A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shariati, Mahdi; Mafipour, Mohammad Saeed; Mehrabi, Peyman; Shariati, Ali; Toghroli, Ali; Trung, Nguyen Thoi; Salih, Musab N.A.

doi:10.1007/s00366-019-00930-x

Cited by 120 publications

(31 citation statements)

References 37 publications

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“…In this paper, five objective criteria, including correlation coefficient (R 2 ), Pearson's correlation coefficient (PR), Nash-Sutcliffe efficiency (NS), root mean square error (RMSE), mean absolute error (MAE) and Wilmot index (WI) were used to evaluate the accuracy of the results and the reliability of the proposed neural network [29][30][31][32]45]. Nash-Sutcliffe (NS) efficiency is a normalised statistic that determines the relative amount of residual variance compared to the variance of calculation (Nash and Sutcliffe [65]).…”

Section: Performance Evaluationmentioning

confidence: 99%

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Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement

et al. 2021

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This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The finite element method (FEM) was employed to increase the available datasets and evaluate the proposed reinforcement method in different geometrical types of sections. The most influential factors on the axial strength were investigated using a feature-selection (FS) method within a multi-layer perceptron (MLP) algorithm. The MLP algorithm was developed by particle swarm optimization (PSO) and FEM results as input. In terms of accuracy evaluation, some of the rolling criteria including results showed that geometrical parameters have almost the same contribution in compression capacity and displacement of the specimens. According to the performance evaluation indexes, the best model was detected and specified in the paper and optimised by tuning other parameters of the algorithm. As a result, the normalised ultimate load and displacement were predicted successfully.

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Section: Performance Evaluationmentioning

confidence: 99%

“…Generally, artificial intelligence (AI) techniques are able to address some of the previous engineering issues due to their advantages compared to classic methods [31][32][33][34][35][36]. Learning and mocking are two significant points of AI, which make these algorithms favourable for researchers [37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement

et al. 2021

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“…Khorramian et al [75] experimentally investigated the 112.5° and 135° tilted positions of angle shear connectors with the steel beam and various angle sizes and lengths for strength improvement. Shariati et al [76] introduced soft computing artificial intelligence techniques and an adaptive neuro-fuzzy inference approach to forecast the behaviour of Cshaped tilt-angle connectors. Their findings open that the slip is a predominant factor and the inclination angle has secondary importance in the shear strength of tilted connectors.…”

Section: C-shaped Angle and Channel Shear Connectorsmentioning

confidence: 99%

Review of Various Shear Connectors in Composite Structures

2021

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Shear connectors are devices that provide shear connection at the interface of steel girders and reinforced concrete slabs in composite structures to accomplish composite action in a flexure. The seismic response of composite structures can be controlled using properly designed shear connectors. This state-of-the-art review article presents considerable information about the distinct types of shear connectors employed in composite structures. Various types of shear connectors, their uniqueness and characteristics, testing methods and findings obtained during the last decade are reviewed. The literature, efficacy, and applicability of the different categories of shear connectors, for example, headed studs, perfobond ribs, fibre reinforced polymer perfobonds, channels, pipes, Hilti X-HVB, composite dowels, demountable bolted shear connectors, and shear connectors in composite column are thoroughly studied. The conclusions made provide a response to the flow of the use of shear connectors for their behaviours, strength, and stiffness to achieve composite action.

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“…The structural performance of shear connectors has been evaluated using NN algorithms. In a study, three well-known algorithms including Extreme Learning Machine (ELM), ANFIS and ANN were applied on the test results of shear strength from tilted angle connector samples and based on the results, all three algorithms produced competitive outcomes, while ELM performed slightly better than other algorithms [66]. The combination of ANN with PSO was developed in a study to predict the slip value of channel shear connectors embedded in normal and high strength concrete, where PSO showed a considerable role in improving the accuracy of the prediction [67].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Performance of a Composite Profile at Elevated Temperatures Using Finite Element and Hybrid Artificial Intelligence Techniques

Ding¹,

Alharbi

Almadhor

et al. 2022

Materials

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It is very important to keep structures and constructional elements in service during and after exposure to elevated temperatures. Investigation of the structural behaviour of different components and structures at elevated temperatures is an approach to manipulate the serviceability of the structures during heat exposure. Channel connectors are widely used shear connectors not only for their appealing mechanical properties but also for their workability and cost-effective nature. In this study, a finite element (FE) evaluation was performed on an authentic composite model, and the behaviour of the channel shear connector at elevated temperature was examined. Furthermore, a novel hybrid intelligence algorithm based on a feature-selection trait with the incorporation of particle swarm optimization (PSO) and multi-layer perceptron (MLP) algorithms has been developed to predict the slip response of the channel. The hybrid intelligence algorithm that uses artificial neural networks is performed on derived data from the FE study. Finally, the obtained numerical results are compared with extreme learning machine (ELM) and radial basis function (RBF) results. The MLP-PSO represented dramatically accurate results for slip value prediction at elevated temperatures. The results proved the active presence of the channels, especially to improve the stiffness and loading capacity of the composite beam. Although the height enhances the ductility, stiffness is significantly reduced at elevated temperatures. According to the results, temperature, failure load, the height of connector and concrete block strength are the key governing parameters for composite floor design against high temperatures.

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A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Cited by 120 publications

References 37 publications

Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement

Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement

Review of Various Shear Connectors in Composite Structures

Evaluation of the Performance of a Composite Profile at Elevated Temperatures Using Finite Element and Hybrid Artificial Intelligence Techniques

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