Use of Radial Basis Function Neural Network for Bearing Pressure Prediction of Strip Footing on Reinforced Granular Bed Overlying Weak Soil

Authors: Srinath Shetty K., Shivashankar R., Rashmi P. Shetty

Abstract:

Earth reinforcing techniques have become useful and economical to solve problems related to difficult grounds and provide satisfactory foundation performance. In this context, this paper uses radial basis function neural network (RBFNN) for predicting the bearing pressure of strip footing on reinforced granular bed overlying weak soil. The inputs for the neural network models included plate width, thickness of granular bed and number of layers of reinforcements, settlement ratio, water content, dry density, cohesion and angle of friction. The results indicated that RBFNN model exhibited more than 84 % prediction accuracy, thereby demonstrating its application in a geotechnical problem.

Keywords: Bearing pressure, granular bed, radial basis function neural network, strip footing.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057145

References:

[1] J. A. Charles, "Ground improvement: the interaction of engineering science and experience- based technology". Geotechnique, Vol. 52, No. 7, (2002). pp. 527-532.
[2] J. M. Mitchel and F. Jardine, A guide to ground improvement,(2000). Report RP 373.London: Construction Industry Research and Information Association.
[3] B. S. Naganna, "Ground improvement in shedi soil with geogrid inclusions-A laboratory study". M.Tech Thesis, (1993). Karnataka Regional Engineering College, Surathkal.
[4] K. Terzaghi and R. B. Peck, Soil Mechanics in engineering practices, (1948). 1st Edition, John Wiley and Sons, Inc. New York, N.Y.
[5] J. Binquet and K. L. Lee, "Bearing capacity tests on reinforced earth slabs". ASCE Journal of Geotechnical Engineering,101(GT12), (1975a). pp. 1241-1255.
[6] J. Binquet and K. L. Lee, "Bearing capacity analysis of reinforced earth slabs". ASCE Journal of Geotechnical Engineering, 101(GT12), (1975b). pp. 1257-1276.
[7] R. Shivashankar, M. R. Madhav and N. Mura, "Reinforced granular beds overlying soft clay". Proceedings of the Eleventh South Asian Geotechnical Conference, Singapore. (1993). pp. 409-414.
[8] M. A. Shahin, H. R. Maier, and M. B. Jaksa, "Predicting settlement of shallow foundations using neural networks". Journal of Geotechnical and Geoenvironmental Engineering, Vol.128, No.9, (2002). pp. 785-793.
[9] M. A. Shahin, H. R. Maier, and M. B. Jaksa, "Data division for developing neural networks applied to Geotechnical engineering". ASCE Journal of Computing in Civil Engineering, Vol.18, No.2, (2004). pp. 105-114.
[10] I. Flood, "Next generation artificial networks for Civil engineering". ASCE Journal of Computing in Civil Engineering, Vol. 20, No. 5, (2006), pp. 305-307.
[11] M. A. Shahin, H. R. Maier, and M.B. Jaksa, "State of the Art of Artificial Neural Networks in Geotechnical Engineering". Electronic Journal of Geotechnical Engineering, Vol. 8, (2008). pp.1-26.
[12] H. Adeli, "Neural networks in Civil engineering: 1989-2000". Computer Aided Civil and Infrastructure Engineering, Vol. 16, No. 2, (2001). pp. 126-142.
[13] O. Yilmaz, I. Kaynar, “Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils”. Expert Systems with Applications, Vol. 38, (2011). pp. 5958-5966.
[14] A. W. Jayawardena, and D. A. K. Fernando, “Use of radial basis function type artificial neural networks for runoff simulation”. Computer-Aided Civil and Infrastructure Engineering, Vol. 13, (1998). pp. 91-99.
[15] R. Jain, P. K. Jain, and S. S. Bhadauria, “Computational approach to predict soil shear strength”. International Journal of Engineering Science and Technology, Vol. 2, No. 8, (2010). pp. 3874-3885.
[16] Simon Haykin, “Neural Network – A Comprehensive Foundation”, (1999), Pearson Education Publishing.
[17] Shiva Kumar, “Experimental investigation and performance & emission modeling in a biodiesel engine using artificial neural networks”. Ph.D. Thesis, (2011), N.M.A.M.I.T. Nitte, VTU, Belgaum.
[18] J. Moody and C. Darken, "Fast Learning in Networks of Locally-Tuned Processing Units," Neural Computation, 1(2), 1989, pp. 281-294.
[19] A.K. Bera, A. Ghosh and A. Ghosh, “Regression model for bearing capacity of square footing on reinforced pond ash”. Geotextiles and Geomembranes, Vol. 23, (2005). pp. 261-285.