput Geosci 51:305–313

    Article  Google Scholar 

  30. Adhikari R, Agrawal RK (2011) Effectiveness of PSO based neural network for seasonal time series forecasting. In: Proceedings of the indian international conference on artificial intelligence (IICAI), Tumkur, India, pp 232–244

  31. Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of IEEE international conference on evolutionary computation, pp 81–86

  32. Momeni E, Jahed Armaghani D, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63

    Article  Google Scholar 

  33. Yu Y, Xie L, Zhang B (2005) Stability of earth–rockfill dams: influence of geometry on the three-dimensional effect. Comput Geotech 32(5):326–339

    Article  Google Scholar 

  34. Kramer SL (1996) Geotechnical earthquake engineering. Prentice-Hall, New Jersey

    Google Scholar 

  35. Dreyfus G (2005) Neural Networks: methodology and application. Springer, Berlin

    Google Scholar 

  36. Monjezi M, Khoshalan HA, Varjani AY (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5(3):441–448

    Article  Google Scholar 

  37. Maulenkamp F, Grima MA (1999) Application of neural networks for prediction of the unconfined compressive strength (UCS) from Equotip Hardness. Int J Rock Mech Min Sci 36:29–39

    Article  Google Scholar 

  38. Tonnizam Mohamad E, Hajihassani M, Jahed Armaghani D, Marto A (2012) Simulation of blasting-induced air overpressure by means of artificial neural networks. Int Rev Model Simul 5(6):2501–2506

    Google Scholar 

  39. Marto A, Hajihassani M, JahedArmaghani D, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J, Article ID 643715

  40. Monjezi M, Ahmadi Z, Varjani AY, Khandelwal M (2013) Backbreak prediction in the Chadormalu iron mine using artificial neural network. Neural ComputAppl 23:1101–1107

    Article  Google Scholar 

  41. Kosko B (1994) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, New Delhi

    Google Scholar 

  42. Simpson PK (1990) Artificial neural system: foundation, paradigms, applications and implementations. Pergamon, New York

    Google Scholar 

  43. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, pp 1942–1948

  44. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57

    Article  Google Scholar 

  45. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of evolutionary computation, CEC, Proceedings of the 1999 Congress, IEEE

  46. Das MT, Dulger LC (2009) Signature verification (SV) toolbox: application of PSO-NN. Eng Appl Artif Intel 22(4):688–694

    Article  Google Scholar 

  47. Victoire T, Jeyakumar AE (2004) Hybrid PSO–SQP for economic dispatch with valve-point effect. Electr Pow Syst Res 71(1):51–59

    Article  Google Scholar 

  48. Ismail A, Jeng DS, Zhang LL (2013) Anoptimised product-unit neural network with a novel PSO–BP hybrid training algorithm: applications to load–deformation analysis of axially loaded piles. Eng Appl Artif Intel 26(10):2305–2314

    Article  Google Scholar 

  49. Zhang JR, Zhang J, Lok TM, Lyu MR (2007) A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Appl Math Comput 185(2):1026–1037

    Article  MATH  Google Scholar 

  50. Engelbrecht AP (2007) Computational intelligence: an introduction, 2nd edn. Wiley, New York

    Book  Google Scholar 

  51. Tonnizam Mohamad E, Jahed Armaghani D, Momeni E, Abad SVANK (2014) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ. doi:10.1007/s10064-014-0638-0

    Google Scholar 

  52. Hajihassani M, JahedArmaghani D, Sohaei H, Tonnizam Mohamad E, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

    Article  Google Scholar 

  53. JahedArmaghani D, Hajihassani M, Tonnizam Mohamad E, Marto A, Noorani SA (2013) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci. doi:10.1007/s12517-013-1174-0

    Google Scholar 

  54. JahedArmaghani D, Hajihassani M, Yazdani Bejarbaneh B, Marto A, Tonnizam Mohamad E (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Measurement 55:487–498

    Article  Google Scholar 

  55. Montana DJ, Davis L (1989) Training feedforward neural networks using genetic algorithms. IJCAI 89:762–767

    Google Scholar 

  56. Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural ComputAppl 16:235–247

