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

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.

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

“…By tuning the model parameters, PSO algorithms are used for hybridization with different machine learning techniques-particularly with ANN models. To optimize an ANN with PSO, a recently outlined procedure was used [62]. PSO starts with random populations for a given fitness function.…”

Self-Collision Avoidance of Arm Robot Using Generative Adversarial Network and Particles Swarm Optimization (Gan-Pso)

“…This represents a mapping data space as ( ; ), where is a differentiable function represented by an ANNs with parameters . This ( ; ) is ANNs calculated consists of Feed-Forward step and Backpropagation step, formulized as [34,35,36…”

“…and is called learning rate. So, the weights updated when the input-hidden error calculated by [34]:…”

GIS-based soil planar slide susceptibility mapping using logistic regression and neural networks: a typical red mudstone area in southwest China

“…Bai et al (2010) used a logistic regression based on GIS data to produce a detailed susceptibility map of one of the most landslide-prone areas in China, the Zhongxing-Shizhu segment in the Three Gorges Reservoir region. In recent years, more and more machine learning methods have been used for landslide susceptibility mapping, such as artificial neural networks ( Møller, 1993;Zeng-Wang 2001;Yesilnacar and Topal 2005;Nawi et al 2006;Pham et al 2017;Le et al 2019aLe et al , 2019bShariati et al 2019;and Lv et al 2020), support vector machine (Guo et al 2005;Yao and Dai 2006;Basak et al 2007;and Dou et al 2015), decision Tree (Chu et al 2009;Saito et al 2009;Nefeslioglu et al 2010;and Tien Bui et al 2012); random forest (Dou et al 2019), neuron fuzzy (Pradhan 2013) and so on. Also, comparison between these models have been made to figure out the most accurate model for landslide susceptibility mapping.…”