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Measured and Predicted Settlement of Shallow Foundations on Cohesionless Soil Read chapter Read first chapter

2019 | OriginalPaper | Chapter

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

Authors : T. Gnananandarao, R. K. Dutta, V. N. Khatri

Published in: Geotechnical Applications

Publisher: Springer Singapore

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Abstract

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.

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previous chapter Analysis of Laterally Loaded Fixed-Headed Single Pile in Multilayered Soil Using P-Y Approach
next chapter Interference of Two Nearby Footings Resting on Clay Medium
Literature
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Metadata
Title
Application of Artificial Neural Network to Predict the Settlement of Shallow Foundations on Cohesionless Soils
Authors
T. Gnananandarao
R. K. Dutta
V. N. Khatri
Copyright Year
2019
Publisher
Springer Singapore
Book
Geotechnical Applications

Print ISBN: 978-981-13-0367-8

Electronic ISBN: 978-981-13-0368-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-0368-5_6