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

Dieu Tien Bui; Hossein Moayedi; Mu'azu Mohammed Abdullahi; Ahmad Safuan A Rashid; Hoang Nguyen

doi:10.3390/s19173678

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

Sensors (Basel). 2019 Aug 24;19(17):3678. doi: 10.3390/s19173678.

Authors

Dieu Tien Bui^{1

2}, Hossein Moayedi^{3

4}, Mu'azu Mohammed Abdullahi⁵, Ahmad Safuan A Rashid⁶, Hoang Nguyen^{7

8}

Affiliations

¹ Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
² Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway.
³ Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. hossein.moayedi@tdtu.edu.vn.
⁴ Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam. hossein.moayedi@tdtu.edu.vn.
⁵ Civil Engineering Department, College of Engineering, University of Hafr Al-Batin, Al-Jamiah 39524, Eastern Province, Kingdom of Saudi Arabia.
⁶ Center of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81300, Malaysia.
⁷ Department of Surface Mining, Hanoi University of Mining land Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam.
⁸ Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam.

Abstract

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 (R²), 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.

Keywords: belled piles; machine learning; pullout behavior.