2020
DOI: 10.11591/ijai.v9.i2.pp236-243
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Slope stability prediction of road embankment on soft ground treated with prefabricated vertical drains using artificial neural network

Abstract: <p><span lang="EN-US">This paper presents the slope stability for road embankment constructed on the soft ground treated with prefabricated vertical drains (PVDs). The slope stability was evaluated based on the factor of safety (FOS) through numerical analysis and modeled with an artificial neural network (ANN). The permeability ratio of the smear effect was verified based on a comparative analysis between field data and numerical simulation to develop the datasets used in ANN model training. A tot… Show more

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Cited by 8 publications
(5 citation statements)
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“…From the picture, the output of DO ANN can follow the pattern of recorded data well, but there are some data that deviate quite far, where the respective errors are 1.40, 1.42, and 2.74. This happened because the data values on that date were very different from most of the data ranges, so that ANN was not able to properly follow the desired target [34]. Overall, the data record value can be predicted by ANN well, which is indicated by the number of small error values approaching zero.…”
Section: The Design and Training Of Artificial Neural Network Model F...mentioning
confidence: 98%
“…From the picture, the output of DO ANN can follow the pattern of recorded data well, but there are some data that deviate quite far, where the respective errors are 1.40, 1.42, and 2.74. This happened because the data values on that date were very different from most of the data ranges, so that ANN was not able to properly follow the desired target [34]. Overall, the data record value can be predicted by ANN well, which is indicated by the number of small error values approaching zero.…”
Section: The Design and Training Of Artificial Neural Network Model F...mentioning
confidence: 98%
“…An artificial neural network (ANN) was employed on numerical analysis data, showcasing the strong potential of ANN for predicting slope stability. The model's performance was evaluated using R 2 and RMSE metrics [30]. An extreme learning neural network was applied to finite element upper and lower bound…”
Section: Comparative Analysis Of Slope Stability Evaluation Studiesmentioning
confidence: 99%
“…Where, Y is FEM and X is LEM. This coefficient of determination (R 2 ) has been used to evaluate the performance of models in several slope studies [29]- [31] based on the criterion that the R 2 value closer to 1 indicates better reliability.…”
Section: Safety Factor Based On Lem and Fem For All Variationsmentioning
confidence: 99%