Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107187
Title: Estimation of soil pore-water pressure variations using a thin plate spline basis function
Authors: Mustafa, M. R.
Rezaur, R. B.
Isa, M. H.
Rahardjo, Harianto
Keywords: DRNTU::Engineering::Environmental engineering::Environmental protection
Issue Date: 2014
Source: Mustafa, M. R., Rezaur, R. B., Isa, M. H., & Rahardjo, H. (2014). Estimation of soil pore-water pressure variations using a thin plate spline basis function. WIT transactions on the built environment, 137, 615-624.
Abstract: Information of soil pore-water pressure changes due to climatic effect is an integral part for studies associated with hill slope analysis. Soil pore-water pressure variations in a soil slope due to rainfall were predicted using Artificial Neural Network (ANN) technique with Thin Plate Spline (TPS) radial basis function. A radial basis function (RBF) neural network with network architecture of 8-36-1 (input-hidden-output) was selected to develop RBF model. Number of hidden neurons was selected using trial and error procedure whereas spread of the basis function was established using normalization method. Time series data of rainfall and pore-water pressure was used for training and testing the RBF model. The performance of the model was evaluated using root mean square error, coefficient of correlation and coefficient of efficiency. The results of the model prediction revealed that the model produced promising results indicating that TPS basis function is able to predict time series of pore-water pressure responses to rainfall. Comparison with other studies showed that the RBF model using TPS basis function can be used as alternate of Gaussian basis function for prediction of soil pore-water pressure variations.
URI: https://hdl.handle.net/10356/107187
http://hdl.handle.net/10220/25397
DOI: 10.2495/HPSM140561
Rights: © 2014 Wessex Institute of Technology Press. This paper was published in WIT Transactions on the Built Environment and is made available as an electronic reprint (preprint) with permission of Wessex Institute of Technology Press. The paper can be found at the following official DOI: [http://dx.doi.org/10.2495/HPSM140561].  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Conference Papers

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