This paper aimed to estimate the slope stability on soft ground by utilising Artificial Intelligence (AI) method that is widely used in the past decade for prediction purposes. The slope stability is predicted using Factor of Safety (FoS) that is generated with Limit Equilibrium Method (LEM) such as Ordinary, Bishop, Janbu and Morgenstern-Price and the total of 233 random datasets in this study. The output of the FoS is also estimated using the input parameters of height of slope (H), unit weight slope aterial (γ), angle of slope (θ), Coefficient of cohesion (c), and internal angle friction (ϕ) that are proceeded with Artificial Neural Networks (ANN) and Adaptive Neural Fuzzy-Logic Inference Systems (ANFIS). The final selected model of the slope stability is tested with the real data to produce a better result of the potential prediction in the accepted range of accuracy.
<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 total of 75 datasets generated from numerical simulations were randomly selected into three groups for training, testing, and validation. The coefficient of determination (</span><em><span lang="EN-US">R<sup>2</sup></span></em><span lang="EN-US">) and root mean square error (RMSE) were considered to evaluate the performance ANN model. It was found that the developed ANN model showed strong potential for predicting slope stability within the accepted range.</span></p>
This article provides a comparative knowledge of predicted and measured settlements of road embankments with prefabricated vertical drains (PVDs). The emphasis of this study was to investigate and back-analyse the ratio of horizontal permeability in an undisturbed area to a smeared zone, which affects the behaviour of vertical drains. Two models of PVDs in soft ground were developed by utilising a plane strain 2D numerical approach based on the equivalent permeability. Suggestions for the improvement of numerical accuracy of the soft ground beneath road embankments have been made in regards to the obtained results. The employment of the equivalent horizontal permeability in numerical analysis produced significantly similar results to those of the measured values. Furthermore, a smear effect permeability ratio of 300 produced a considerably accurate result with a model based on the equivalent horizontal permeability and measured data. Lastly, the smear effect ratio of 6 using the equivalent horizontal permeability approach was employed in order to predict the behaviour of vertical drains in the soft grounds under road embankments.
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