Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

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@Article{Khan:2022:Materials,

• author = "Sangeen Khan and Mohsin Ali Khan and Adeel Zafar and Muhammad Faisal Javed and Fahid Aslam and Muhammad Ali Musarat and Nikolai Ivanovich Vatin",
• title = "Predicting the Ultimate Axial Capacity of Uniaxially Loaded {CFST} Columns Using Multiphysics Artificial Intelligence",
• journal = "Materials",
• year = "2022",
• volume = "15",
• number = "1",
• article-number = "39",
• keywords = "genetic algorithms, genetic programming, Gene Expression Programming, concrete filled steel tube, artificial neural network, multi-physics model, Random Forest Regression, Adaptive Neuro-Fuzzy Inference System, gene expression programming, bearing capacity of columns",
• ISSN = "1996-1944",
• URL = "https://www.mdpi.com/1996-1944/15/1/39",
• URL = "https://www.mdpi.com/1996-1944/15/1/39.pdf",
• pubmedid = "35009186",
• DOI = "doi:10.3390/ma15010039",
• size = "27 pages",
• abstract = "The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 data points in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.",
• notes = "Department of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan",

}

Genetic Programming entries for Sangeen Khan Mohsin Ali Khan Adeel Zafar Muhammad Faisal Javed Fahid Aslam Muhammad Ali Musarat Nikolai Ivanovich Vatin

Citations