Predicting the Grouting Ability of Sandy Soils by Artificial Neural Networks Based On Experimental Tests

Document Type: Research Papers

Authors

1 Assistant Professor, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.

2 M.Sc. Student, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.

Abstract

In this paper, the grouting ability of sandy soils is investigated by artificial neural networks based on the results of chemical grout injection tests. In order to evaluate the soil grouting potential, experimental samples were prepared and then injected. The sand samples with three different particle sizes (medium, fine, and silty) and three relative densities (%30, %50, and %90) were injected with the sodium silicate grout with three different concentrations (water to sodium silicate ratio of 0.33, 1, and 2). A multi-layer Perceptron type of the artificial neural network was trained and tested using the results of 138 experimental tests. The multi-layer Perceptron included one input layer, two hidden layers and one output layer. The input parameters consisted of initial relative densities of grouted samples, the average size of particles (D50), the ratio of the grout water to sodium silicate and the grout pressure. The output parameter was the grout injection radius. The results of the experimental tests showed that the radius of grout injection is a complicated function of the mentioned parameters. In addition, the results of the trained artificial neural network showed to be reasonably consistent with the experimental results.

Keywords


Agrawal, G., Weeraratne, S. and Khilnani, K. (1994). “Estimating clay liner and cover permeability using computational neural networks”, Proceedings of the 1st Congress on Computing in Civil Engineering, Washington.

Akbulut, S. (1999). “The improvement of geotechnical properties in granular soils by grouting”, Ph.D. Thesis. The Institute of the Istanbul Technical University, Istanbul.

Army Corps of Engineers. (1995). “Engineer manual. Chemical Grouting”, EM 1110-1-3500.

Ata, A. and Vipulanandan, C. (1998). “Cohesive and adhesive properties of silicate grout on grouted- sand behavior”, Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 124(1), 38-44.

Ata, A. and Vipulanandan, C. (1999). “Factors affecting mechanical and creep properties of silicate-grouted sands”, Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 125(10), 868-876.

Balakrishnan, S.N. and Weil, R.D. (1986). “Neurocontrol: a literature survey”, Mathematical and Computer Modeling, 23(1-2), 101-117.

Banimahd, M., Yasrobi, S.S. and Woodward, P. (2005). “Artificial neural network for stress-strain behavior of sandy soils: knowledge based verification”, Computers and Geotechnics, 32(5), 377-386.

Basheer, I. and Hajmeer, M. (2000). “Artificial neural networks: fundamentals, computing, design, and application”, Journal of Microbiological Methods, 43, 3-31.

Bell, F.G. (1993). “Engineering Treatment of soils”, Spon, London.

Caglar, N. and Arman, H. (2007). “The applicability of neural networks in the determination of soil properties”, Bulletin of Engineering Geology and the Environment, 66, 295–301.

Cal, Y. (1995). “Soil classification by neural-network”, Advances in Engineering Software, 22(2), 95-97.

Dano, C., Hicher, P-Y. and Taillierz, S., (2004), "Engineering Properties of grouted sands", J. Geotecnique and Geoenvironmental Engineering, 130(3), 328-338.

Demuth H. and Beale M., (2003), "Neural network toolbox for use with MATLAB", The MathWorks Inc.

Erzin, Y., Rao, B.H. and Singh, D.N. (2008). “Artificial neural networks for predicting soil thermal resistivity”, International Journal of Thermal Science, 47(10), 1347–1358.

Goh, A.T.C. (1995). “Modeling soil correlations using neural networks”, Journal of Computing in Civil Engineering, ASCE, 9(4), 275-278.

Gribb, M.M. and Gribb, G.W. (1994). “Use of neural networks for hydraulic conductivity determination in unsaturated soil”, Proceedings of the 2nd International Conference on Ground Water Ecology, Bethesda, 155-163.

Griffiths, K.A. and Andrews, R.C. (2011). “Application of artificial neural networks for filtration optimization”, Journal of Environmental Engineering, 137(11), 1040–1047.

Grima, M.A., Bruines, P.A. and Verhoef, P.N.W. (2000). “Modeling tunnel boring machine performance by neuro-fuzzy methods”, Journal of Tunneling and Underground Space Technology, 15(3), 259- 269.

Hassanlourad, M., Salehzadeh, H. and Shahnazari, H. (2010). “Mechanical properties of ungrouted and grouted carbonate sands”, International Journal of Geotechnical Engineering, 4(4), 507-516.

Hassanlourad M., Salehzadeh H., Shahnazari H., (2012). "Shear behavior of chemically grouted carbonate sands", International Journal of Geotechanical Engineering, 6(4), 445-454

Herndon, J. and Lenahan, T. (1976). “Grouting in soils”, Design and Operations Manual, Federal Highway Administration, Halliburton Services, Duncan, Oklahoma, Technical Report, Vol. 2.

Incecik, M. and Ceren, I. (1995). “Cement  grouting model tests”, Bulletin of The technical University of Istanbul, 48(2), 305-317.

Karol, R.H. (1983). Chemical grouting, Marcel Dekker Inc., New York.

Kim, Y. and Kim, B. (2006). “Use of artificial neural networks in the prediction of liquefaction resistance of sands”, Journal of Geotechnical and Geoenvironmental Engineering, 132(11), 1502-1504.

Kutzner, C. (1996). Grouting of rock and soil, Bulkema, Netherlands, 10-195.

Lee, I.M. and Lee. J.H. (1996). “Prediction of pile bearing capacity using artificial neural networks”, Computers and Geotechnics, 18(3), 189–200.

Levenberg, K.A. (1944). “Method for the solution of certain non-linear problems in least squares”, Quarterly of Applied Mathematics, 2(2), 164–168.

Liao, K.W., Fan, J.Ch. and Huang, Ch.L. (2011). “An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts”, Computers and Geotechnics, 38, 978–986.

 Lu, Y. (2005). “Underground blast induced ground shock and its modeling using artificial neural network”, Computers and Geotechnics, 32(3), 164-178.

Maier, H.R. and Dandy, G.C. (2000). “Neural networks for the prediction and forecasting of water resource variables: a review of modeling issues and applications”, Environmental Modeling and Software, 15(1), 101-124.

Marquardt, D.W. (1963). “An Algorithm for the least-squares estimation of nonlinear parameters”, SIAM Journal of Applied Mathematics, 11(2), 431–441.

Mittelmann, H.D. (2004). “The least squares problem”,  http://plato.asu.edu/topics/problems/nlolsq.html.

Najjar, Y.M., Basheer, I.A. and Naouss, W.A. (1996). “On the identification of compaction characteristics by neuronets”, Computers and Geotechnics, 18(3), 167-187.

Nelson, M.M. and Illingworth, W.T. (1991). A practical guide to neural nets, Addison-Wesley Publishing Company, Massachusetts, United States.

Saute, A. and Saglamer, A. (2002). “Estimating the groutability of granular soils: a new approach”, Journal of Tunneling and Underground Space Technology, l17(4), 371-380.

Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2002). “Predicting settlement of shallow foundations using neural networks”, Journal of Geotechnical and Geoenvironmental Engineering, 128(9), 785–793.

Shahin, M.A. and Jaksa, M.B. (2004). “Probabilistic assessment of the uncertainty associated with the pullout capacity of marquee ground anchors”, Proceedings of the 9th Australia New Zealand Conference on Geomechanics, Auckland.

Shahin, M.A. and Jaksa, M.B. (2005a). “Modeling the pullout capacity of marquee ground anchors using neurofuzzy technique”, Proceedings of the International Journal of Modeling and Simulation (MODSIM 2005), Melbourne, Australia, 66-72.

Shahin, M.A. and Jaksa, M.B. (2005b). “Neural network prediction of pullout capacity of marquee ground anchors”, Computers and Geotechnics, 32(3), 153-163.

Shahin, M.A. and Jaksa, M.B. (2006). “Pullout capacity of small ground anchors by direct cone penetration test methods and neural methods”, Canadian Geotechnical Journal, 43(6), 626-637.

Shang, J.Q., Ding, W., Rowe, R.K. and Josic, L. (2004). “Detecting heavy metal contamination in soil using complex permittivity and artificial neural networks”, Canadian Geotechnical Journal, 41(6), 1054-1067.

Sinha, S.K. and Wang, M.C. (2008). “Artificial neural network prediction models for soil compaction and permeability”, Geotechnical Engineering Journal, 26(1), 47-64.

The, C.I., Wong, K.S. and Goh, A.T.C. and Jaritngam. S. (1997). “ Prediction of pile capacity using neural networks”, Journal of Computing in Civil Engineering, 11(2), 129–138.

Willmott, C.J. and Matsuura, K. (2005). “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance”, Climate Research, 30, 79–82.