ArticleMaterials (Basel, Switzerland)2021
Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest.Materials (Basel, Switzerland) · 2026Article
- Axial Load Capacity Prediction of Concrete-Filled Steel Tubes Using Machine Learning: A Comparative Study.Materials (Basel, Switzerland) · 2026Article
- Modeling mechanical properties of rubberized concrete using gene expression programming (GEP) and random forest: a comparative study.Scientific reports · 2026Article
- Modeling the mechanical properties of lightweight high-strength concrete incorporating supplementary cementitious materials using multi-expression programming and random forest.Scientific reports · 2026Article
- Comparative performance evaluation of machine learning models for predicting the ultimate bearing capacity of shallow foundations on granular soils.Scientific reports · 2025Article
- Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.Scientific reports · 2024Article
- Article
- Physics-assisted machine learning methods for predicting the splitting tensile strength of recycled aggregate concrete.Scientific reports · 2023Article
- Prediction of Axial Capacity of Concrete Filled Steel Tubes Using Gene Expression Programming.Materials (Basel, Switzerland) · 2022Article
- Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming.Materials (Basel, Switzerland) · 2022Article
- Article
- Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming.Materials (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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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 datapoints 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
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