Experimental study and machine learning algorithms for evaluating the performance of U-shaped ultra-high performance reinforced fiber concrete under static and impact loads

Al-shawafi, Ali; Zhu, Han; Haruna, Sadi İbrahim; Zhao, Bo; Laqsum, Saleh Ahmed; Borito, Said Mirgan

doi:10.1016/j.jobe.2023.106389

Cited by 8 publications

(2 citation statements)

References 60 publications

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“…Haruna et al [23] developed a support vector machine (SVM) model that accurately predicted the impact resistance of U-shaped polyurethane-based polymer concrete. Al-shawafi et al [24] predicted the energy absorption capacity of Ultra High-Performance Reinforced Concrete (UHPFRC) in the failure cracking stage using ANN and multiple linear regression (MLR) methods. Although ML methods have shown certain advantages in analyzing the impact resistance of concrete structures, the "black-box" nature of ML methods often obscures the relationship between input variables and output parameters.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Interpretation of Residual Bearing Capacity of Cfst Columns under Impact Loads Based Interpretable Stacking Fusion Modeling

Yang,

Zhang

2023

Buildings

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The utilization of Concrete-filled steel Tubular (CFST) columns is increasingly widespread. However, the assessment of the residual bearing capacity of CFST columns currently relies mainly on costly and time-consuming experiments and numerical simulations. In this study, we propose a machine learning-based model for rapidly identifying the residual bearing capacity of CFST columns. The results demonstrate that the predictions of the proposed Stacking-KRXL model align well with the actual values, with most prediction errors falling within ±10%. The RSquared value of 0.97 significantly surpasses that of other methods. The stability and robustness of the model are analyzed. Additionally, the Shapley additive explanations method is applied for global and local interpretations, revealing positive or negative correlations between different parameters and the residual bearing capacity of CFST columns, mainly influenced by the concrete area in the core region.

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Section: Introductionmentioning

confidence: 99%

Prediction and Interpretation of Residual Bearing Capacity of Cfst Columns under Impact Loads Based Interpretable Stacking Fusion Modeling

Yang,

Zhang

2023

Buildings

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“…With this high demand, concrete has become the object Available Online at https://ojs.unik-kediri.ac.id/index.php/ukarst/article/view/4401 https://doi.org/10.30737/ukarst.v7i1.4401 of much research. Several concrete innovations have been developed, such as fiber concrete [3], ultra-high performance concrete (UHPC) [4], self-compacting concrete (SCC) [5], and selfhealing concrete [6]. However, it must undergo a series of laboratory tests to develop this technology.…”

Section: Introductionmentioning

confidence: 99%

Discrete Element Method Approach to Simulate Cracks in Four-Point Flexural Test

arif

Widodo

Nugroho

et al. 2023

U Karst

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Concrete is a material that is widely used in construction. Concrete research efforts are ongoing and through a series of experimental tests. On the other hand, experimental tests require a lot of money, take a long time, and create waste. Several studies have revealed that numerical testing can accurately test concrete to fractures. However, modeling for the four-point load flexure test pattern is still not widely discussed. This study aimed to model the four-point flexural test of concrete using the discrete element method (DEM) approach. Sieve gradation was performed to determine particle size, and flexure testing was performed to calibrate the DEM model. DEM flexure testing was made using Yet Another Dynamic Engine (YADE) software with ASTM D6272 reference and beam dimensions 105 x 105 x 535 mm. The cohesive contact model with spherical particles is used, and the algorithm developed modifies the faceted sphere of interaction. The study results revealed that DEM can simulate crack behavior in flexural testing of unreinforced concrete. The DEM results show only a 2.13% difference in the experimental results of the flexural strength test. Meanwhile, crack behavior can be observed directly in the DEM simulation. The results of this study can be used to predict the failure pattern of the flexural test structure and to design the right proportion of the mixture to match the desired flexural strength. So that material efficiency and concrete flexure testing time can be achieved.

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Static properties and impact resistance performance of U-shaped PU-modified concrete under repeated drop-weight impact load

Laqsum,

Zhu,

et al. 2023

Archiv.Civ.Mech.Eng

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Experimental study and machine learning algorithms for evaluating the performance of U-shaped ultra-high performance reinforced fiber concrete under static and impact loads

Cited by 8 publications

References 60 publications

Prediction and Interpretation of Residual Bearing Capacity of Cfst Columns under Impact Loads Based Interpretable Stacking Fusion Modeling

Prediction and Interpretation of Residual Bearing Capacity of Cfst Columns under Impact Loads Based Interpretable Stacking Fusion Modeling

Discrete Element Method Approach to Simulate Cracks in Four-Point Flexural Test

Static properties and impact resistance performance of U-shaped PU-modified concrete under repeated drop-weight impact load

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