Universiti Teknologi Malaysia Institutional Repository

Prediction of pullout behavior of belled piles through various machine learning modelling techniques

Dieu, Tien Bui and Moayedi, Hossein and Mohammed Abdullahi, Mu’azu and A. Rashid, Ahmad Safuan and Nguyen, Hoang (2019) Prediction of pullout behavior of belled piles through various machine learning modelling techniques. Sensors (Switzerland), 19 (17). p. 3678. ISSN 1424-8220


Official URL: http://dx.doi.org/10.3390/s19173678


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.

Item Type:Article
Uncontrolled Keywords:Belled piles, Machine learning
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Civil Engineering
ID Code:88463
Deposited By: Widya Wahid
Deposited On:15 Dec 2020 08:06
Last Modified:15 Dec 2020 08:06

Repository Staff Only: item control page