Large-scale fatigue testing of post-installed shear connectors in partially-composite bridge girders

Azad, Amir Reza Ghiami; Kreitman, Kerry; Engelhardt, Michael D.; Helwig, Todd A.; Williamson, Eric B.

doi:10.1016/j.jcsr.2019.06.007

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…An experiment was conducted on partially composite bridge girders with adhesive anchors. Compared to the previously reported fatigue test results based on direct‐shear tests, these shear connectors performed better in beam tests 19 …”

Section: Introductioncontrasting

confidence: 64%

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Roshanfar,

Ghiami Azad,

Forouzanfar

2023

Fatigue Fract Eng Mat Struct

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Fatigue limit states often govern the design of shear connectors in steel‐concrete composite bridges. AASHTO LRFD bridge design specifications provides a linear equation in a semi‐logarithmic S‐N curve for predicting the fatigue life of shear connectors. However, this equation can be too conservative in some cases, as supported by the available experimental data. In this paper, artificial intelligence (AI) was incorporated into the prediction of the fatigue life of shear connectors. Six different machine learning (ML) algorithms were considered for this purpose. The predictions of ML algorithms were compared both with the available experimental data and the equation provided by AASHTO. The results showed that the fatigue life predicted by ML methods is more accurate than that predicted by the current equation of AASHTO. The results of this study showed that AI can be a proper alternative to the existing methods for predicting the fatigue life of shear connectors.

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Section: Introductioncontrasting

confidence: 64%

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Roshanfar,

Ghiami Azad,

Forouzanfar

2023

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

show abstract

“…Composite systems have always been of interest as they benefit from the combined properties of different materials simultaneously. Shear connectors are mainly used in steel-concrete composite systems to establish a connection through which the developed shear forces at the interface of the materials can be collected and transferred [1,2]. In composite beams with partial interaction, a specific number of shear connectors are employed along the length of beams, and these connectors primarily control the behavior of the beams under different loading conditions [3,4].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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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Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework

Morasaei

Ghabussi

Aghlmand

et al. 2021

Engineering with Computers

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Large-scale fatigue testing of post-installed shear connectors in partially-composite bridge girders

Cited by 8 publications

References 6 publications

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

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

Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework

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