Safety-factor dataset for high embankments determined with different analytical methods

Mesa-Lavista, Milena; Álvarez-Pérez, José; Tejeda-Piusseaut, Eduardo; Lamas‐Fernández, Francisco

doi:10.1016/j.dib.2021.107315

Cited by 8 publications

(7 citation statements)

References 1 publication

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“…Besides, the geo-bag method showed the factor of safety with 3.7 which is slightly lower than model with geotextile which had the value of 3.8. It can be seen that all methods meet the minimum requirement for safety factor, where any values that are greater than 1.5 are acceptable safety factors [6]. This section concluded that as the settlement of the embankment had been increased, the factor of safety value has gone down [7].…”

Section: Factor Of Safetymentioning

confidence: 94%

Formulation of a Slope Protection Scheme for Senggarang Coastal Embankment Subjected to Tidal Assault

Othman

Chan²,

Yahya

et al. 2022

JAITA

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The seaward slope of embankment that experience erosion due to washing of soil particles from the wave action might resulting the slope become steeper by time and posing a threat to the closest residents. The objective of this research is to examine the existing slope protection methods for Senggarang Coastal Embankment (SCE) in terms of functionality and performance. This paper explores the possible success and failure factors of the slope protection methods by performing the stability analysis of the slope. Desk research were conducted to identify the potential slope protection scheme for SCE then simulated with PLAXIS 2D to refine the proposed solution. Embankment was simulated using silty clay with different water level at 1 m, 2 m and 3 m to see the total displacement, effective stress, excess pore pressure and discharge of seepage. As a result, the deformation for embankment with innovative method is 10% lower than embankment without any slope protection. Meanwhile, the discharge of seepage for embankment with innovative slope protection has reduced by 40% compared to the embankment in the absence of slope protection. In summary, this study has found the water level effect on the displacement and stability of the embankment. The higher the water level, the higher the displacement of the embankment.

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Section: Factor Of Safetymentioning

confidence: 94%

Formulation of a Slope Protection Scheme for Senggarang Coastal Embankment Subjected to Tidal Assault

Othman

Chan²,

Yahya

et al. 2022

JAITA

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show abstract

“…The data has been obtained from an open-source repository for this study (Mesa-Lavista et al. , 2021).…”

Section: Methodsmentioning

confidence: 99%

“…The data has been obtained from an open-source repository for this study (Mesa-Lavista et al, 2021). This dataset will be utilized for training and testing the machine learning models.…”

Section: Utilized Databasementioning

confidence: 99%

Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

Habib¹,

Bashir

Alsalman

et al. 2023

MMMS

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PurposeSlope stability analysis is essential for ensuring the safe design of road embankments. While various conventional methods, such as the finite element approach, are used to determine the safety factor of road embankments, there is ongoing interest in exploring the potential of machine learning techniques for this purpose.Design/methodology/approachWithin the study context, the outcomes of the ensemble machine learning models will be compared and benchmarked against the conventional techniques used to predict this parameter.FindingsGenerally, the study results have shown that the proposed machine learning models provide rapid and accurate estimates of the safety factor of road embankments and are, therefore, promising alternatives to traditional methods.Originality/valueAlthough machine learning algorithms hold promise for rapidly and accurately estimating the safety factor of road embankments, few studies have systematically compared their performance with traditional methods. To address this gap, this study introduces a novel approach using advanced ensemble machine learning techniques for efficient and precise estimation of the road embankment safety factor. Besides, the study comprehensively assesses the performance of these ensemble techniques, in contrast with established methods such as the finite element approach and empirical models, demonstrating their potential as robust and reliable alternatives in the realm of slope stability assessment.

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“…The data obtained for this research were from an open-source repository [35]. The dataset that will be in this study used for training and testing the ANN models were collected from comprehensive parametric research on various road embankments' safety factors computed using the finite element approach.…”

Section: Utilized Databasementioning

confidence: 99%

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

Alqawasmeh¹

2023

GEOMATE

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The slope-stability analysis is one of the most important parameters for ensuring a safe design of road embankments. Currently, various traditional approaches to computing this variable can be seen in the literature. Among them, the finite element method is considered an accurate way to define the safety factor of road embankments. Previous research has investigated the capability of artificial neural networks for rapid safety-factor estimation to overcome the long process of modeling and calculations required in the aforementioned approach. However, most of these studies have focused on a single type of neural network and did not investigate the capabilities of other approaches. Therefore, this study is intended to evaluate the performance of various artificial neural network techniques in predicting the safety factor of road embankments. Within this context, the feed-forward back-propagation, cascade forward neural networks, and general regression neural network results will be compared and benchmarked against various methods used to predict this parameter. Moreover, it is intended to report the influence of neural network architecture on the accuracy of the estimation. Generally, the study results have shown that an artificial neural network provides a rapid and accurate method for calculating road embankments' safety factors. Besides, the best neural network model achieved a coefficient of determination of about 0.91 and a root mean square error of 0.236, which proves the efficiency of this technique. Moreover, the reliability assessment by comparing the neural network models against the traditional methods has shown that they provide better agreement with the finite element technique.

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Safety-factor dataset for high embankments determined with different analytical methods

Cited by 8 publications

References 1 publication

Formulation of a Slope Protection Scheme for Senggarang Coastal Embankment Subjected to Tidal Assault

Formulation of a Slope Protection Scheme for Senggarang Coastal Embankment Subjected to Tidal Assault

Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

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