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

TLDR

Channel shear connectors offer a cost‑effective and easily installed alternative to conventional shear connectors, yet their behavior is typically assessed experimentally, which is expensive, time‑consuming, and limited in revealing parameter effects. This study investigates applying a hybrid ANN–PSO model to predict the behavior of channel connectors embedded in normal and high‑strength concrete. Data were obtained from experiments on channel connectors with varying dimensions and concrete strengths, and an ANN–PSO model was trained to predict load and slip, while a conventional ANN with back‑propagation was also developed for comparison. The ANN model accurately predicted connector behavior, reducing reliance on costly experiments, and the ANN–PSO approach outperformed the ANN–BP model, yielding superior performance indices.

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.