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Modelling the Pull-out Capacity of Ground Anchors Using Multi-objective Feature Selection

  • Research Article - Civil Engineering
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Abstract

Pull-out capacity of ground anchors is analogous to axial capacity of piles as both of them apply same methods. These available methods are mostly empirical; therefore, in this paper efficient prediction models for determining the uplift capacity of small ground anchors have been presented using recently developed artificial intelligence (AI) techniques. Multi-objective feature selection (MOFS) has been utilised to find the subset of influential parameters responsible for the pull-out capacity of ground anchors along with the development of prediction equations. MOFS has been applied with artificial neural network and non-dominated sorting genetic algorithm. Prediction models are also presented using two other AI techniques: functional network and multi-variate adaptive regression spline. AI models were compared in terms of different statistical parameters such as mean absolute error, root-mean-square error, correlation coefficient and ranking criterion approach have been implemented to assess the performance of different prediction models.

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References

  1. Bustamante, M.; Gianeselli, L.: Pile bearing capacity prediction by means of static penetrometer CPT. In: Proceedings of the 2nd European Symposium on Penetration Testing, Amsterdam, pp. 493–500 (1982)

  2. Das, B.M.: Principles of Foundation Engineering, 3rd edn. PWS Publishing Company, Boston (1995)

    Google Scholar 

  3. Bowles, J.E.: Foundation Analysis and Design. Mc-Graw-Hill, New York (1997)

    Google Scholar 

  4. Soleimanbeigi, A.; Hataf, N.: Prediction of settlement of shallow foundations on reinforced soils using neural networks. Geosynth. Int. 13(4), 161–170 (2006)

    Article  Google Scholar 

  5. Ching, J.; Liao, H.J.; Lee, J.Y.: Predicting rainfall-induced landslide potential along a mountain road in Taiwan. Géotechnique 61(2), 153–166 (2011)

    Article  Google Scholar 

  6. Rakhshandehroo, G.R.; Vaghefi, M.; Aghbolaghi, M.A.: Forecasting groundwater level in Shiraz plain using artificial neural networks. Arab. J. Sci. Eng. 37, 1871–1883 (2012)

    Article  Google Scholar 

  7. Sarıdemir, M.; Severcan, M.H.: The use of genetic programming and regression analysis for modeling the modulus of elasticity of NSC and HSC. Arab. J. Sci. Eng. 41(10), 3959–3967 (2016)

    Article  Google Scholar 

  8. Sedighi, F.; Vafakhah, M.; Javadi, M.R.: Rainfall–runoff modeling using support vector machine in snow-affected watershed. Arab. J. Sci. Eng. 41(10), 4065–4076 (2016)

    Article  Google Scholar 

  9. Khairi, M.T.M.; Ibrahim, S.; Yunus, M.A.M.; Faramarzi, M.; Yusuf, Z.: Artificial neural network approach for predicting the water turbidity level using optical tomography. Arab. J. Sci. Eng. 41(9), 3369–3379 (2016)

    Article  Google Scholar 

  10. Lau, D.S.; Simmons, J.V.: Interpretation of field tests on small-scale ground anchors. In: Speciality Geomechanics Symposium: Interpretation of Field Testing for Design Parameters, vol. 1 and 2. National conference publication (Institution of Engineers, Australia), Barton, Australia, pp. 85–88 (1986)

  11. Shahin, M.A.; Jaksa, M.B.: Modelling the pull-out capacity of marquee ground anchors. School of Civil and Environmental Engineering, The University of Adelaide, Adelaide, Research Report No. R174 (2003)

  12. Shahin, M.A.; Jaksa, M.B.: Neural network prediction of pull-out capacity of marquee ground anchors. Comput. Geotech. 32, 153–163 (2005)

    Article  Google Scholar 

  13. Shahin, M.A.; Jaksa, B.: Pull-out capacity of small ground anchors by direct cone penetration test methods and neural networks. Can. Geotech. J. 43, 626–637 (2006)

    Article  Google Scholar 

  14. Samui, P.; Kim, D.; Aiyer, G.: Pull-out capacity of small ground anchor: a least square support vector machine approach. J. Zhejiang Univ. Sci. A (Appl. Phys. Eng) 16(4), 295–301 (2015)

    Article  Google Scholar 

  15. Shahin, M.A.: Use of evolutionary computing for modelling some complex problems in geotechnical engineering. Geomech. Geoeng. 10(2), 109–125 (2015)

    Article  Google Scholar 

  16. Das, S.K.: Artificial neural networks in geotechnical engineering: modeling and application issues. In: Yang, X., Gandomi, A.H., Talatahari, S., Alavi, A.H. (eds.) Metaheuristics in Water, Geotechnical and Transport Engineering, pp. 231–270. Elsevier, London (2013)

