Analysis of training parameters in the ANN learning process to mapping the concrete carbonation depth

Dimensionamento Otimizado De Lajes Nervuradas, Utilizando Algoritmo Genético

“…De acordo com Felix et al (2019) em todo processo de modelagem, a escolha das variáveis do modelo é de suma importância, pois a seleção inapropriada pode dificultar ou fazer com que as RNA não consigam processar informações, inviabilizando o mapeamento entre os dados de entrada e saída.…”

Development of hybrid machine learning-based carbonation models with weighting function

“…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.…”

“…and Kellouche et al showed that ANNs had better performance in carbonation depth prediction than other analytical models [15,17]. In addition, Akpinar et al reported the efficiency of an ANN in carbonation frontier prediction and discussed the importance of different influences on carbonation depth based on their proposed ML model [18].…”

Long Short-Term Memory-Based Methodology for Predicting Carbonation Models of Reinforced Concrete Slab Bridges: Case Study in South Korea

“…Artificial neural network (ANN) algorithms have been applied to solve several complex problems in concrete technology [7][8][9][10][11][12][13], and ANN-based models are widely used for estimating specific carbonation depths [14][15][16][17][18][19]. Although most studies consider certain influencing parameters, environmental conditions are not considered because of continuous changes in climate [20,21].…”