Predicting the Collapsibility Potential of Unsaturated Soils Using Adaptive Neural Fuzzy Inference System and Particle Swarm Optimization

Document Type : Article

Authors

1 Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

2 Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

Abstract

Soil collapsibility is one of the important phenomena in unsaturated soil mechanics. This phenomenon can impose extensive financial damages on civil engineering structures due to soil subsidence. Because of uncertainties in effective parameters and their measurements, no precise mathematical relation has been proposed for collapsibility potential evaluation. Therefore, soft computing techniques such as fuzzy logic could be a suitable choice to account for different factors. Adaptive neural fuzzy inference system (ANFIS) was used in this study. To predict the collapsibility potential, hybrid algorithm and particles swarm optimization (PSO) were employed by ANFIS for system training. Gaussian membership functions were utilized for fuzzifying the data. Also, data classification was performed in a subtractive form in the fuzzy inference system. A total of 327 laboratory data was used in particles swarm algorithm, 266 of which were chosen for training and 66 for testing. The obtained results showed the effects of different parameters and the rate of their changes in collapsibility potential. Moreover, comparison of different approaches of system training was done using correlation coefficient. The superiority of the proposed method and the utilized techniques was shown by comparing the results with the ones obtained by other researches.

Keywords

Main Subjects


References
1. Derbyshi, E. Geological hazards in loess terrains
whit particular reference to the loes regions of china",
Earth-science, Reviews, 54, pp. 31-60 (2001).
2. Jennings, J.E. and Knight, K. A guide to construction
on or with materials exhibiting additional settlements
M.M. Hasheminejad et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 2980{2996 2987
due to collapse of grain structure", Proceedings, Sixth
Regional Conference for Africa on Soil Mechanics and
Foundation Engineering, Johannesburg, pp. 99-105
(1975).
3. Clevenger, W.A. Experiences with loess as a foundation
material", Transactions American Society for
Civil Engineers, 123, pp. 51-80 (1959).
4. Gibbs, H.J. and Bara, J.P. Predicting surface subsidence
from basic soil test", A.S.T.M. Spec. Teach.
Pub., 322, pp. 231-246 (1962).
5. Denisov, N.Y. About the nature of high sensitivity of
quick clays", Osnov. Fudam. Mekh. Grunt, 5, pp. 5-8
(1964).
6. Fookes, P.G. and Best, R. Consolidation characteristics
of some late Pleistocene periodical metastable
soils of east Kent". Quarterly Journal of Engineering
Geology, 2, pp. 103-128 (1969).
7. Terzaghi, K., Theoretical Soil Mechanics, Wiley, New
York (1943).
8. Bishop, A.W., Alpan, I., Blight, G.E., and Donald,
I.B. Factors controlling the shear strength of cohesive
soils", Proc., ASCE Res. Conf., New York, pp. 503-532
(1960).
9. Burland, J.E. E ective stresses in partly saturated
soils discussion on some aspects of e ective stresses
in saturated and partly saturated soils by G.E. Blight
and A.W. Bishop", Geotechnique, London, 14, pp. 65-
68 (1964).
10. Khademi, F., Akbari, M., and Jamal, S.M. Prediction
of concrete compressive strength using ultrasonic pulse
velocity test and arti cial neural network modeling",
Revista Romana de Materiale, 46(3), p. 343 (2016)
11. Zorlu, K. and Gokceoglu, C. Prediction of the collapse
index by a Mamdani fuzzy inference system",
In: Lovrek, I., Howlett, R.J., and Jain, L.C. (Eds.)
Knowledge-Based Intelligent Information and Engineering
Systems, KES, Lecture Notes in Computer
Science, 5177, Springer, Berlin (2008)
12. Momeni, M., Sha ee, A., Heidari, M., Jafari, M.K.,
and Mahdavifar, M.R. Application of fuzzy set theory
in evaluation of soil collapse potential", IJST, Transactions
of Civil Engineering, 35(C2), pp. 271-275 (2011)
13. Kang, F. and Li, J. Arti cial bee colony algorithm
optimized support vector regression for system reliability
analysis of slopes", Journal of Computing in Civil
Engineering, 30(3), pp. 41-54 (2016).
14. Basma, A.A. and Tuncer, E.R. Evaluation and control
of collapsible soils", J. Geotech. Engrg. Asce,
118(10), pp. 1491-1504 (1992).
15. Habibagahi, Gh. and Taherian, M. Prediction of
collapse potential for compacted soils using arti cial
neural networks", Scientia Iranica, 11(12), pp. 1-20
(2004).
16. Zhang, Y., Gallipoli, D., and Augarde, C.E. Parameter
identi cation for elasto-plastic modelling of
unsaturated soils from pressure meter tests by parallel
modi ed particle swarm optimization", Computers and
Geotechnics, 48, pp. 293-303 (2013).
17. Saeedi, E., Mahboubi Ardekani, A.R., and Rahami, H.
Determine the critical failure surface in the homogeneous
slopes by using the particle swarm optimization
algorithm (PSO)", Science Road Journal, 08, pp. 23-
33 (2014).
18. Jalalvandi, M. and Kashani, A. Design of reinforcement
length in reinforced slope using particle swarm
optimization algorithm", International Research Journal
of Applied and Basic Sciences, 8(9), pp. 1158-1164
(2014).
19. Kang, F., Xu, Q., and Li, J. Slope reliability analysis
using surrogate models via new support vector machines
with swarm intelligence", Applied Mathematical
Modelling, 40(11), pp. 6105-6120 (2016).
20. Kang, F., Li, J., and Li, J. System reliability analysis
of slopes using least squares support vector machines
with particle swarm optimization", Neurocomputing,
209, pp. 46-56 (2016).
21. Dudley, J.H. Review of collapsing soils", J. Soil Mech.
Found. Div., ASCE, 96(3), pp. 925-947 (1970).
22. Jang, J.-R. ANFIS: adaptive-network-based fuzzy
inference system", in IEEE Transactions on Systems,
Man, and Cybernetics, 23(3), pp. 665-685 (May-June
1993). DOI: 10.1109/21.256541
23. Jang, J.-R. and Chuen-Tsai Sun Neuro-fuzzy modeling
and control", in Proceedings of the IEEE, 83(3),
pp. 378-406 (March 1995). DOI: 10.1109/5.364486
24. Khademi, F., Jamal, M., Deshpande, N., and Londhe,
Sh. Predicting strength of recycled aggregate concrete
using arti cial neural network, adaptive neuro-fuzzy
inference system and multiple linear regression", International
Journal of Sustainable Built Environment,
5(2), pp. 355-369 (2016).
25. Sugeno, M. and Yasukawa, T.A. fuzzy-logic-based
approach to qualitative modeling", IEEE T Fuzzy
Syst., 1(1), pp. 7-31 (1993).
26. Takagi, T. and Sugeno, M. Fuzzy identi cation of
systems and its applications to modeling and control",
IEEE Trans Syst. Man Cybern., 15(1), pp. 116-13
(1985).
27. Salagegheh, E., Salagegheh, J., Seyedpoor, S., and
Khatibinia, M. Optimal design of geometrically nonlinear
space trusses using an adaptive neuro-fuzzy
inference system", Scientia Iranica, Transactions A:
Civil Engineering, 16(5), pp. 403-14 (2009).
28. Zeighami, V., Akbari, R., and Ziarati, K. Development
of a method based on particle swarm optimization
to solve resource constrained project scheduling
problem", Scientia Iranica, 20(6), pp. 2123-2137
(2013).