Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Fuhaid, Abdulrahman Fahad Al; Alanazi, Hani

doi:10.3390/ma16031277

Cited by 6 publications

(3 citation statements)

References 72 publications

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Section: Model Evaluation Metricsmentioning

confidence: 99%

“…The smaller the value of these three, the smaller the error and the higher the prediction accuracy, that is, the better the point prediction effect. 13 The evaluation indicators are defined as follows: where n is the number of samples, y^i is the predicted value of the i th sample, and yi is the real value of the i th sample.…”

Section: Screening Index Interval Prediction Modelmentioning

confidence: 99%

See 1 more Smart Citation

Interval prediction of sinter screening index based on gate recurrent unit and kernel density estimation algorithm

Kang,

Wu,

Chen

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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Since there are many uncertain factors in the actual production process, reliable information cannot be obtained from point prediction results. Therefore, this paper proposes an interval prediction method to evaluate the screening index. Firstly, feature engineering algorithms are used to select the most suitable data and variables for modeling. Secondly, based on the sequential characteristics of the sintering process, the gated cycle unit (GRU) model is used to make point predictions on the indicators. then the kernel density estimation (KDE) algorithm is used to quantify the error of the index to obtain the interval prediction results, and the RandomizedSearchCV algorithm is used to optimize the parameters of the model. Finally, comparison shows that the GRU model has higher point prediction accuracy, and the KDE algorithm can better quantify the prediction error, and then obtain reliable interval prediction results. This method has guiding significance for the high-quality production of sinter.

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Section: Model Evaluation Metricsmentioning

confidence: 99%

Section: Screening Index Interval Prediction Modelmentioning

confidence: 99%

Interval prediction of sinter screening index based on gate recurrent unit and kernel density estimation algorithm

Kang,

Wu,

Chen

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

View full text Add to dashboard Cite

show abstract

“…Studies [157][158][159][160][161] have found that a large amount of machine-learning-based work has focused on concrete permeability resistance and the diffusion coefficient. Supervised machine learning approaches, such as SVM, RF, and ANN, are mainly used for chloride permeability and diffusion.…”

Section: Application Of Machine Learning To Chloride Penetrationsmentioning

confidence: 99%

Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review

Zhao,

Li,

Guan

2024

Buildings

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Chloride corrosion is a key factor affecting the life of marine concrete, and surface chloride concentration is the main parameter for analyzing its durability. In this paper, we first introduce six erosion mechanism models for surface chloride ion concentration, reveal the convection effect in the diffusion behavior of chloride ions, and then introduce the corrosion mechanisms that occur in different marine exposure environments. On this basis, the analysis is carried out using empirical formulations and machine learning methods, which provides a clearer understanding of the research characteristics and differences between empirical formulas and emerging machine learning techniques. This paper summarizes the time-varying model and multifactor coupling model on the basis of empirical analysis. It is found that the exponential function and the reciprocal function are more consistent with the distribution law of chloride ion concentration, the multifactor model containing the time-varying law is the most effective, and the Chen model is the most reliable. Machine learning, as an emerging method, has been widely used in concrete durability research. It can make up for the shortcomings of the empirical formula method and solve the multifactor coupling problem of surface chloride ion concentration with strong prediction ability. In addition, the difficulty of data acquisition is also a major problem that restricts the development of machine learning and incorporating concrete maintenance conditions into machine learning is a future development direction. Through this study, researchers can systematically understand the characteristics and differences of different research methods and their respective models and choose appropriate techniques to explore the durability of concrete structures. Moreover, intelligent computing will certainly occupy an increasingly important position in marine concrete research.

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A optimum prediction model of chloride ion diffusion coefficient of machine-made sand concrete based on different machine learning methods

Zheng,

Cai

2024

Construction and Building Materials

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Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Cited by 6 publications

References 72 publications

Interval prediction of sinter screening index based on gate recurrent unit and kernel density estimation algorithm

Interval prediction of sinter screening index based on gate recurrent unit and kernel density estimation algorithm

Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review

A optimum prediction model of chloride ion diffusion coefficient of machine-made sand concrete based on different machine learning methods

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