Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques

The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-08, Vol.19 (17), p.3678
Main Authors: Tien Bui, Dieu, Moayedi, Hossein, Abdullahi, Mu'azu Mohammed, Safuan A Rashid, Ahmad, Nguyen, Hoang
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial intelligence
Back propagation
belled piles
Civil engineering
Data collection
Datasets
Friction
Fuzzy logic
Fuzzy systems
Gene expression
General regression neural networks
Genetic algorithms
Laboratories
Load
Machine learning
Modelling
Neural networks
Permeability
Plasma etching
pullout behavior
R&D
Radial basis function
Reliability analysis
Research & development
Root-mean-square errors
Statistical analysis
Statistical methods
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Summary:The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset. All results have been analyzed by ANFIS, in which the reliability has been confirmed through the comparing of the results. Consequently, regarding FFNN, RBNN, GRNN, and ANFIS, statistical indexes of coefficient of determination ( ), variance account for (VAF) and root mean square error (RMSE) in the values of (0.957, 0.968, 0.939, 0.902, 0.998), (95.677, 96.814, 93.884, 90.131, 97.442) and (2.176, 1.608, 3.001, 4.39, 0.058) have been achieved for training datasets and the values of (0.951, 0.913, 0.729, 0.685 and 0.995), (95.04, 91.13, 72.745, 66.228, 96.247) and (2.433, 4.032, 8.005, 10.188 and 1.252) are for testing datasets indicating a satisfied reliability of ANFIS in estimating of pullout behavior of belled piles.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19173678