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
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 spacing...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/11056 |
| _version_ | 1797511262121230336 |
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| author | Ehsan Taheri Peyman Mehrabi Shervin Rafiei Bijan Samali |
| author_facet | Ehsan Taheri Peyman Mehrabi Shervin Rafiei Bijan Samali |
| author_sort | Ehsan Taheri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T05:42:52Z |
| format | Article |
| id | doaj.art-10431878d66c456d9b38580340031a5b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:42:52Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-10431878d66c456d9b38580340031a5b2023-11-22T22:23:00ZengMDPI AGApplied Sciences2076-34172021-11-0111221105610.3390/app112211056Numerical 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 DisplacementEhsan Taheri0Peyman Mehrabi1Shervin Rafiei2Bijan Samali3Centre for Infrastructure Engineering, Western Sydney University, Kingswood, Sydney, NSW 2747, AustraliaDepartment of Civil Engineering, K.N. Toosi University of Technology, Mirdamad, Tehran 1969764449, IranDepartment of Construction Engineering and Management, Amirkabir University of Technology, Tehran 1591634311, IranCentre for Infrastructure Engineering, Western Sydney University, Kingswood, Sydney, NSW 2747, AustraliaThis 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.https://www.mdpi.com/2076-3417/11/22/11056artificial intelligencefinite element methodcold-formedrack uprightfeature-selection methodmulti-layer perceptron |
| spellingShingle | Ehsan Taheri Peyman Mehrabi Shervin Rafiei Bijan Samali 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 Applied Sciences artificial intelligence finite element method cold-formed rack upright feature-selection method multi-layer perceptron |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | artificial intelligence finite element method cold-formed rack upright feature-selection method multi-layer perceptron |
| url | https://www.mdpi.com/2076-3417/11/22/11056 |
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