Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/12718
Title: Developing a hybrid adoptive neuro-fuzzy inference system in predicting safety of factors of slopes subjected to surface eco-protection techniques
Authors: Sari P.
Suhatril M.
Osman N.
Mohammed, Mu'azu
Katebi J.
Abavisani, Ali
Ghaffari N.
Sadeghi Chahnasir E.
Wakil, Karzan
Khorami, Majid
Petković D.
Issue Date: 2020
Abstract: © 2019, Springer-Verlag London Ltd., part of Springer Nature. This study predicts the investigation of surface eco-protection techniques for cohesive soil slopes along the selected Guthrie Corridor Expressway stretch by way of analyzing a new set of probabilistic models using a hybrid technique of artificial neural network and fuzzy inference system namely adaptive neuro-fuzzy inference system (ANFIS). Soil erosion and mass movement which induce landslides have become one of the disasters faced in Selangor, Malaysia causing enormous loss affecting human lives, destruction of property and the environment. Establishing and maintaining slope stability using mechanical structures are costly. Hence, biotechnical slope protection offers an alternative which is not only cost effective but also aesthetically pleasing. A parametric study was carried out to discover the relationship between various eco-protection techniques, i.e., application of grasses, shrubs and trees with different soil properties as well as slope angles. Then the data have been used to develop a new hybrid ANFIS technique for prediction of factor of safety (FOS) of slopes. Four inputs were considered in relation to the different vegetation types, i.e., slope angle (θ), unit weight (γ), effective cohesion (c′), effective friction angle (ø′). Then, many hybrid ANFIS models were constructed, trained and tested using various parametric studies. Eventually, a hybrid ANFIS model with a high performance prediction and a low system error was developed and introduced for solving problem of slope stability.
URI: https://scidar.kg.ac.rs/handle/123456789/12718
Type: article
DOI: 10.1007/s00366-019-00768-3
ISSN: 0177-0667
SCOPUS: 2-s2.0-85065700960
Appears in Collections:Faculty of Engineering, Kragujevac

Page views(s)

117

Downloads(s)

3

Files in This Item:
File Description SizeFormat 
PaperMissing.pdf
  Restricted Access
29.86 kBAdobe PDFThumbnail
View/Open


Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.