Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models
During design and construction of buildings, the employed materials can substantially impact the structures’ performance. In composite columns, the properties and performance of concrete and steel have a significant influence on the behavior of structure under various loading conditions. In this stu...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1944/15/9/3309 |
| _version_ | 1797503971565240320 |
|---|---|
| author | Payam Sarir Danial Jahed Armaghani Huanjun Jiang Mohanad Muayad Sabri Sabri Biao He Dmitrii Vladimirovich Ulrikh |
| author_facet | Payam Sarir Danial Jahed Armaghani Huanjun Jiang Mohanad Muayad Sabri Sabri Biao He Dmitrii Vladimirovich Ulrikh |
| author_sort | Payam Sarir |
| collection | DOAJ |
| description | During design and construction of buildings, the employed materials can substantially impact the structures’ performance. In composite columns, the properties and performance of concrete and steel have a significant influence on the behavior of structure under various loading conditions. In this study, two metaheuristic algorithms, particle swarm optimization (PSO) and competitive imperialism algorithm (ICA), were combined with the artificial neural network (ANN) model to predict the bearing capacity of the square concrete-filled steel tube (SCFST) columns. To achieve this objective and investigate the performance of optimization algorithms on the ANN, one of the most extensive datasets of pure SCFST columns (with 149 data samples) was used in the modeling process. In-depth and detailed predictive modeling of metaheuristic-based models was conducted through several parametric investigations, and the optimum factors were designed. Furthermore, the capability of these hybrid models was assessed using robust statistical matrices. The results indicated that PSO is stronger than ICA in finding optimum weights and biases of ANN in predicting the bearing capacity of the SCFST columns. Therefore, each column and its bearing capacity can be well-predicted using the developed metaheuristic-based ANN model. |
| first_indexed | 2024-03-10T03:58:00Z |
| format | Article |
| id | doaj.art-e0d4244e941743e6bab072810744b956 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T03:58:00Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-e0d4244e941743e6bab072810744b9562023-11-23T08:41:32ZengMDPI AGMaterials1996-19442022-05-01159330910.3390/ma15093309Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network ModelsPayam Sarir0Danial Jahed Armaghani1Huanjun Jiang2Mohanad Muayad Sabri Sabri3Biao He4Dmitrii Vladimirovich Ulrikh5College of Civil Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76 Lenin Prospect, 454080 Chelyabinsk, RussiaCollege of Civil Engineering, Tongji University, Shanghai 200092, ChinaPeter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, RussiaDepartment of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76 Lenin Prospect, 454080 Chelyabinsk, RussiaDuring design and construction of buildings, the employed materials can substantially impact the structures’ performance. In composite columns, the properties and performance of concrete and steel have a significant influence on the behavior of structure under various loading conditions. In this study, two metaheuristic algorithms, particle swarm optimization (PSO) and competitive imperialism algorithm (ICA), were combined with the artificial neural network (ANN) model to predict the bearing capacity of the square concrete-filled steel tube (SCFST) columns. To achieve this objective and investigate the performance of optimization algorithms on the ANN, one of the most extensive datasets of pure SCFST columns (with 149 data samples) was used in the modeling process. In-depth and detailed predictive modeling of metaheuristic-based models was conducted through several parametric investigations, and the optimum factors were designed. Furthermore, the capability of these hybrid models was assessed using robust statistical matrices. The results indicated that PSO is stronger than ICA in finding optimum weights and biases of ANN in predicting the bearing capacity of the SCFST columns. Therefore, each column and its bearing capacity can be well-predicted using the developed metaheuristic-based ANN model.https://www.mdpi.com/1996-1944/15/9/3309structural performancesquare concrete-filled steel tube columnsmetaheuristic-based ANN modelspredictive models |
| spellingShingle | Payam Sarir Danial Jahed Armaghani Huanjun Jiang Mohanad Muayad Sabri Sabri Biao He Dmitrii Vladimirovich Ulrikh Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models Materials structural performance square concrete-filled steel tube columns metaheuristic-based ANN models predictive models |
| title | Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models |
| title_full | Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models |
| title_fullStr | Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models |
| title_full_unstemmed | Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models |
| title_short | Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models |
| title_sort | prediction of bearing capacity of the square concrete filled steel tube columns an application of metaheuristic based neural network models |
| topic | structural performance square concrete-filled steel tube columns metaheuristic-based ANN models predictive models |
| url | https://www.mdpi.com/1996-1944/15/9/3309 |
| work_keys_str_mv | AT payamsarir predictionofbearingcapacityofthesquareconcretefilledsteeltubecolumnsanapplicationofmetaheuristicbasedneuralnetworkmodels AT danialjahedarmaghani predictionofbearingcapacityofthesquareconcretefilledsteeltubecolumnsanapplicationofmetaheuristicbasedneuralnetworkmodels AT huanjunjiang predictionofbearingcapacityofthesquareconcretefilledsteeltubecolumnsanapplicationofmetaheuristicbasedneuralnetworkmodels AT mohanadmuayadsabrisabri predictionofbearingcapacityofthesquareconcretefilledsteeltubecolumnsanapplicationofmetaheuristicbasedneuralnetworkmodels AT biaohe predictionofbearingcapacityofthesquareconcretefilledsteeltubecolumnsanapplicationofmetaheuristicbasedneuralnetworkmodels AT dmitriivladimirovichulrikh predictionofbearingcapacityofthesquareconcretefilledsteeltubecolumnsanapplicationofmetaheuristicbasedneuralnetworkmodels |