Abstract
Laboratory model tests were conducted for the ultimate bearing capacity of shallow rough circular surface foundation resting over a sand layer of limited thickness subjected to an eccentrically inclined load. Based on the laboratory model test results, a neural network model is developed to estimate the reduction factor (RF). The reduction factor can be used to estimate the ultimate eccentrically inclined load per unit area of the foundation supported by a sand layer of limited thickness from the ultimate bearing capacity of a foundation on a sand layer extending to a great depth under an eccentrically inclined load. A thorough sensitivity analysis was carried out to determine the important parameters affecting the reduction factor. Importance was given on the construction of neural interpretation diagram. Based on this neural interpretation diagram, the direct or inverse relationships that exists between the input and output parameters were determined. Results from the artificial neural network (ANN) were compared with the laboratory model test results and these results are well matched.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Behera, R.N., et al.: Prediction of ultimate bearing capacity of eccentrically inclined loaded strip footing by ANN part 1. Int. J. Geotech. Eng. (2013). https://doi.org/10.1179/1938636212z.00000000012
Garson, G.D.: Interpreting neural-network connection weights. Artif. Intell. Expert 6(7), 47–51 (1991)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Goh, A.T.C., et al.: Bayesian neural network analysis of undrained side resistance of drilled shafts. J. Geotech. Geoenviron. Eng. (2005). https://doi.org/10.1061/(asce)10900241(2005)131:1(84)
Hornik, K., et al.: Multilayer feed forward networks are universal approximators. Neural Netw. 2, 359–366 (1989)
Olden, J.D., et al.: An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model. (2004). https://doi.org/10.1016/j.ecolmodel.2004.03.0.013
Ozesmi, S.L., Ozesmi, U. An artificial neural network approach to spatial modeling with inter specific interactions. Ecol. Model. (1999). https://doi.org/10.1016/S0304-3800(98)00149-5
Sahu, R., et al.: Use of ANN and Neuro fuzzy model to predict bearing capacity factor of strip footing resting on reinforced sand and subjected to inclined loading. Int. J. Geosynthetics Ground Eng. 3(3), 29 (2017)
Sahu, R., et al.: Bearing capacity prediction of inclined loaded strip footing on reinforced sand by ANN. In: International Congress and Exhibition “Sustainable Civil Infrastructures: Innovative Infrastructure Geotechnology”, pp. 97–109. Springer, Cham (2018)
Sethy, B.P., et al.: Application of ANN and ANFIS for predicting the ultimate bearing capacity of eccentrically loaded rectangular foundations. Int. J. Geosynthetics Ground Eng. 3(4), 35 (2017)
Shahin, M.A., et al.: Predicting settlement of shallow foundations using neural network. J. Geotech. Geoenviron. Eng. (2002). https://doi.org/10.1061/(asce)1090-0241(2002)128:9(785)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sethy, B.P., Patra, C.R., Sobhan, K., Das, B.M. (2019). Ultimate Bearing Capacity of Eccentrically Inclined Loaded Circular Foundation on Sand Layer of Limited Thickness Using ANN. In: Shehata, H., Das, B. (eds) Advanced Research on Shallow Foundations. GeoMEast 2018. Sustainable Civil Infrastructures. Springer, Cham. https://doi.org/10.1007/978-3-030-01923-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-01923-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01922-8
Online ISBN: 978-3-030-01923-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)