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...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/88463/1/AhmadSafuanARashid2019_PredictionofPulloutBehaviorofBelledPiles.pdf http://eprints.utm.my/id/eprint/88463/ http://dx.doi.org/10.3390/s19173678 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.88463 |
---|---|
record_format |
eprints |
spelling |
my.utm.884632020-12-15T00:06:40Z http://eprints.utm.my/id/eprint/88463/ Prediction of pullout behavior of belled piles through various machine learning modelling techniques Dieu, Tien Bui Moayedi, Hossein Mohammed Abdullahi, Mu’azu A. Rashid, Ahmad Safuan Nguyen, Hoang TA Engineering (General). Civil engineering (General) 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. MDPI AG 2019-09 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/88463/1/AhmadSafuanARashid2019_PredictionofPulloutBehaviorofBelledPiles.pdf 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 http://dx.doi.org/10.3390/s19173678 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
language |
English |
topic |
TA Engineering (General). Civil engineering (General) |
spellingShingle |
TA Engineering (General). Civil engineering (General) Dieu, Tien Bui Moayedi, Hossein Mohammed Abdullahi, Mu’azu A. Rashid, Ahmad Safuan Nguyen, Hoang Prediction of pullout behavior of belled piles through various machine learning modelling techniques |
description |
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. |
format |
Article |
author |
Dieu, Tien Bui Moayedi, Hossein Mohammed Abdullahi, Mu’azu A. Rashid, Ahmad Safuan Nguyen, Hoang |
author_facet |
Dieu, Tien Bui Moayedi, Hossein Mohammed Abdullahi, Mu’azu A. Rashid, Ahmad Safuan Nguyen, Hoang |
author_sort |
Dieu, Tien Bui |
title |
Prediction of pullout behavior of belled piles through various machine learning modelling techniques |
title_short |
Prediction of pullout behavior of belled piles through various machine learning modelling techniques |
title_full |
Prediction of pullout behavior of belled piles through various machine learning modelling techniques |
title_fullStr |
Prediction of pullout behavior of belled piles through various machine learning modelling techniques |
title_full_unstemmed |
Prediction of pullout behavior of belled piles through various machine learning modelling techniques |
title_sort |
prediction of pullout behavior of belled piles through various machine learning modelling techniques |
publisher |
MDPI AG |
publishDate |
2019 |
url |
http://eprints.utm.my/id/eprint/88463/1/AhmadSafuanARashid2019_PredictionofPulloutBehaviorofBelledPiles.pdf http://eprints.utm.my/id/eprint/88463/ http://dx.doi.org/10.3390/s19173678 |
_version_ |
1687393574599000064 |
score |
13.18916 |