The applications and future perspectives of adaptive neuro-fuzzy inference system in road embankment stability

Rufaizal Che Mamat, Anuar Kasa, Siti Fatin Mohd Razali

Research output: Contribution to journalReview article

Abstract

The stability of road embankment is influenced by two main factors, namely slope stability and settlement. The use of an adaptive neuro-fuzzy inference system (ANFIS) has received encouraging responses over the last decade in various research areas. This paper aims to elaborate on the previous study on the application of ANFIS to predict factors that affect the stability of road embankment. Additionally, study reports on optimization techniques using ANFIS approach such as genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA) and satin bowerbird optimization algorithm (SBO) is also discussed. It is observed that most researchers developed ANFIS models to predict soil properties. We thus present proposals for future research. Overall, this study highlights the need for ANFIS to predict the stability of road embankment. Interestingly, we find that researchers successfully use the ANFIS model with the ability to predict with acceptable accuracy. Nevertheless, our findings also revealed that no researchers had done the use of ANFIS to predict slope stability and settlement.

Original languageEnglish
Pages (from-to)75-90
Number of pages16
JournalJournal of Engineering Science and Technology Review
Volume12
Issue number5
DOIs
Publication statusPublished - 1 Jan 2019

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Embankments
Fuzzy inference
Slope stability
Particle swarm optimization (PSO)
Genetic algorithms
Soils

Keywords

  • ANFIS
  • Optimization techniques
  • Road embankment
  • Settlement
  • Slope stability

ASJC Scopus subject areas

  • Engineering(all)

Cite this

The applications and future perspectives of adaptive neuro-fuzzy inference system in road embankment stability. / Mamat, Rufaizal Che; Kasa, Anuar; Razali, Siti Fatin Mohd.

In: Journal of Engineering Science and Technology Review, Vol. 12, No. 5, 01.01.2019, p. 75-90.

Research output: Contribution to journalReview article

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