A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier

Afshin Jahangirzadeh, Shahaboddin Shamshirband, Saeed Aghabozorgi, Shatirah Akib, Hossein Basser, Nor Badrul Anuar, Miss Laiha Mat Kiah

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)

Abstract

In this study, a new procedure to determine the optimum dimensions for a rectangular collar to minimize the temporal trend of scouring around a pier model is proposed. Unlike previous methods of predicting collar dimensions around a bridge pier, the proposed approach concerns the selection of different collar dimension sizes around a bridge scour in terms of the flume's upstream (Luc/D), downstream (Ldc/D) and width (Lw/D) of the flume. The projected determination method involves utilizing Expert Multi Agent System (E-MAS) based Support Vector Regression (SVR) agents with respect to cooperative-based expert SVR (Co-ESVR). The SVR agents (i.e. SVRLuc, SVRLdc and SVRLw) are set around a rectangular collar to predict the collar dimensions around a bridge pier. In the first layer, the Expert System (ES) is adopted to gather suitable data and send it to the next layer. The multi agent-based SVR adjusts its parameters to find the optimal cost prediction function in the collar dimensions around the bridge pier to reduce the collar around the bridge scour. The weighted sharing strategy was utilized to select the cost optimization function through the root mean square error (RMSE). The efficiency of the proposed optimization method (Co-ESVR) was explored by comparing its outcomes with experimental results. Numerical results indicate that the Co-ESVR achieves better accuracy in reducing the percentage of scour depth (re) with a smaller network size, compared to the non-cooperative approaches.

Original languageEnglish
Pages (from-to)172-184
Number of pages13
JournalNeurocomputing
Volume140
DOIs
Publication statusPublished - 22 Sep 2014

Fingerprint

Bridge piers
Scour
Costs and Cost Analysis
Expert Systems
Piers
Multi agent systems
Mean square error
Expert systems
Costs

Cite this

Jahangirzadeh, A., Shamshirband, S., Aghabozorgi, S., Akib, S., Basser, H., Anuar, N. B., & Kiah, M. L. M. (2014). A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier. Neurocomputing, 140, 172-184. https://doi.org/10.1016/j.neucom.2014.03.024
Jahangirzadeh, Afshin ; Shamshirband, Shahaboddin ; Aghabozorgi, Saeed ; Akib, Shatirah ; Basser, Hossein ; Anuar, Nor Badrul ; Kiah, Miss Laiha Mat. / A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier. In: Neurocomputing. 2014 ; Vol. 140. pp. 172-184.
@article{cfbf79bf18484bd2b89ef9e449d3c5fd,
title = "A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier",
abstract = "In this study, a new procedure to determine the optimum dimensions for a rectangular collar to minimize the temporal trend of scouring around a pier model is proposed. Unlike previous methods of predicting collar dimensions around a bridge pier, the proposed approach concerns the selection of different collar dimension sizes around a bridge scour in terms of the flume's upstream (Luc/D), downstream (Ldc/D) and width (Lw/D) of the flume. The projected determination method involves utilizing Expert Multi Agent System (E-MAS) based Support Vector Regression (SVR) agents with respect to cooperative-based expert SVR (Co-ESVR). The SVR agents (i.e. SVRLuc, SVRLdc and SVRLw) are set around a rectangular collar to predict the collar dimensions around a bridge pier. In the first layer, the Expert System (ES) is adopted to gather suitable data and send it to the next layer. The multi agent-based SVR adjusts its parameters to find the optimal cost prediction function in the collar dimensions around the bridge pier to reduce the collar around the bridge scour. The weighted sharing strategy was utilized to select the cost optimization function through the root mean square error (RMSE). The efficiency of the proposed optimization method (Co-ESVR) was explored by comparing its outcomes with experimental results. Numerical results indicate that the Co-ESVR achieves better accuracy in reducing the percentage of scour depth (re) with a smaller network size, compared to the non-cooperative approaches.",
author = "Afshin Jahangirzadeh and Shahaboddin Shamshirband and Saeed Aghabozorgi and Shatirah Akib and Hossein Basser and Anuar, {Nor Badrul} and Kiah, {Miss Laiha Mat}",
year = "2014",
month = "9",
day = "22",
doi = "10.1016/j.neucom.2014.03.024",
language = "English",
volume = "140",
pages = "172--184",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",