    Chapter  Google Scholar 

  17. Shalan, S.A.B.; Ykhlef, M.: Solving multi-objective portfolio optimization problem for Saudi Arabia stock market using hybrid clonal selection and particle swarm optimization. Arab. J. Sci. Eng. 40, 2407–2421 (2015)

    Article  MathSciNet  Google Scholar 

  18. Zhang, K.; Yuan, F.; Guo, J.; Wang, G.: A novel neural network approach to transformer fault diagnosis based on momentum-embedded BP neural network optimized by genetic algorithm and fuzzy c-means. Arab. J. Sci. Eng. (2015). doi:10.1007/s13369-015-2001-6

    Google Scholar 

  19. Guyon, I.; Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  20. Yang, Y.; Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceedings of Fourteenth International Conference on Machine Learning (ICML’97), vol. 97, Nashville, Tennessee, USA, pp. 412–420 (1997)

  21. Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)

    MATH  Google Scholar 

  22. Bolasso, F.R.B.: Model consistent Lasso estimation through the bootstrap. In: McCallum, A., Roweis, S.T. (ed.) Proceedings of 25th International conference on Machine learning, (ICML2008), Helsinki, Finland, pp. 33–40 (2008)

  23. Zare, H.; Haffari, G.; Gupta, A.; Brinkman, R.R.: Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis. BMC Genom. 14, S14 (2013)

    Article  Google Scholar 

  24. Maruf, S.; Javed, K.; Babri, H.A.: Improving text classification performance with random forests-based feature selection. Arab. J. Sci. Eng. 41, 951–964 (2016)

    Article  Google Scholar 

  25. He, X.; Zhang, Q.; Sun, N.; Dong, Y.: Feature selection with discrete binary differential evolution. In: Proceedings of International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, vol. 4, art. no. 5376334, Shanghai, pp. 327–330 (2009)

  26. Zhu, Z.X.; Ong, Y.S.; Dash, M.: Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 37(1), 70–76 (2007)

    Article  Google Scholar 

  27. Neshatian, K.; Zhang, M.: Pareto front feature selection: using genetic programming to explore feature space. In: The 11th Annual Conference on Genetic and Evolutionary Computation (GECCO 2009), pp. 1027–1034 (2009)

  28. Cervante, L.; Xue, B.; Zhang, M.; Shang, L.: Binary particle swarm optimisation for feature selection: a filter based approach. In: Proceedings of Evolutionary Computation (CEC), 2012 IEEE Congress, art. no. 6256452, Brisbane, QLD, pp. 881–888 (2012)

  29. Xue, B.; Cervante, L.; Shang, L.; Browne, W.N.; Zhang, M.: A multi-objective particle swarm optimisation for filter based feature selection in classification problems. Connect. Sci. 24(2–3), 91–116 (2012)

    Article  Google Scholar 

  30. Xue, B.; Cervante, L.; Shang, L.; Browne, W.N.; Zhang, M.: Binary PSO and rough set theory for feature selection: a multi-objective filter based approach. Int. J. Comput. Intell. Appl. 13(02), 1450009 (2014)

  31. Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  32. Castillo, E.; Cobo, A.; Gutierrez, J.M.; Pruneda, E.: An introduction to functional networks with applications. Kluwer, Boston (1998)

    MATH  Google Scholar 

  33. Castillo, E.; Cobo, A.; Manuel, J.; Gutierrez, J.M.; Pruneda, E.: Functional networks: a new network-based methodology. Comput. Aided Civ. Infrastruct. Eng. 15, 90–106 (2000)

    Article  Google Scholar 

  34. Das, S.K.; Suman, S.: Prediction of lateral load capacity of pile in clay using multivariate adaptive regression spline and functional network. Arab. J. Sci. Eng. 40(6), 1565–1578 (2015)

    Article  Google Scholar 

  35. Friedman, J.: Multivariate adaptive regression splines. Ann. Stat. 19, 1–141 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  36. Smith, G.N.: Probability and Statistics in Civil Engineering: An Introduction. Collins, London (1986)

    Google Scholar 

  37. Abu-Farsakh, M.Y.; Titi, H.H.: Assessment of direct cone penetration test methods for predicting the ultimate capacity of friction driven piles. J. Geotech. Geoenv. Eng. 130(9), 935–944 (2004)

    Article  Google Scholar 

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Authors and Affiliations

  1. Civil Engineering Department, National Institute of Technology, Rourkela, Odisha, 769008, India

    Ranajeet Mohanty, Shakti Suman & Sarat Kumar Das

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  1. Ranajeet Mohanty
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  2. Shakti Suman
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  3. Sarat Kumar Das
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Correspondence to Sarat Kumar Das.

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Mohanty, R., Suman, S. & Das, S.K. Modelling the Pull-out Capacity of Ground Anchors Using Multi-objective Feature Selection. Arab J Sci Eng 42, 1231–1241 (2017). https://doi.org/10.1007/s13369-016-2361-6

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  • DOI: https://doi.org/10.1007/s13369-016-2361-6

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