Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete

Mahdi Shariati; Mohammad Saeed Mafipour; Peyman Mehrabi; Alireza Bahadori; Yousef Zandi; Musab N A Salih; Hoang Nguyen; Jie Dou; Xuan Song; Shek Poi-Ngian

doi:10.3390/app9245534

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Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete

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Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete

Mahdi Shariati, Mohammad Saeed Mafipour, Peyman Mehrabi, Alireza Bahadori, Yousef Zandi, Musab N A Salih, Hoang Nguyen, Jie Dou, Xuan Song and Shek Poi-Ngian

Applied sciences, Vol.9(24), p.5534

12/16/2019

DOI: 10.3390/app9245534

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Abstract

Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly but also time-consuming. Moreover, the impact of other parameters cannot be easily seen in the behavior of the connectors. This paper aims to investigate the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC). To generate the required data, an experimental project was conducted. Dimensions of the channel connectors and the compressive strength of concrete were adopted as the inputs of the model, and load and slip were predicted as the outputs. To evaluate the ANN-PSO model, an ANN model was also developed and tuned by a backpropagation (BP) learning algorithm. The results of the paper revealed that an ANN model could properly predict the behavior of channel connectors and eliminate the need for conducting costly experiments to some extent. In addition, in this case, the ANN-PSO model showed better performance than the ANN-BP model by resulting in superior performance indices.

Details

Title: Subtitle: Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete
Creators: Mahdi Shariati
Mohammad Saeed Mafipour
Peyman Mehrabi
Alireza Bahadori
Yousef Zandi
Musab N A Salih
Hoang Nguyen
Jie Dou
Xuan Song
Shek Poi-Ngian
Resource Type: Journal article
Publication Details: Applied sciences, Vol.9(24), p.5534
DOI: 10.3390/app9245534
ISSN: 2076-3417
eISSN: 2076-3417
Language: English
Date published: 12/16/2019
Academic Unit: Industrial and Systems Engineering; Injury Prevention Research Center
Record Identifier: 9984187046702771

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