Use of Radial Basis Function Neural Network for Bearing Pressure Prediction of Strip Footing on Reinforced Granular Bed Overlying Weak Soil
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.
[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.
[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.
@article{"International Journal of Architectural, Civil and Construction Sciences:51534", author = "Srinath Shetty K. and Shivashankar R. and Rashmi P. Shetty", title = "Use of Radial Basis Function Neural Network for Bearing Pressure Prediction of Strip Footing on Reinforced Granular Bed Overlying Weak Soil", 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.", volume = "6", number = "11", pages = "920-4", }
