Comparison of ANN and ANFIS Models for Stability Prediction of Cantilever Reinforced Concrete Retaining Walls
Rohaya Alias1, Anuar Kasa2, Siti Jahara Matlan3

1Rohaya Alias, Faculty Department of Civil Engineering, University Teknologi MARA Pahang, 26400 Bandar Tun Razak Jengka, Pahang, Malaysia.
2Anuar Kasa, Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia.
3Siti Jahara Matlan, Department of Civil Engineering Program, Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia.

Manuscript received on 10 December 2017 | Revised Manuscript received on 18 December 2017 | Manuscript Published on 30 December 2017 | PP: 165-167 | Volume-7 Issue-2, December 2017 | Retrieval Number: B5274127217/17©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) models are used to predict the external stability of cantilever reinforced concrete (RC) retaining walls. A total of 235 different designs of cantilever RC retaining walls using procedure of BS: 8110 were used. Three input parameters were used namely; height of wall, angle of slope, and surcharge, while the output parameters consist of the external stability namely: factors of safety (FOS) for sliding, overturning and bearing capacity. The output data generated through design is used as a target for both models. Two criteria involving the determination coefficient (R2 ) and root mean square error (RMSE) were used to evaluate the accuracy of prediction models. The results showed that prediction made using ANFIS more accurate compared with ANN.
Keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Retaining wall, Stability.

Scope of the Article: Fuzzy Logics