Abstract
Construction of highway roads, railways and other engineering structures on soft clay soils normally encounters problems related to excessive settlement issues. The conventional methods are inadequate to analyze and to predict the settlement behavior. Artificial neural network systems are included to predict settlement under embankment load using soft soil properties together with various geometric parameters as inputs for each stone column arrangement and embankment conditions. A case study site investigated field data are taken from a highway project Lebuhraya Pantai Timur2 in Terengganu, Malaysia. Actual angle of internal friction (ϕ), spacing ratio (s/D), cylindrical ratio (L/D) and height of the embankment (H) were used as the input parameters, while the settlement ratio was the main output. The properties of materials on a stone column (ϕ) have high relative importance (40.15 %) compared with the other parameters. Two techniques namely non-cross-validation (β NCV) and ten-fold cross-validation (β FCV) were used to build the ANN model. The β FCV model gives higher efficiency of 0.985 for training and 0.939 for testing, while β NCV model gives 0.937 and 0.905. The β FCV model provides results of greater accuracy as compared to the β NCV models.
Similar content being viewed by others
References
Aboshi H, Ichimoto E, Enoki M, Proc. KH (1979) The compozer-a method to improve characteristics of soft clays by inclusion of large diameter sand columns. In: International conference on soil reinforcement: reinforced earth and other techniques, Paris, pp 211–216
Barksdale RD, Bachus RC (1983) Design and construction of stone columns, vol I, and vol II. FHWA/RD-83/026, Federal Highway Administration, Washington, DC
Barksdale RD, Takefumi T (1990) Design, construction and testing of sand compaction piles, symposium on deep foundation improvements: design, construction and testing. ASTM Publications, Las Vegas
Bergado DT, Anderson LR, Miura N, Balasubramaniam AS (1996) Improvement of soft Bangkok clay using vertical drains. Geotext Geomembr 12:615–663
Priebe HJ (1995) The design of vibro-replacement. In: Proceedings of the Institution of Civil Engineers, Ground Engineering, vol 10, pp 31–37
Guetif Z, Bouassida M, Debats JM (2007) Improved soft clay characteristics due to stone column installation. Comput Geotech 34(2):104–111. doi:10.1016/j.compgeo.2006.09.008
Deb K, Chandra S, Basudhar PK (2008) Response of multilayer geosynthetic—reinforced bed resting on soft soil with stone columns. Comput Geotech 35(3):323–330
Zhou C, Yin J-H, Ming J-P (2002) Bearing capacity and settlement of weak fly ash ground improved using lime—fly ash or stone columns. Canad Geotech J 39(3):585–596. doi:10.1139/t02-011
Cimentada A, Da Costa A, Cañizal J, Sagaseta C (2011) Laboratory study on radial consolidation and deformation in clay reinforced with stone columns. Can Geotech J 48(1):36–52. doi:10.1139/T10-043
Zahmatkesh A, Choobbasti AJ (2010) Settlement evaluation of soft clay reinforced with stone columns using the equivalent secant modulus. Arab J Geosci 5(1):103–109. doi:10.1007/s12517-010-0145-y
Bo MW, Choa V (2004) Reclamation and ground improvement. Thomson Learning, Singapore
Lo SR, Zhang R, Mak J (2010) Geosynthetic-encased stone columns in soft clay: a numerical study. Geotext Geomembr 28(3):292–302. doi:10.1016/j.geotexmem.2009.09.015
Rumelhart DE, McClelland JL, Group tPR (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol (chapter 8) volume 1. Foundations. MIT Press, Cambridge
Haykin S (1998) Neural networks: a comprehensive foundation. Prentice Hall, New Jersey
Shahin MA, Indraratna B (2006) Modeling the mechanical behavior of railway ballast using artificial neural networks. Can Geotech J 43(11):1144–1152. doi:10.1139/t06-077
Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2(4):311–319. doi:10.1007/s12517-009-0035-3
Torabi SR, Shirazi H, Hajali H, Monjezi M (2011) Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS. Arab J Geosci. doi:10.1007/s12517-011-0415-3
Salmasi F, Yıldırım G, Masoodi A, Parsamehr P (2012) Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques. Arab J Geosci. doi:10.1007/s12517-012-0540-7
Vafakhah M (2012) Comparison of cokriging and adaptive neuro-fuzzy inference system models for suspended sediment load forecasting. Arab J Geosci. doi:10.1007/s12517-012-0550-5
Najah AA, El-Shafie A, Karim OA, Jaafar O (2010) Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation. Neural Comput Appl 21(5):833–841. doi:10.1007/s00521-010-0486-1
Soyupak S, Karaer F, Gurbuz H, Kivrak E, Senturk E, Yazici A (2003) A neural network based approach for calculating dissolved oxygen profiles in reservoirs. Neural Comput Appl 12:166–172
Priebe HJ (1991) Vibro-replacement—design criteria and quality control. Deep foundation improvements design, construction, and testing. ASTM STP 1089, ASTM
Arukrajah A, Affendi A (2002) Vibro replacement design of high-speed railway embankments. Paper presented at the 2nd World Engineering Congress. Kuching, Malaysia
Singh KP, Basant A, Malik A, Jain G (2009) Artificial neural network modeling of the river water quality—a case study. Ecol Model 220:888–895
Attoh-Okine N (2002) Combining use of rough set and artificial neural network in doweled-pavement performance modelling-a hybrid approach. J Transp Eng (ASCE) 128(3):270–275
Demuth H, Beale M, Hagan M (2008) Neural network toolbox: user’s guide, version 6, The Mathworks, Inc. http://www.mathworks.com
Beale R, Jackson T (1990) Neural computing: an introduction. Institute of Publishing, Bristol
Flood I, Kartam N (1994) Neural network in civil engineering. I: principles and understandings. J Comput Civil Eng (ASCE) 8(2):131–148
Flood I, Kartam N (1994) Neural network in civil engineering. II: systems and applications. J Comput Civil Eng (ASCE) 8(2):149–162
Banimahd M, Yasrobi SS, Woodward PK (2005) Artificial neural network for stress–strain behavior of sandy soils: knowledge based verification. Comput Geotech 32:377–386
Shahin MA, Maier HR, Jaksa MB (2004) Data division for developing neural networks applied to geotechnical engineering. J Comput Civil Eng (ASCE) 18(2):105–114
Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, New York
Mitchell T (1997) Machine learning Burr Ridge. McGraw Hill, New York
Haykin S (1999) Neural networks. A comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River
Goh A (1994) Seismic liquefaction potential assessed by neural network. J Geotech Eng 120(9):1467–1480
Shahin MA, Jaska MB, Maier HR (2002) Predicting settlement of shallow foundation using neural networks. J Geotech Geoenviron Eng (ASCE) 128(9):785–793
Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model 178:389–397
Garson GD (1998) Neural networks an introductory guide for social scientists. Sage Publications, California
Elmolla ES, Chaudhuri M, Eltoukhy MM (2010) The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. J Hazard Mat 179(1–3):127–134. doi:10.1016/j.jhazmat.2010.02.068
Acknowledgments
The authors would like to thank Universiti Kebangsaan Malaysia (UKM) for GUP-2012-03 research grant in this work and Department of Civil and Structural Engineering, for the use of computer laboratory.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chik, Z., Aljanabi, Q.A. Intelligent prediction of settlement ratio for soft clay with stone columns using embankment improvement techniques. Neural Comput & Applic 25, 73–82 (2014). https://doi.org/10.1007/s00521-013-1449-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-013-1449-0