Use of the Gene-Expression Programming Equation and FEM for the High-Strength CFST Columns

Jiang, Huanjun; Mohammed, Ahmed Salih; Kazeroon, Reza Andasht; Sarir, Payam

doi:10.3390/app112110468

Cited by 24 publications

(5 citation statements)

References 56 publications

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“…Javed et al 23 implemented GEP to predict the load-bearing strength of circular CFST long columns. Furthermore, Jiang et al 24 compared GEP results and finite element analysis outcomes for circular CFST columns. Naser et al 25 employed GA and GEP to develop predictive models for the axial ultimate load of rectangular and circular CFST columns.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the axial compression capacity of stub CFST columns using machine learning techniques

Megahed,

Mahmoud,

Abd-Rabou

2024

Sci Rep

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Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models—Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1316 specimens was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML algorithms, including support vector regression optimized with particle swarm optimization (PSVR), artificial neural networks, XGBoost (XGB), CatBoost (CATB), Random Forest, and LightGBM models. Through performance metrics, the CATB, GPR, PSVR and XGB models emerge as the most accurate and reliable models from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that section slenderness ratio and concrete strength parameters negatively impact the compressive strength index.

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Section: Introductionmentioning

confidence: 99%

Prediction of the axial compression capacity of stub CFST columns using machine learning techniques

Megahed,

Mahmoud,

Abd-Rabou

2024

Sci Rep

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“…Ref [26] proposes a hybrid model that includes an ANN with a particle swarm optimization (PSO) algorithm. This model was used to predict flexural bending capacity and flexural stiffness at the initial and serviceability limits of CFST beams.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Models for Determining the Load-Bearing Capacity of Eccentrically Compressed Short Concrete-Filled Steel Tubular Columns

Chepurnenko,

Turina,

Akopyan

2024

CivilEng

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Artificial neural networks (ANN) have a great promise in predicting the load-bearing capacity of building structures. The purpose of this work was to develop ANN models to determine the ultimate load of eccentrically compressed concrete-filled steel tubular (CFST) columns of circular cross-sections, which operated on the widest possible range of input parameters. Short columns were considered for which the amount of deflection does not affect the bending moment. A feedforward network was selected as the neural network type. The input parameters of the neural networks were the outer diameter of the columns, the thickness of the pipe wall, the yield strength of steel, the compressive strength of concrete and the relative eccentricity. Artificial neural networks were trained on synthetic data generated based on a theoretical model of the limit equilibrium of CFST columns. Two ANN models were created. When training the first model, the ultimate loads were determined at a given eccentricity of the axial force without taking into account additional random eccentricity. When training the second model, additional random eccentricity was taken into account. The total volume of the training dataset was 179,025 samples. Such a large training dataset size has never been used before. The training dataset covers a wide range of changes in the characteristics of the pipe metal and concrete of the core, pipe diameters and wall thicknesses, as well as eccentricities of the axial force. The trained models are characterized by high mean square error (MSE) scores. The correlation coefficients between the predicted and target values are very close to 1. The ANN models were tested on experimental data for 81 eccentrically compressed samples presented in five different works and 265 centrally compressed samples presented in twenty-six papers.

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“…Various studies have been implemented on the effects of different parameters, including fibers, mineral admiXtures, and curing conditions, on the behavior of UHPFRC. For instance, the effects of various types of fibers on the mechanical performance and ductility behavior cured in water have been studied by researchers [28,29]. For this purpose, a w/b of 0.195 and different types of fiber content ranging between 0.25 and 2 % fiber volume fraction were used.…”

Section: Introductionmentioning

confidence: 99%

Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures

Emad

Mohammed

Brás

et al. 2022

Construction and Building Materials

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Use of the Gene-Expression Programming Equation and FEM for the High-Strength CFST Columns

Cited by 24 publications

References 56 publications

Prediction of the axial compression capacity of stub CFST columns using machine learning techniques

Prediction of the axial compression capacity of stub CFST columns using machine learning techniques

Artificial Neural Network Models for Determining the Load-Bearing Capacity of Eccentrically Compressed Short Concrete-Filled Steel Tubular Columns

Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures

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