    Article  Google Scholar 

  57. Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43:224–235

    Article  Google Scholar 

  58. Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intel 22(4):808–814

    Article  Google Scholar 

  59. Yilmaz I, Yuksek G (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. Int J Rock Mech Min Sci 46(4):803–810

    Article  Google Scholar 

  60. Negnevitsky M (2002) Artificial Intelligence: A Guide to Intelligent Systems. Addison-Wesley, England

    Google Scholar 

  61. Singh VK, Singh D, Singh TN (2001) Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38(2):269–284

    Article  Google Scholar 

  62. Hassoun MH (1995) Fundamentals of Artificial Neural Networks. MIT Press, Cambridge

    MATH  Google Scholar 

  63. Fu L (1995) Neural networks in computer intelligence. McGraw-Hill, New York

    Google Scholar 

  64. Wyhthoff BJ (1993) Backpropagation neural networks: a tutorial. Chemometr Intell Lab Syst 18:115–155

    Article  Google Scholar 

  65. Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering. Dayton, OH, USA, pp 277–280

  66. Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725

    Article  Google Scholar 

  67. Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the First IEEE international conference on neural networks, San Diego, CA, USA, pp 11–14

  68. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  69. Baheer I (2000) Selection of methodology for modeling hysteresis behavior of soils using neural networks. J Comput Aid Civil Infrastruct Eng 5(6):445–463

    Article  Google Scholar 

  70. Sonmez H, Gokceoglu C (2008) Discussion on the paper by H. Gullu and E. Ercelebi A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey. Eng Geol 97:91–93

    Article  Google Scholar 

  71. Caudill M (1988) Neural networks primer part III. Al Expert 3(6):53–59

    Google Scholar 

  72. Ripley BD (1993) Statistical aspects of neural networks. In: Jensen JL, Kendall WS (eds) Barndoff-Neilsen OE networks and chaos-statistical and probabilistic aspects. Chapman & Hall, London, pp 40–123

    Chapter  Google Scholar 

  73. Paola JD (1994) Neural network classification of multispectral imagery. MSc thesis, The University of Arizona, USA

  74. Wang C (1994) A theory of generalization in learning machines with neural application. PhD thesis, The University of Pennsylvania, USA

  75. Masters T (1994) Practical neural network recipes in C++. Academic Press, Boston

    Google Scholar 

  76. Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236

    Article  Google Scholar 

  77. Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):861–867

    Article  Google Scholar 

  78. Hajihassani M (2013). Tunneling-induced ground movement and building damage prediction using hybrid artificial neural networks. PhD Thesis, Universiti Teknologi Malaysia

  79. Mendes R, Cortes P, Rocha M, Neves J (2002) Particle swarms for feed forward neural net training. In: Proceedings of IEEE international joint conference on neural networks, Honolulu, HI, USA, 12–17 May 2002, pp 1895–1899

  80. Kennedy J, Clerc M (2006) Standard PSO 2006. http://www.particleswarm.info/Standard-PSO-2006.c

  81. Clerc M (2011) Standard particle swarm optimisation, from 2006 to 2011. http://clerc.maurice.free.fr/pso/SPSO_descriptions.pdf (2011-07-13 version)

  82. Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96(3):141–158

    Article  Google Scholar 

  83. Clerc M, Kennedy J (2002) The particle swarm explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  84. Kalatehjari R, Ali N, Kholghifard M, Hajihassani M (2014) The effects of method of generating circular slip surfaces on determining the critical slip surface by particle swarm optimization. Arab J Geosci 7(4):1529–1539

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Behrouz Gordan & Danial Jahed Armaghani

  2. Construction Research Alliance, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Mohsen Hajihassani

  3. Department of Mining, Tarbiat Modares University, Tehran, 14115-143, Iran

    Masoud Monjezi

Authors
  1. Behrouz Gordan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohsen Hajihassani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masoud Monjezi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behrouz Gordan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gordan, B., Jahed Armaghani, D., Hajihassani, M. et al. Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers 32, 85–97 (2016). https://doi.org/10.1007/s00366-015-0400-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-015-0400-7

Keywords

Search

Navigation