Machine learning (ML) based models for predicting the ultimate strength of rectangular concrete-filled steel tube (CFST) columns under eccentric loading

Wang, Chen; Chan, Tak‐Ming

doi:10.1016/j.engstruct.2022.115392

Cited by 31 publications

(5 citation statements)

References 40 publications

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“…Compared with some ML models introduced in the literature, as summarized in Table 1 , the developed models achieved notable improvement in prediction accuracy. The introduced GPR model exhibits an a20-index of 98.8%, surpassing the models introduced by Wang et al 26 (a20-index = 96%) and the GPR model proposed by Le et al 15 (a20-index = 92.5%). The enhanced performance of the introduced GPR model compared to the GPR model of Le et al 15 can be attributed to using a combination of kernels, which can capture various aspects of the data, including smoothness, noise, and variations.…”

Section: Performance and Results Of ML Modelsmentioning

confidence: 55%

“…During the training phase, the grid searching technique was employed for tuning the model hyperparameters, and fivefold cross-validation was utilized to reduce overfitting issues. As recommended by Nguyen 2020 23 and other studies 24 , 26 , eighty percent of the original dataset was chosen randomly for training, leaving the remaining 20% to test the models. To compare and evaluate the effectiveness and reliability of the introduced models, two different ML models, including the support vector machine integrated with particle swarm optimized (PSVR) 22 and ANN models, were introduced.…”

Section: Performance and Results Of ML Modelsmentioning

confidence: 99%

“…ML has emerged as a promising tool to tackle complicated problems by conserving resources using existing experimental tests and lessening the necessity for additional testing 12 – 17 . Recently, many studies have employed various ML algorithms, including artificial neural networks (ANNs), support vector regression (SVR) 18 , and Gaussian process (GPR) 19 , to develop empirical formulas and statistical models for predicting the compressive strength of RCFST columns based on experimental tests collected from the literature and have provided positive satisfactory outcomes 20 – 26 .…”

Section: Introductionmentioning

confidence: 99%

“…Memarzadeh et al 25 predicted the axial capacity of square CFST specimens by training GEP and ANN models using 347 axially loaded rectangular CFST specimens. Wang et al 26 trained three models, including SVR, ANN, and random forest (RFR) models, to predict the strength of eccentrically loaded rectangular CFST specimens. Table 1 summarizes the recent machine learning models in predicting square and rectangular CFST column strength.…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning models in the capacity prediction of RCFST columns

Megahed,

Mahmoud,

Abd-Rabou

2023

Sci Rep

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Rectangular concrete-filled steel tubular (RCFST) columns are widely used in structural engineering due to their excellent load-carrying capacity and ductility. However, existing design equations often yield different design results for the same column properties, leading to uncertainty for engineering designers. Furthermore, basic regression analysis fails to precisely forecast the complicated relation between the column properties and its compressive strength. To overcome these challenges, this study suggests two machine learning (ML) models, including the Gaussian process (GPR) and the extreme gradient boosting model (XGBoost). These models employ a range of input variables, such as the geometric and material properties of RCFST columns, to estimate their strength. The models are trained and evaluated based on two datasets consisting of 958 axially loaded RCFST columns and 405 eccentrically loaded RCFST columns. In addition, a unitless output variable, termed the strength index, is introduced to enhance model performance. From evolution metrics, the GPR model emerged as the most accurate and reliable model, with nearly 99% of specimens with less than 20% error. In addition, the prediction results of ML models were compared with the predictions of two existing standard codes and different ML studies. The results indicated that the developed ML models achieved notable enhancement in prediction accuracy. In addition, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The feature analysis results reveal that the column length and load end-eccentricity parameters negatively impact compressive strength.

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Section: Performance and Results Of ML Modelsmentioning

confidence: 55%

Section: Performance and Results Of ML Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of machine learning models in the capacity prediction of RCFST columns

Megahed,

Mahmoud,

Abd-Rabou

2023

Sci Rep

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“…In the CFST members, the outer steel tube provides the confinement to the infill concrete, whereas, the concrete helps delaying the local buckling of the steel tube, thereby resulting in higher load-carrying capacity and ductility [1]. In the past, a wide range of investigations have been conducted on the axial performance of CFST columns that studied its behaviour under various forms of loading [2][3][4][5][6]. From the existing research investigations, it is evident that under axial compression the capacity of both steel tube and infill concrete can be fully realised and a composite behaviour can be achieved.…”

Section: Introductionmentioning

confidence: 99%

Study on headed shear studs influencing the interfacial bond behaviour in CFST columns

Debnath,

Chen,

Varma

et al. 2023

ce papers

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The performance of concrete‐filled steel tubular (CFST) columns with headed shear studs under push‐out loading has been investigated. The primary objective of the research programme was to investigate the bond behaviour between the steel tube and the infill concrete core of the square CFST column. A numerical investigation has been carried out using the commercially available finite element software. For the simulated numerical models, they have been initially validated with existing experimental results. The validations were made in terms of failure modes, bond strength‐slip curves and the initial stiffness. The validated models were further used to carryout parametric studies. The parameters studied include, vertical spacing of shear studs, horizontal spacing of shear studs, and CFST cross‐section slenderness. From the investigation, it is observed that with reduced vertical spacing of studs, the bond strength increases significantly. Specimens having horizontal spacing of studs as width/4 has more bond strength than specimen with horizontal spacing of width/2 and 0. For specimens with reduced vertical and horizontal spacing of studs though leads to higher bond stress, but also drops suddenly due to loss of contact between the stud and concrete. Therefore, 70% of the peak stress value may be considered as the achieved bond stress, as this stress value sustains for higher slip values. Lastly, as the column cross‐section slenderness reduces, there is a trend in increase in interfacial bond strength.

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Numerical and machine learning models for concentrically and eccentrically loaded CFST columns confined with FRP wraps

Xu,

Zhang,

Isleem

et al. 2024

Structural Concrete

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Previous research largely concentrated on predicting load‐carrying capacities of concrete‐filled steel tubes (CFST) confined with fiber‐reinforced polymer (FRP) wraps under pure concentric loads, neglecting the more complex failure mechanisms that occur under real‐life eccentric loading conditions. This study, therefore, employs both finite element modeling (FEM) and machine learning methods to accurately predict the load‐bearing capacities under both concentric and eccentric loading conditions. This research analyzed a comprehensive dataset comprising 128 experimental tests and an equivalent number of FEM simulations designed to evaluate their eccentric performance. These models have been thoroughly generated and validated against existing literature. Additionally, the developed ML models, particularly a hybrid deep learning model, demonstrated significant predictive accuracy, with an average R2 value of 0.969 across all model folds. Partial dependence analysis further highlighted the significant influence of concrete strength and the interactive effects of steel tube area and FRP wrap thickness on the load‐carrying capacity of the columns. Furthermore, to enhance cost‐efficiency and resource management compared with traditional laboratory testing, a user‐friendly graphical user interface (GUI) has been developed and hosted on an open‐source platform such as GitHub. This interface supports real‐time, precise predictive capabilities and promotes a collaborative environment for ongoing model refinement and improvement.

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Machine learning (ML) based models for predicting the ultimate strength of rectangular concrete-filled steel tube (CFST) columns under eccentric loading

Cited by 31 publications

References 40 publications

Application of machine learning models in the capacity prediction of RCFST columns

Application of machine learning models in the capacity prediction of RCFST columns

Study on headed shear studs influencing the interfacial bond behaviour in CFST columns

Numerical and machine learning models for concentrically and eccentrically loaded CFST columns confined with FRP wraps

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