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
Source abstract
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.
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Research context
OpenAlex topics: Concrete Corrosion and Durability · Structural Behavior of Reinforced Concrete · Innovative concrete reinforcement materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh 758307, Vietnam
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
School of Civil Engineering, College of Engineering, University of Tehran, Tehran 1417466191, Iran
MSc graduate, Department of Civil Engineering, K.N. Toosi University of Technology, Tehran 15875-4416, Iran
Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz 5157944533, Iran
School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata 940-2188, Japan
Center for Spatial Information Science, the University of Tokyo, 5-1-5, Kashiwa 277-8568, Japan
Construction Research Center (CRC), Institute for Smart Infrastructure & Innovative Construction (ISIIC), School of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
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