Skip to main content

Prediction of uplift capacity of suction caissons using a neuro-genetic network

Abstract

Suction caissons are frequently used for the anchorage of large offshore structures. The uplift capacity of the suction caissons is a critical issue that needs to be predicted reliably. A neuro-genetic model has been employed for this purpose. The neuro-genetic model uses the multilayer feed forward neural network (NN) as its host architecture and employs genetic algorithms to determine its weights. In comparison to the application of a conventional NN model [49] for the uplift capacity prediction problem, the application of a hybrid model such as the neuro-genetic network appears attractive. The conventional NN model is sensitive to training parameters and initial conditions and calls for a longer training of the network. Also it is not free of the inherent problem of settling for the local minimum in the neighborhood of the initial solution. In contrast, the hybrid model is much less sensitive to training parameters and initial conditions and inherently looks for a global optimum in a complex search space, which may be multimodal or non-differentiable, with a modest amount of training. The performance of the neuro-genetic model has been studied in detail over specific data sets pertaining to suction caissons, gathered from 12 independent studies [49] and compared with the predictions made by NN and finite element method models.

This is a preview of subscription content, access via your institution.

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

References

  1. Haykin S (1994) Neural networks: a comprehensive foundation. IEEE Computer Society Press, Macmillan

    MATH  Google Scholar 

  2. Tsoukalas LH, Uhrig RE (1997) Fuzzy and neural approaches in engineering. Wiley, New York

    Google Scholar 

  3. Schwefel HP (1995) Evolution and optimum seeking. Wiley, New York

    Google Scholar 

  4. Fogel DB (1991) System identification through simulated evolution: a machine learning approach to modeling. Needham Heights, Ginn, MA

    Google Scholar 

  5. Holland JL (1973) Genetic algorithms and the optimal allocation of trials. SIAM J Comput 2(2):88–105

    Article  MATH  MathSciNet  Google Scholar 

  6. Holland JL (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  7. Gen M, Cheng R (1997) Genetic algorithm and engineering design. Wiley, New York

    Google Scholar 

  8. Harp S, Samad T, Guha A (1989) Towards the genetic synthesis of neural networks. Proceedings of the third international conference on genetic algorithms. Morgan Kaufmann, San Mateo

  9. Montana D, Davis L (1989) Training feed forward neural networks using Genetic algorithms. In: 11th joint international conference on AI. IJCAI-11, pp 762–767

  10. Whitley D, Bogart C (1989) Optimizing neural networks using faster more accurate genetic search. In: Proceedings of the third international conference on genetic algorithms, Morgan Kaufmann, San Mateo

  11. Whitley D, Bogart C (1990) The evolution of connectivity: pruning neural networks using genetic algorithms. In: Proceedings of IJCNN-90, Washington

  12. Whitley D, Starkwerther T (1990) Optimizing small neural networks using a distributed genetic algorithm. In: Proceedings of IJCNN-90, Washington

  13. Kitano H (1990) Empirical studies on the speed of convergence of neural networks training using genetic algorithms. In: Proceedings of the eighth national conference on AI, pp 789–795

  14. Schiffman W, Joost M, Werner R (1993) Application of GAs to the construction of topologies for multilayer perceptrons. In: Proceedings of the international conference on ANNs and GAs, ICANNGA 93, pp 675–682

  15. Maniezzo V (1994) Genetic evolution of the topology of weight distribution of neural networks. IEEE trans Neural Networks 5(1):39–53

    Article  Google Scholar 

  16. Schaffer JD, Whitley D, Eshelman LJ (1992) Combination of genetic algorithms and neural networks: a survey of state of the art. In: Proceedings of international works on combinations of genetic algorithms and neural networks, pp 1–37

  17. Yao Xin (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423–1447

    Article  Google Scholar 

  18. Montana DJ (1995) Neural network weight selection using genetic algorithms. In: Khebbal S, Goonatilake S (eds) Intelligent hybrid systems. Wiley, New York

    Google Scholar 

  19. Montana DJ (1992) A weighted probabilistic neural network. Advances in Neural Information Processing Syst 4:1110–1117

    Google Scholar 

  20. Schaffer JD, Caruana RA, Eshelman LJ (1990) Using genetic search to exploit the emerging behaviour of neural networks. In: Forrest S (ed) Emergent computation, pp 102–112

  21. Koza JR, Rice JP (1991) Genetic generation of both the weights and architecture for a neural network. In: Proceedings of the IEEE joint conference on neural networks, pp 397–404

  22. Bornholdt S, Graudenz D (1991) Asymmetric neural networks and structure design by genetic algorithms, Deutsches Electron-Synchrotron

  23. Collins R, Jefferson D (1991) An artificial neural network representation for artificial organisms. Parallel problem solving from nature

  24. Grua F (1992) Genetic synthesis of boolean neural networks with a cell rewriting development process. In: Proceedings of the IEEE workshop on combinations of genetic algorithms and neural networks

  25. Whitley D, Starkwerther T, Bogart C (1990) Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Comput 14(3):347–361

    Article  Google Scholar 

  26. Belew RK, McInerney J, Schraudolph NN (1992) Evolving networks: using the genetic algorithm with connectionist learning. Artif Life II, pp 511–547

