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Modeling the compression index for fine soils using an intelligent method

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Research Paper 01/11/2014
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Modeling the compression index for fine soils using an intelligent method

Seyed Mahmood Kashefipour, Mehdi Daryaee
J. Bio. Env. Sci.5( 5), 197-204, November 2014.
Certificate: JBES 2014 [Generate Certificate]
Key: Artificial neural networkClay soilsCompression coefficientSoil physical properties

Abstract

Construction of buildings and different structures leads to soil consolidation, hence, causes soil settlement. Soil settlement depends on numerous factors, for instance, pressure deformation, depletion of pore water, and so forth. One way to calculate soil settlement, is utilizing compression index, which is obtained through consolidation test. Obtaining this index through consolidation test is too time-consuming; thus, researchers have attempted to relate compression index to soil physical parameters such as plasticity limit, liquid limit, void ratio, and relative density, which all could be simply measured; therefore, there is great deal of empirical relations in this regard. In this study, the correlation coefficients between the physical characteristics of fine soil and compression index were investigated using the Artificial Neural Network (ANN). A few but common empirical equations describing the relationship of compression index with other soil properties were evaluated along with the developed ANN model in this study. The results have indicated that among the considered empirical relations, the Rendon-Herrero formula performed better in calculating the compression index. By comparison, the ANN calculates the compression index more accurately and with less error than the Rendon-Herrero formula.

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Ahadian J. 2004. Estimation of the compression index with the aid of soil physical parameters in Ahwaz. M.Sc. thesis. Water Sciences Engineering Faculty, Shahid Chamran University of Ahwaz, Iran (In Persian).

Amarasinghe PM, Katti KS, Katti DR. 2012. Insight into role of clay-fluid molecular interactions on permeability and consolidation behavior of Na-montmorillonite swelling clay. Journal of Geotechnical and Geoenvironmental Engineering, ASCE 138(2), 138-146.

Di Matteo L, Bigotti F, Ricco R. 2011. Compressibility of Kaolinitic clay contaminated with ethanol-gasoline blends. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, Technical Note 137(9), 846-849.

Erzin Y, Gumaste SD, Gupta AK, Singh DN.2009.  Artificial  neural  network  (ANN)  models  for determining  hydraulic  conductivity  of  compacted fine-grained soils. Canadian Geotechnical Journal 46(8), 955-968.

Gautam MR, Zhu J, Ye M. 2011. Regularized artificial neural network training for biased data of soil hydraulic parameters. Soil Science 176(11), 567-575.

Hong CS, Shackelford CD, Malusis MA. 2012. Consolidation and hydraulic conductivity of zeolite-amended soil-Bentonite backfills. Journal of Geotechnical and Geoenvironmental Engineering, ASCE 138(1), 15-25.

Karimi Maddahi SS, Hassani M. 2012. Optimal Design of Insulators using Artificial Neural Network (ANN). Journal of Basic and Applied Scientific Research 2(1), 60-64.

Kashefipour SM, Lin B, Falconer FA. 2005. Neural networks for predicting seawater bacterial levels. Proceedings of the Institution of Civil Engineers, Water Management 158, 111-118.

Nagaraj T, Murty BRS. 1985. Predication of the pre-consolidation pressure and recompression index of soils. Geotechnical Testing Journal 8 (4), 199-202.

Namdar-Khojasteh D, Shorafa M, Omid M, Fazeli-Shaghani M. 2010. Application of artificial neural networks in modeling soil solution electrical conductivity. Soil Science 175(9), 432-437.

Nishida Y. 1956. A brief note on compression index of soil. Journal of Soil Mechanics and Foundation Engineering, ASCE 82(3), 1-14.

Park JH, Koumoto T. 2004. New compression index equation. Journal of geotechnical and geoenvironmental engineering 130(2), 223-226.

Rendon-Herrero O. 1980. Universal compression index equation. Journal of the Geotechnical Engineering Division 106(11), 1179-1200.

Sarangi A, Bhattacharya AK. 2005. Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India. Agricultural water management 78(3), 195-208.

Shahin MA, Jaksa MB, Maier HR. 2001. Artificial neural network applications in geotechnical engineering. Australian Geomechanics 36, 49-62.

Skempton AW. 1944. Notes on the compressibility of clays. Quarterly Journal of the Geotechnical Society 102(1-4), 205-209

Sverdrup HU. 1946. The humidity gradient over the sea surface. Journal of Meteorology 3(1), 1-8.

Tareghian R, Kashefipour SM. 2007. Application of fuzzy systems and artificial neural networks for flood forecasting. Journal of Applied Sciences 7(22), 3451-3459.

Terzaghi K, Peck RB. 1968. Soil mechanics in engineering practice, 2nd Ed. Wiley, New York.

Xiong C, Li T. 2011. Application of artificial neural networks to prediction of deformation in deep foundation pit. In Proceeding of meeting held on 26-28 July, Hangzhou, China, Multimedia Technology (ICMT), IEEE Conference Publications, pp. 1448-1453.

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