2020
DOI: 10.3989/mc.2020.02019
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Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks

Abstract: Carbonation is a deleterious concrete durability problem which may alter concrete microstructure and yield initiation of corrosion in reinforcing steel bars. Previous studies focused on the use of Artificial Neural Networks (ANN) for the prediction of concrete carbonation depth and to minimize the need for destructive and elaborated civil engineering laboratory tests. This study aims to provide improved accuracy of simulation and prediction of carbonation with an ANN architecture including eighteen input param… Show more

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Cited by 19 publications
(10 citation statements)
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“…In this study, four ML models (ANN, SVM and 2 hybrid models) were developed to estimate the carbonation depth of concrete incorporating fly-ash. Despite single ANN and SVM models being reported in past studies [35][36][37], it is not appropriate to directly compare their performance across different research fields, or that of the hybrid models demonstrated in this study because of the variation in datasets used. Therefore, the rationale for developing different models here was to appraise the performance of each using the same dataset, thus providing a reliable comparison among them.…”
Section: Model Developmentmentioning
confidence: 99%
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“…In this study, four ML models (ANN, SVM and 2 hybrid models) were developed to estimate the carbonation depth of concrete incorporating fly-ash. Despite single ANN and SVM models being reported in past studies [35][36][37], it is not appropriate to directly compare their performance across different research fields, or that of the hybrid models demonstrated in this study because of the variation in datasets used. Therefore, the rationale for developing different models here was to appraise the performance of each using the same dataset, thus providing a reliable comparison among them.…”
Section: Model Developmentmentioning
confidence: 99%
“…According to past publications, carbonation models can be classified into four categories based on how the relationship between carbonation depth and its influencing parameters is determined [1]: (1) empirical models, where the relationship between the carbonation depth and its determinants is derived from actual experiments [2][3][4][5][6][7][8][9], such as the square root model [3]; (2) statistical models, where the dependent and independent variables are related by mathematical functions, such as multiple linear regression [10]; (3) numerical models, which consider several physiochemical equations, including chemical reaction rates, mass conservation in gas-liquid two-phase flow, diffusion and dispersion of CO2 in water, energy conservation in porous media, and solubility of CO2 in water, and simulate the phenomena by computer software [11][12][13][14]; (4) machine learning (ML)-based models, which has been applied in recent simulations to find complex nonlinear relationships during the carbonation process [15][16][17][18]. However, all these models have their drawbacks.…”
Section: Introductionmentioning
confidence: 99%
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“…From the perspective of research methods, these pieces of research can be divided into theoretical models based on the carbonation mechanism and empirical models based on test results, and these pieces of research combined the two. In terms of the property of the models, these models can be divided into three types, which are as follows: the deterministic model [6][7][8][9][10][11][12][13][14][15][16][17], stochastic model [18], and other models based on the neural network, grey system theory, cellular automata, and other theories [19,20]. From the perspective of research objects, most of these pieces of research are based on the material, while a few are based on the component or structure.…”
Section: Introductionmentioning
confidence: 99%
“…The model is cost effective in mechanistic-empirical pavement design guide (MEPDG). However, predictive models for the design of mixture parameters cannot standalone without a complimentary binder model [43].…”
Section: Introductionmentioning
confidence: 99%