Modeling the corrosion initiation time of slag concrete using the artificial neural network

Hodhod, Osama A.; Ahmed, H.

doi:10.1016/j.hbrcj.2013.12.002

Cited by 33 publications

(13 citation statements)

References 5 publications

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“…Neural networks are used to find the solutions to constrained optimization problems and can be applied for storing, recalling classifying and mapping data or patterns [16]. Some ANN models related to measuring steel corrosion in concrete are found in [8], [17], [18], [19], [20] and [21].…”

Section: Ann Modelling For Corrosionmentioning

confidence: 99%

An Artificial Neural Network Model for the Corrosion Current Density of Steel in Mortar Mixed With Seawater

C¹

2019

GEOMATE

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Corrosion is a very complicated phenomenon in the field of science and engineering. Over the years, several numerical models have been developed to predict the damage caused by the corrosion process. The use of the artificial neural network in modeling corrosion has gained popularity in recent years. Many of the factors affecting corrosion are difficult to control. Thus, the artificial neural network may be a better technique to consider due to its ability to tolerate relatively imprecise, noisy or incomplete data, less vulnerability to outliers, filtering capacity and adaptability. This study aims to generate a corrosion current density prediction model using the artificial neural network approach. Microcell corrosion current density is defined as the rate of corrosion expressed in electric current per unit area of cross-section. Several variables were considered as input variables namely: age, water to cement ratio, cement content, compressive strength, type of mixing water, corrosion potential, solution resistance, and polarization resistance. These variables were entered into the neural network architecture and simulated in MATLAB. The feedforward backpropagation technique was used to generate the best model for the corrosion current density. The best neural network architecture consists of 8 input variable, 8 neurons in the hidden layer and one output variable. The resulting neural network model satisfactorily predicted the corrosion current density with a coefficient of correlation values of 0.96536, 0.80817, and 0.7662 for training, validation and testing phases, respectively.

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Section: Ann Modelling For Corrosionmentioning

confidence: 99%

An Artificial Neural Network Model for the Corrosion Current Density of Steel in Mortar Mixed With Seawater

C¹

2019

GEOMATE

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“…e results have revealed that, in terms of the regression coefficient R 2 and MSE, ANN models have provided better results than MLR. Other studies have used ANNs for predicting some durability-related properties of concrete [28][29][30][31][32][33][34][35]. ey concluded that ANN models could be used effectively in predicting the concrete durability properties.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria‐Based Concrete

al-Swaidani

Khwies

2018

Advances in Civil Engineering

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Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

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“…These approaches use input parameters to model responses, and the output models are validated by experimentation. For construction applications, ML algorithms estimate concrete strength [ 43 , 44 , 45 , 46 , 47 ], bituminous mixture performance [ 48 ], and concrete durability [ 49 , 50 , 51 ].…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

et al. 2022

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The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na2SiO3/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study’s aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient (R2). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts.

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Modeling the corrosion initiation time of slag concrete using the artificial neural network

Cited by 33 publications

References 5 publications

An Artificial Neural Network Model for the Corrosion Current Density of Steel in Mortar Mixed With Seawater

An Artificial Neural Network Model for the Corrosion Current Density of Steel in Mortar Mixed With Seawater

Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria‐Based Concrete

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

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