An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures
The deformation of a Geosynthetic reinforced soil (GRS) structure is a key factor in designing this type of retaining structures. On the other hand, the feasibility of artificial intelligence techniques in solving geotechnical engineering problems is underlined in literature. This paper is aimed to...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Article |
Published: |
Elsevier
2021
|
Subjects: | |
Online Access: | http://eprints.um.edu.my/26789/ https://doi.org/10.1016/j.trgeo.2020.100446 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.um.eprints.26789 |
---|---|
record_format |
eprints |
spelling |
my.um.eprints.267892022-02-23T03:34:06Z http://eprints.um.edu.my/26789/ An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures Momeni, Ehsan Yarivand, Akbar Dowlatshahi, Mohammad Bagher Armaghani, Danial Jahed TA Engineering (General). Civil engineering (General) The deformation of a Geosynthetic reinforced soil (GRS) structure is a key factor in designing this type of retaining structures. On the other hand, the feasibility of artificial intelligence techniques in solving geotechnical engineering problems is underlined in literature. This paper is aimed to show the workability of two soft computing techniques in predicting the deformation of GRS structures. For this reason, first a relevant case study was modelled into ABAQUS, a finite element (FE) software. Then, the FE results (GRS deformations) were checked against the recorded deformations of the full-scale test. Subsequently, 166 finite element analyses were performed for dataset construction. Then, two predictive models of GRS deformations were constructed. For intelligent model construction, two artificial neural networks (ANN) were coupled with Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO), respectively. It was found that both GSA-based ANN and PSO-based ANN predictive models work good enough. However, the correlation coefficient (R) of 0.981 as well as the system error of 0.0101 for testing data suggest that the GSA-based ANN predictive model outperforms the PSO-based ANN model with R value of 0.973 and system error of 0.0127. Overall, findings recommend that the proposed models can be implemented in assessing the performance of geosynthetic reinforced soil structures. Elsevier 2021-01 Article PeerReviewed Momeni, Ehsan and Yarivand, Akbar and Dowlatshahi, Mohammad Bagher and Armaghani, Danial Jahed (2021) An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures. Transportation Geotechnics, 26. ISSN 2214-3912, DOI https://doi.org/10.1016/j.trgeo.2020.100446 <https://doi.org/10.1016/j.trgeo.2020.100446>. https://doi.org/10.1016/j.trgeo.2020.100446 10.1016/j.trgeo.2020.100446 |
institution |
Universiti Malaya |
building |
UM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaya |
content_source |
UM Research Repository |
url_provider |
http://eprints.um.edu.my/ |
topic |
TA Engineering (General). Civil engineering (General) |
spellingShingle |
TA Engineering (General). Civil engineering (General) Momeni, Ehsan Yarivand, Akbar Dowlatshahi, Mohammad Bagher Armaghani, Danial Jahed An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures |
description |
The deformation of a Geosynthetic reinforced soil (GRS) structure is a key factor in designing this type of retaining structures. On the other hand, the feasibility of artificial intelligence techniques in solving geotechnical engineering problems is underlined in literature. This paper is aimed to show the workability of two soft computing techniques in predicting the deformation of GRS structures. For this reason, first a relevant case study was modelled into ABAQUS, a finite element (FE) software. Then, the FE results (GRS deformations) were checked against the recorded deformations of the full-scale test. Subsequently, 166 finite element analyses were performed for dataset construction. Then, two predictive models of GRS deformations were constructed. For intelligent model construction, two artificial neural networks (ANN) were coupled with Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO), respectively. It was found that both GSA-based ANN and PSO-based ANN predictive models work good enough. However, the correlation coefficient (R) of 0.981 as well as the system error of 0.0101 for testing data suggest that the GSA-based ANN predictive model outperforms the PSO-based ANN model with R value of 0.973 and system error of 0.0127. Overall, findings recommend that the proposed models can be implemented in assessing the performance of geosynthetic reinforced soil structures. |
format |
Article |
author |
Momeni, Ehsan Yarivand, Akbar Dowlatshahi, Mohammad Bagher Armaghani, Danial Jahed |
author_facet |
Momeni, Ehsan Yarivand, Akbar Dowlatshahi, Mohammad Bagher Armaghani, Danial Jahed |
author_sort |
Momeni, Ehsan |
title |
An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures |
title_short |
An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures |
title_full |
An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures |
title_fullStr |
An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures |
title_full_unstemmed |
An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures |
title_sort |
efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures |
publisher |
Elsevier |
publishDate |
2021 |
url |
http://eprints.um.edu.my/26789/ https://doi.org/10.1016/j.trgeo.2020.100446 |
_version_ |
1735409458447646720 |
score |
13.160551 |