Slope stability prediction of road embankment on soft ground treated with prefabricated vertical drains using artificial neural network

Mamat, Rufaizal Che; Samad, Abd Manan; Kasa, Anuar; Razali, Siti Fatin Mohd; Ramli, Azuin; Omar, Mohd Badrul Hafiz Che

doi:10.11591/ijai.v9.i2.pp236-243

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%

Dissolved oxygen control system in polishing unit using logic solver

et al. 2023

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The research consists of two parts, the first one is to design the dynamic plant model of polishing unit using artificial neural network (ANN) type backpropagation, and the second one is to design a simulation of a close loop control system on Simulink consisting of logic solver, control valve and ANN polishing unit. The ANN polishing unit was trained and obtained the best model structure 4-24-3 with four inputs chemical oxygen demand (COD) influent, oil in water (OIW) influent, urea, and triple superphosphate (TSP), twenty-four hidden layer nodes, and three outputs (OIW effluent, COD effluent and dissolved oxygen (DO)). The mean square error (MSE) and root mean square error (RMSE) from ANN trained were 0.00485 and 0.06964, obtained by the second iteration. From the simulation results on Simulink by giving several scenarios in the logic solver condition table, the action is brought in the form of urea and TSP nutrition issued by the control valve. The values are used to achieve the DO setpoint (2 mg/L), among others: when COD and OIW influent exceed the quality standard, COD exceeds the quality standard, and OIW does not exceed the quality standard, and the DO error is below zero.

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Section: The Design and Training Of Artificial Neural Network Model F...mentioning

confidence: 98%

Dissolved oxygen control system in polishing unit using logic solver

et al. 2023

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“…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%

A Novel Multi-Objective Particle Swarm Optimized RF-SVR Model for Reinforced Soil Slope Stability Analysis

2024

IJIES

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In geotechnical engineering, the stability analysis of reinforced soil slopes is important to ensure the safety and longevity of infrastructures. In this study, a novel multi-objective particle swarm optimized RF-SVR (random forest and support vector regression) model aimed to evaluate the stability of reinforced soil slopes. The proposed Hybrid RF-SVR-MOPSOA model combines the advantages of both machine learning techniques and optimization algorithms and offers enhanced predictive accuracy and efficiency. Assessing the model's effectiveness involves proposed model was compared with three traditional regression models: Elastic net regression (ENR), ridge regression (RR), and lasso regression (LR). Various performance assessment parameters and ROC curve plots were employed to determine the most suitable model for reinforced soil slope stability analysis. The findings indicate that the proposed hybrid RF-SVR-MOPSOA model demonstrates superior performance compared to other traditional regression models. This innovative approach significantly expands the possibilities for enhancing the assessment of slope stability and ensuring safer and more resilient infrastructural development.

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“…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%

Safety Factors Investigation Based on FEM and LEM Approach in Toll Road Embankment Slope

Fitri¹,

Fitria²

2022

cea

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The Pejagan-Pemalang toll road which is a Section II project was designed at different embankment heights on soft soil subgrade. The process involved investigating the slope stability of these embankments through the determination of the safety factor. This can be achieved using different methods such as the limit equilibrium method (LEM) and the finite element method (FEM) which are considered the standard approaches. However, the presentation of different numerous results for the safety factors (SF) in landslide calculations usually makes it difficult for the engineer to understand some uncertain conditions. Therefore, this study aims to determine the safety factors using LEM and FEM approaches at different conditions. The soil subgrade used was in SPT and three models of the properties were obtained. Moreover, a Geo-Studio program with Slope-W analysis was applied using the Morgenstern-Price for the LEM and PLAXIS for the FEM. The height was varied at 2 to 8m with the slope H: V at 1:1, 1:1.5, and 1:2. The simulation was conducted at three different levels of groundwater. The results showed that the height, embankment geometry, and groundwater levels were affected in the SF analysis using the two methods. It was also discovered in all categories that higher embankments had smaller SF while the higher angle of slope produced a higher SF. Furthermore, the properties of the soft soil were observed to have influenced the SF result as indicated by the narrow difference in the correlation between the embankment height and SF. The results of the LEM and FEM were also compared and the observations were explained. The findings of this study are expected to serve as a guide for engineers, especially those in road toll projects, to determine the optimum model needed to predict slope failure in embankments for toll roads.

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Slope stability prediction of road embankment on soft ground treated with prefabricated vertical drains using artificial neural network

Cited by 8 publications

References 0 publications

Dissolved oxygen control system in polishing unit using logic solver

Dissolved oxygen control system in polishing unit using logic solver

A Novel Multi-Objective Particle Swarm Optimized RF-SVR Model for Reinforced Soil Slope Stability Analysis

Safety Factors Investigation Based on FEM and LEM Approach in Toll Road Embankment Slope

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