Citation

Habib M, Bashir B, Alsalman A, Bachir H. Multidiscip. Model. Mater. Struct. 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Brill Academic Publishers)

DOI

10.1108/MMMS-12-2022-0290

PMID

unavailable

Abstract

PURPOSE Slope 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/APPROACH Within 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.

FINDINGS Generally, 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/VALUE Although 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.

Language: en

Keywords

Machine learning; Rapid assessment; Road embankment; Slope stability