A multivariate adaptive regression spline approach for predcition of maximum shear modulus (G max) and minimum damping ratio (ζ min)

Engineering Journal

Volumn 16, Issue 5, 2012, Pages 69-77

  Samui, Pijush ^a   Kothari, Dwarkadas Pralhaddas ^b  

^a VIT UNIVERSITY   (India)

^b Raisoni Group   (India)

Author keywords

Adaptive neuro fuzzy inference system; Maximum shear modulus; Minimum damping ratio; Multi layer perceptron; Multivariate adaptive regression spline; Prediction

Indexed keywords

EID: 84867466477     PISSN: 01258281     EISSN: None     Source Type: Journal    
DOI: 10.4186/ej.2012.16.5.69     Document Type: Article

References (19)

1. A. McGown, K. Z. Andrawes, and M. M. Al-Hasani,. "Effect of inclusion properties on the behavior of a sand, " Geotechnique, vol. 28, no. 3, pp. 237-346, 1978.
2. N. C. Consoli, P. D. M. Prietto, and L. A. Ulbrich, "The behavior of a fiber-reinforced cemented soil, " Ground Improv, vol. 3, pp. 21-30, 1999.
3. A. J. Puppala and C. Musenda, "Effects of fiber reinforcement on strength and volume change in expansive soils, " Transport Res Rec, vol. 1736, pp. 134-40, 2000.
4. J. J. Murray, J. D. Frost, and Y. Wang, "Behavior of a sandy silt reinforced with discontinuous recycled fiber inclusions, " Transport Res Rec, vol. 1714, pp. 9-17, 2000.
5. R. L. Santoni, J. S. Tingle, and S. Webster, "Engineering properties of sand-fiber mixtures for road construction, " Journal of Geotechnical and Geoenvironmental Engineering, vol. 127, no. 3, pp. 258-68, 2001.
6. S. Akbulbut, A. S. Hsiloglu, and S. Pamuken, "Data generation for shear modulus and damping ration in reinforced sands using adaptive neuro-fuzzy inference system, " Soil Dynam Earthquake Eng, vol. 24, pp. 805-814, 2004.
7. J. H. Friedman, "Multivariate adaptive regression splines, " The Annals of Statistics, vol. 19, pp. 1-14, 1991.
8. T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning. New York: Springer-Verlag, 2003.
9. P. A. W. Lewis and J. G. Stevens, "Nonlinear modeling of time series using multivariate adaptive regression splines (mars), " Journal of the American Statistical Association, vol. 86, no. 416, pp. 864-877, 1991.
10. T. Ekman and G. Kubin, "Nonlinear prediction of mobile radio channels: measurements and mars model designs, " in Proceedings of IEEE International Conference on Acoustics, Speech, Signal Processing, 1999, vol. 5, pp. 2667-2670.
11. C. C. Yang, S. O. Prasher, R. Lacroix, and S. H. Kim, "Application of multivariate adaptive regression splines (mars) to simulate soil temperature, " Transactions of the ASAE, vol. 47, no. 3, pp. 881-887, 2004.
12. S. Crino and D. E. Brown, "Global optimization with multivariate adaptive regression splines, " IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 37, no. 2, pp. 333-340, 2007.
13. E. Deconinck, D. Coomans, and V. Y. Heyden, "Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs, " Journal of Pharmaceutical and Biomedical Analysis, vol. 43, pp. 119-130, 2007.
14. N. O. Attoh-Okine, K. Cooger, and S. Mensah, "Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling, " Construction and Building Materials, vol. 23, no. 9, pp. 3020-3023, 2009.
15. J. D. Andrés, P. Lorca, F. J. D. C. Juez, and F. S. Lasheras, "Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS), " Expert Systems with Applications, vol. 38, pp. 1866-1875, 2011.
16. F. Vidoli, "Evaluating the water sector in Italy through a two stage method using the conditional robust nonparametric frontier and multivariate adaptive regression splines, " European Journal of Operational Research, vol. 212, pp. 583-595, 2011.
17. S. Sekulic and B. R. Kowalski, "MARS: A tutorial, " J. Chemom., vol. 6, pp. 199-216, 1992.
18. P. Craven and G. Wahba, "Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation, " Numer Math, vol. 31, pp. 317-403, 1979.
19. S. Y. Liong, W. H. Lim, and G. N. Paudyal, "River stage forecasting in Bangladesh: neural network approach, " Journal of Computing in Civil Engineering, vol. 14, no. 1, pp. 1-8, 2000.