Shear response of steel fiber reinforced recycled concrete-filled steel tube columns

Liu, Zhenzhen; Lu, Yiyan; Liao, Jiancong

doi:10.1177/13694332211009322

Cited by 9 publications

(4 citation statements)

References 54 publications

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“…In order to train and test the ML models, a database was compiled from the experimental results of 230 shear strength tests reported in the literature. [21][22][23][24][25][26][27][28][29][30][31]70 Only the specimens with circular cross-sections that had experienced shear failure under loading were included in this database. The specimens with square cross sections and those that had experienced flexural or flexural-shear failure were excluded.…”

Section: Experimental Databasementioning

confidence: 99%

“…In order to train and test the ML models, a database was compiled from the experimental results of 230 shear strength tests reported in the literature 21–31,70 . Only the specimens with circular cross‐sections that had experienced shear failure under loading were included in this database.…”

Section: Experimental Databasementioning

confidence: 99%

“…The shear tests of Ye et al 25 and Lehman et al 26 showed that these members tend to exhibit a relatively ductile shear behavior. Other experimental studies have also investigated the impact of longitudinal and transverse reinforcement, 27 the use of steel fibers in concrete, 28,29 the use of recycled concrete, 29,30 and the use of precast segments containing demolished concrete lumps 31 on the shear behavior of CFSTs.…”

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confidence: 99%

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Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes

Mansouri,

Mangalathu

2024

Structural Design Tall Build

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SummaryPrecise estimation of the shear strength of concrete‐filled steel tubes (CFSTs) is a crucial requirement for the design of these members. The existing design codes and empirical equations are inconsistent in predicting the shear strength of these members. This paper provides a data‐driven approach for the shear strength estimation of circular CFSTs. For this purpose, the authors evaluated and compared the performance of nine machine learning (ML) methods, namely linear regression, decision tree (DT), k‐nearest neighbors (KNN), support vector regression (SVR), random forest (RF), bagging regression (BR), adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) in estimating the shear strength of CFSTs on an experimental database compiled from the results of 230 shear tests on CFSTs in the literature. For each model, hyperparameter tuning was performed by conducting a grid search in combination with k‐fold cross‐validation (CV). Comparing the nine methods in terms of several performance measures showed that the XGBoost model was the most accurate in predicting the shear strength of CFSTs. This model also showed superior accuracy in predicting the shear strength of CFSTs when compared to the formulas provided in design codes and the existing empirical equations. The Shapley Additive exPlanations (SHAP) technique was also used to interpret the results of the XGBoost model. Using SHAP, the features with the greatest impact on the shear strength of CFSTs were found to be the cross‐sectional area of the steel tube, the axial load ratio, and the shear span ratio, in that order.

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Section: Experimental Databasementioning

confidence: 99%

Section: Experimental Databasementioning

confidence: 99%

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confidence: 99%

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Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes

Mansouri,

Mangalathu

2024

Structural Design Tall Build

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“…Many types of structures have benefitted from the application of combined constructional elements from various materials and physical properties (Cheng et al, 2020; Liu et al, 2021; Machelski and Toczkiewicz, 2006). Concrete-filled steel tube (CFST) members have gained great attentions due to several structural benefits, namely higher strength, advantageous ductility, and large energy absorption capacities (Natalli et al, 2021; Tao et al, 2013; Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Performance assessment of post-tensioned concrete-filled steel tube beams using unbonded tendons with uncertainty quantification

Ghaemdoust,

Yang,

Wang

et al. 2023

Advances in Structural Engineering

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The centerpiece of many research projects is evaluating the structural performance of the concrete-filled steel tubes (CFSTs). When these structural elements are employed as flexural members, some parts of the concrete infill are subjected to tension, and consequently the concrete is cracked so that the confinement effect is reduced. Therefore, the application of tendons and modification of the CFST configuration are deployed to improve the serviceability of the CFST beams. A non-linear finite element (FE) model considering the effects of the material and geometrical non-linearity and initial imperfection is developed. Concurrently, an analytical model based on the plastic stress distribution method (PSDM) is proposed to compare the results. In addition, a probabilistic assessment along with the uncertainty quantification is conducted to calculate the reliability indices and the sensitivity of the beams to the variation of the input variables. The results of the study demonstrate that the implemented measures considerably enhance the moment capacity and serviceability performance of the CFST beams. Moreover, the sensitivity analyses show the significance of the variables on the model output with different steel yield stress values and the importance is ordered as the steel tube width followed by the steel tube thickness and the concrete strength.

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Research advances in fiber‐reinforced concrete‐filled steel tube columns

Liu,

Ma,

et al. 2024

Structural Design Tall Build

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SummaryFiber‐reinforced concrete‐filled steel tube (CFST) adopts the microskeleton and bridging effect of fibers, thereby optimizing the confinement effect between the steel tube and concrete core while reducing the concrete core friability. This structure offers a viable solution for solving the interface disengagement and insufficient ductility problems of conventional CFSTs. For further theoretical research and engineering application, the mechanical properties of fiber‐reinforced CFSTs under different loading conditions are reviewed. The research results are summarized, and future research scopes are suggested. The literature review shows that adding fibers improves the ductility of CFSTs significantly but the bearing capacity only slightly. The bond strength between steel tube and concrete core is enhanced by fibers, and the degradation in the bond strength is simultaneously delayed. However, in existing research, the mechanical properties and design method are still inadequate. More experimental works, further theoretical analyses, and numerical simulation should be undertaken to establish the quantitative relations between the generalized fiber parameters and structural performance of CFSTs. Future research should propose a unified design theory of fiber‐reinforced CFST structures based on service performance requirements.

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Shear response of steel fiber reinforced recycled concrete-filled steel tube columns

Cited by 9 publications

References 54 publications

Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes

Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes

Performance assessment of post-tensioned concrete-filled steel tube beams using unbonded tendons with uncertainty quantification

Research advances in fiber‐reinforced concrete‐filled steel tube columns

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