    Google Scholar 

  27. Rajasekaran S, Vijayalakshmi Pai GA (1996) Genetic algorithm based weight determination for Backpropagation network. In: Proceedings of the fourth international conference on advanced computing, Tata McGraw Hill, pp 73–79

  28. Rajasekaran S, Vijayalakshmi Pai GA (2003) Neural networks, fuzzy logic and genetic algorithms: synthesis and applications. Prentice Hall, India

    Google Scholar 

  29. Karunanithi N, Das R, Whitley D (1992) Genetic cascade learning for neural networks. In: Proceedings of the IEEE workshop on combinations of genetic algorithms and neural networks

  30. Potter MA (1992) A genetic cascade correlation learning algorithm. In: Proceedings of the IEEE workshop on combinations of GA and NN

  31. Wieland AP (1991) Evolving neural network controllers for unstable systems. IEEE joint conference on neural networks, pp 667–673

  32. Torreele J (1991) Temporal processing with recurrent networks: an evolutionary approach. In: Proceedings of the fourth international conference on GA, pp 555–561

  33. Hochman R, Khoshgoftaar TM, Allen EB, Hudepohl JP (1997) Evolutionary neural networks: a robust approach to software reliability problems. In: Proceedings of the 8th international symposium software reliability Engneering. IEEE Comput Soc, pp 13–26

  34. Goldberg D (1988) Genetic algorithm in search, optimization and machine learning. Addison Wesley, California

    Google Scholar 

  35. Spears WM, De Jong KA (1990) An analysis of multi-point crossover. In: Rawlins GJE (ed) Foundations of genetic algorithms. Morgan Kaufmann, California, pp 310–315

    Google Scholar 

  36. Srinivas M, Patnaik LM (1991) Learning neural network weights using genetic algorithms-improving performance by search space reduction. In: Proceedings of the 1991 IEEE international joint conference on neural networks (IJCNN 91), 3:2331–2336

  37. Janson DJ, Frenzel JF (1993) Training product unit neural networks with genetic algorithms. IEEE Expert 8:26–33

    Article  Google Scholar 

  38. Menczer F, Parisi D (1992) Evidence of hyperplanes in the genetic learning of neural networks. Biol Cybern 66:283–289

    Article  PubMed  Google Scholar 

  39. Antonisse J (1989) A new interpretation of schema notation that overturns the binary encoding constraint. In: Schaffer JD (ed), Proceedings of 3rd international conference genetic algorithms and their applications. Morgan Kaufmann, San Fransisco, pp 86–91

  40. Fogel DB, Fogel LJ, Porto VW (1990) Evolving neural networks. Biol Cybern 63(6):487–493

    Article  Google Scholar 

  41. Porto VW, Fogel DB, Fogel LJ (1995) Alternative neural network training methods. IEEE Expert 10:16–22

    Article  Google Scholar 

  42. Saravanan N, Fogel DB (1995) Evolving neural control systems. IEEE Expert 10:23–27

    Article  Google Scholar 

  43. Porto VW, Fogel DB (1990) Neural network techniques for navigation of AUVs. In: Proceedings of IEEE symposium on autonomous underwater vehicle technology, pp 137–141

  44. Whitley D, Dominic S, Das R (1991) Genetic reinforcement learning with multi-layer neural networks. In: Proceedings of the 4th international conference on genetic algorithms, pp 562–569

  45. Syswerda G (1989) Uniform crossover in genetic algorithms. In: Proceedings of the 3rd international conference on genetic algorithms, pp 2–9

  46. Johansson EM, Dowla FU, Goodman DM (1991) Backpropagation learning for multi-layer feedforward neural networks using the conjugate gradient method. Int J Neural Syst 2(4):291–301

    Article  Google Scholar 

  47. Deng W, Carter JP (2000) Inclined uplift capacity of suction caissons in sand. In: Proceedings of Offshore technology conference, Houston, CD Paper No. 12196

  48. Deng W, Carter JP (2000) Uplift capacity of suction anchors in uniform soils. In: Proceedings of Geological Engineering, Melhourne

  49. Rahman MS, Wang J, Deng W, Carter JP (2001) A neural network model for the uplift capacity of suction caissons. Comput Geotech 28:269–287

    Article  Google Scholar 

  50. Fahlman SE (1988) Faster learning variation on backpropagation: an empirical study. In: Proceedings of the 1988 connectionist models summer school, pp 38–51

  51. Prados DL (1992) Training multi-layered neural networks by replacing the least fit hidden neurons. In: Proceedings of the IEEE SOTHEAST-CON 92, 2:634–637

  52. Sexton RS, Dorsey RE, Johnson JD (1998) Towards global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decision Support Syst 22(2):171–185

    Article  Google Scholar 

  53. Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press, Oxford

    Google Scholar 

Download references

Acknowledgements

The author expresses her sincere thanks to All India Council of Technical Education, New Delhi for supporting this research under the Project Grant of AICTE Career Award for Young Teachers, 2001.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. A. Vijayalakshmi Pai.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Pai, G.A.V. Prediction of uplift capacity of suction caissons using a neuro-genetic network. Engineering with Computers 21, 129–139 (2005). https://doi.org/10.1007/s00366-005-0315-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-005-0315-9

Keywords

  • Neural networks
  • Genetic algorithms
  • Neuro-genetic networks
  • Suction caissons
  • Prediction of uplift capacity