Application of Artificial Neural Network to Predict the Settlement of Shallow Foundations on Cohesionless Soils

  • T. Gnananandarao
  • R. K. Dutta
  • V. N. Khatri
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 13)


The present study tries to predict the settlement of shallow foundation on granular soil using a mathematical model. The application of feed-forward neural networks with back propagated algorithm is followed for the same. For the development of ANN model, 193 in situ tests data were collected from the literature. The inputs required for the development of model were the foundation pressure, width of footing and the standard penetration number. The predicted settlement using this model was found to compare favourably with the measured settlement. Further the results of sensitivity analysis indicated that the width of foundation has highest impact on the predicted settlement in comparison to other input variables. The present study confirms the ability of ANN models to predict a complex relationship between the nonlinear data as in present case.


Shallow foundations Settlement Artificial neural networks Sandy soil 


  1. Boger, Z., & Guterman, H. (1997). Knowledge extraction from artificial neural network models. IEEE International Conference on Computational Cybernetics and Simulation, 4, 3030–3035.Google Scholar
  2. Bowles, J. E. (1987). Elastic foundation settlements on sand deposits. Journal of Geotechnical Engineering, 113(8), 846–860.CrossRefGoogle Scholar
  3. Burland, J. B., & Burbidge, M. C. (1985). Settlement of foundations on sand and gravel. Proceedings Institution of Civil Engineers Part I, 78(6), 1325–1381. CrossRefGoogle Scholar
  4. Dutta, R. K., et al. (2015). Prediction of deviator stress of sand reinforced with waste plastic strips using neural network. International Journal of Geosynthetics and Ground Engineering, 1(11), 1–12.Google Scholar
  5. Maugeri, M., et al. (1998). Observed and computed settlements of two shallow foundations on sand. Journal of Geotechnical and Geoenvironmental Engineering, 124(7), 595–605.CrossRefGoogle Scholar
  6. Papadopoulos, B. P. (1992). Settlements of shallow foundations on cohesionless soils. Journal of Geotechnical Engineering, 118(3), 337–393.CrossRefGoogle Scholar
  7. Schmertmann, J. H. (1970). Static cone to compute static settlement over sand. Journal of Soil Mechanics & Foundations Div, American Society of Civil Engineers, 96(SM3), 1011–1043.Google Scholar
  8. Shahin, M. A., Maier, H. R. & Jaksa, M. B. (2002). Predicting settlement of shallow foundations using neural networks. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 128(9), 785–793. CrossRefGoogle Scholar
  9. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of Royal Statistical Society, B, 36, 111–147.MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Institute of Technology, HamirpurHamirpurIndia
  2. 2.Indian Institute of Technology, DhanbadDhanbadIndia

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