Hybrid machine learning-based prediction model for the bond strength of corroded Cr alloy-reinforced coral aggregate concrete

Niu, Ditao; Luo, Daming; Wang, Xiaoqian; Zhang, Lu; Su, Li; Li, Yalin

doi:10.1016/j.mtcomm.2023.106141

Cited by 6 publications

(2 citation statements)

References 41 publications

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“…Rock materials exhibit complex behaviours and a high level of uncertainty under laboratory testing [49][50][51][52][53][54]. Machine learning (ML) techniques have been developed and used by an increasing number of researchers in the last several decades [55][56][57][58][59][60][61][62]. Compared with traditional test methods and empirical models, ML can effectively find implicit relationships between variables and well handle nonlinear problems [63,64].…”

Section: References Shear Strength Model Peak Dilation Anglementioning

confidence: 99%

Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities

Xie,

Yao,

Yan

et al. 2023

Materials

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The peak dilation angle is an important mechanical feature of rock discontinuities, which is significant in assessing the mechanical behaviour of rock masses. Previous studies have shown that the efficiency and accuracy of traditional experimental methods and analytical models in determining the shear dilation angle are not completely satisfactory. Machine learning methods are popular due to their efficient prediction of outcomes for multiple influencing factors. In this paper, a novel hybrid machine learning model is proposed for predicting the peak dilation angle. The model incorporates support vector regression (SVR) techniques as the primary prediction tools, augmented with the grid search optimization algorithm to enhance prediction performance and optimize hyperparameters. The proposed model was employed on eighty-nine datasets with six input variables encompassing morphology and mechanical property parameters. Comparative analysis is conducted between the proposed model, the original SVR model, and existing analytical models. The results show that the proposed model surpasses both the original SVR model and analytical models, with a coefficient of determination (R2) of 0.917 and a mean absolute percentage error (MAPE) of 4.5%. Additionally, the study also reveals that normal stress is the most influential mechanical property parameter affecting the peak dilation angle. Consequently, the proposed model was shown to be effective in predicting the peak dilation angle of rock discontinuities.

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Section: References Shear Strength Model Peak Dilation Anglementioning

confidence: 99%

Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities

Xie,

Yao,

Yan

et al. 2023

Materials

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“…The protection of reinforcement in coral aggregate concrete (CAC) is currently being studied by researchers. For example, Wu Changyu et al [18,19] conducted electrochemical tests on various reinforcements in coral concrete specimens exposed for 0-270 days in the marine environment, used the linear polarization resistance method to determine the corrosion rates of various reinforcements, and discovered that 2205 duplex steel had the best corrosion resistance among various types of reinforcements. Electrochemical impedance spectroscopy (EIS) testing was used by Dabo et al [20] to examine the impact of various reinforcement types and protective layer thicknesses on the durability performance of coral concrete.…”

Section: Introductionmentioning

confidence: 99%

Protective Performance of Coated Reinforcement in Coral Concrete under Dry/Wet Cycling

Cao

Akbar

et al. 2023

Materials

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The actual protective performance of the coated reinforcement in coral concrete was investigated by measuring the chloride ion diffusion coefficient, electrochemical analysis, and numerical simulation. The test results show that the corrosion rate of coated reinforcement in coral concrete under the action of wet and dry cycles is kept at a low level, and the Rp value is always greater than 250 kΩ·cm2 during the test period, which is in the uncorroded state and has good protection performance. Moreover, the chloride ion diffusion coefficient D is in accordance with the power function relationship with the wet and dry cycle time, and a time-varying model of chloride ion concentration on the surface of coral concrete is established. The surface chloride ion concentration of coral concrete reinforcement was modeled as a time-varying model; the cathodic zone of coral concrete members was the most active, increasing from 0 V to 0.14 V from 0 to 20 years, with a large increase in potential difference before the 7th year, and a significant decrease in the increase after the 7th year.

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Compressive strength resistance coefficient of sustainable concrete in sulfate environments: Hybrid machine learning model and experimental verification

Sun,

Li,

Bei

et al. 2024

Materials Today Communications

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Hybrid machine learning-based prediction model for the bond strength of corroded Cr alloy-reinforced coral aggregate concrete

Cited by 6 publications

References 41 publications

Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities

Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities

Protective Performance of Coated Reinforcement in Coral Concrete under Dry/Wet Cycling

Compressive strength resistance coefficient of sustainable concrete in sulfate environments: Hybrid machine learning model and experimental verification

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