}

Jahangirzadeh, A, Shamshirband, S, Aghabozorgi, S, Akib, S, Basser, H, Anuar, NB & Kiah, MLM 2014, 'A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier', Neurocomputing, vol. 140, pp. 172-184. https://doi.org/10.1016/j.neucom.2014.03.024

A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier. / Jahangirzadeh, Afshin; Shamshirband, Shahaboddin; Aghabozorgi, Saeed; Akib, Shatirah; Basser, Hossein; Anuar, Nor Badrul; Kiah, Miss Laiha Mat.

In: Neurocomputing, Vol. 140, 22.09.2014, p. 172-184.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier

AU - Jahangirzadeh, Afshin

AU - Shamshirband, Shahaboddin

AU - Aghabozorgi, Saeed

AU - Akib, Shatirah

AU - Basser, Hossein

AU - Anuar, Nor Badrul

AU - Kiah, Miss Laiha Mat

PY - 2014/9/22

Y1 - 2014/9/22

N2 - In this study, a new procedure to determine the optimum dimensions for a rectangular collar to minimize the temporal trend of scouring around a pier model is proposed. Unlike previous methods of predicting collar dimensions around a bridge pier, the proposed approach concerns the selection of different collar dimension sizes around a bridge scour in terms of the flume's upstream (Luc/D), downstream (Ldc/D) and width (Lw/D) of the flume. The projected determination method involves utilizing Expert Multi Agent System (E-MAS) based Support Vector Regression (SVR) agents with respect to cooperative-based expert SVR (Co-ESVR). The SVR agents (i.e. SVRLuc, SVRLdc and SVRLw) are set around a rectangular collar to predict the collar dimensions around a bridge pier. In the first layer, the Expert System (ES) is adopted to gather suitable data and send it to the next layer. The multi agent-based SVR adjusts its parameters to find the optimal cost prediction function in the collar dimensions around the bridge pier to reduce the collar around the bridge scour. The weighted sharing strategy was utilized to select the cost optimization function through the root mean square error (RMSE). The efficiency of the proposed optimization method (Co-ESVR) was explored by comparing its outcomes with experimental results. Numerical results indicate that the Co-ESVR achieves better accuracy in reducing the percentage of scour depth (re) with a smaller network size, compared to the non-cooperative approaches.

AB - In this study, a new procedure to determine the optimum dimensions for a rectangular collar to minimize the temporal trend of scouring around a pier model is proposed. Unlike previous methods of predicting collar dimensions around a bridge pier, the proposed approach concerns the selection of different collar dimension sizes around a bridge scour in terms of the flume's upstream (Luc/D), downstream (Ldc/D) and width (Lw/D) of the flume. The projected determination method involves utilizing Expert Multi Agent System (E-MAS) based Support Vector Regression (SVR) agents with respect to cooperative-based expert SVR (Co-ESVR). The SVR agents (i.e. SVRLuc, SVRLdc and SVRLw) are set around a rectangular collar to predict the collar dimensions around a bridge pier. In the first layer, the Expert System (ES) is adopted to gather suitable data and send it to the next layer. The multi agent-based SVR adjusts its parameters to find the optimal cost prediction function in the collar dimensions around the bridge pier to reduce the collar around the bridge scour. The weighted sharing strategy was utilized to select the cost optimization function through the root mean square error (RMSE). The efficiency of the proposed optimization method (Co-ESVR) was explored by comparing its outcomes with experimental results. Numerical results indicate that the Co-ESVR achieves better accuracy in reducing the percentage of scour depth (re) with a smaller network size, compared to the non-cooperative approaches.

UR - http://www.scopus.com/inward/record.url?scp=84901360343&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2014.03.024

DO - 10.1016/j.neucom.2014.03.024

M3 - Article

VL - 140

SP - 172

EP - 184

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -