Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach

Salami, Babatunde Abiodun; Olayiwola, Teslim; Oyehan, Tajudeen A.; Raji, Ishaq Adeyanju

doi:10.1016/j.conbuildmat.2021.124152

Cited by 45 publications

(15 citation statements)

References 87 publications

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“…While investigating Figure 12 b,d, it can be observed that the MAE values are smaller than the RMSE values, corroborating the statistical evidence [ 56 ]. Similar to the correlation comparison, the value of MAE was observed as 3.07 and 3.16 for the training and validation data of the GBT model.…”

Section: Resultssupporting

confidence: 76%

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

et al. 2022

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Cement production is one of the major sources of decomposition of carbonates leading to the emission of carbon dioxide. Researchers have proven that incorporating industrial wastes is of paramount significance for producing green concrete due to the benefits of reducing cement production. The compressive strength of concrete is an imperative parameter to consider when designing concrete structures. Considering high prediction capabilities, artificial intelligence models are widely used to estimate the compressive strength of concrete mixtures. A variety of artificial intelligence models have been developed in the literature; however, evaluation of the modeling procedure and accuracy of the existing models suggests developing such models that manifest the detailed evaluation of setting parameters on the performance of models and enhance the accuracy compared to the existing models. In this study, the computational capabilities of the adaptive neurofuzzy inference system (ANFIS), gene expression programming (GEP), and gradient boosting tree (GBT) were employed to investigate the optimum ratio of ground-granulated blast furnace slag (GGBFS) and fly ash (FA) to the binder content. The training process of GEP modeling revealed 200 chromosomes, 5 genes, and 12 head sizes as the best hyperparameters. Similarly, ANFIS hybrid subclustering modeling with aspect ratios of 0.5, 0.1, 7, and 150; learning rate; maximal depth; and number of trees yielded the best performance in the GBT model. The accuracy of the developed models suggests that the GBT model is superior to the GEP, ANFIS, and other models that exist in the literature. The trained models were validated using 40% of the experimental data along with parametric and sensitivity analysis as second level validation. The GBT model yielded correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE), equaling 0.95, 3.07 MPa, and 4.80 MPa for training, whereas, for validation, these values were recorded as 0.95, 3.16 MPa, and 4.85 MPa, respectively. The sensitivity analysis revealed that the aging of the concrete was the most influential parameter, followed by the addition of GGBFS. The effect of the contributing parameters was observed, as corroborated in the literature.

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Section: Resultssupporting

confidence: 76%

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

et al. 2022

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“…The root means the square error is determined by the following formula [19]- [22]. The RMSE is employed due to its wider applicability in regression model evaluation [23] where: Σ is the sum of the difference between t h e predicted and observed values for the i th observation in the dataset, Oi is the observed value for the i th observation in the dataset and N is the sample size. The regression models are trained on 7 input values of concrete strength composition parameters such as water, cement, coarse and fine aggregate, and other features.…”

Section: Performance Measuresmentioning

confidence: 99%

Estimation of concrete compression using regression models

Assegie¹,

Salau

Badrudeen

2022

Bulletin EEI

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The objective of this study is to evaluate the effectiveness of different regression models in concrete compressive strength estimation. A concrete compressive strength dataset is employed for the estimation of the regressor models. Regression models such as linear regressor, ridge regressor, k-neighbors regressor, decision tree regressor, random forest regressor, gradient boosting regressor, AdaBoost regressor, and support vector regressor are used for developing the model that predicts the concrete strength. Cross-validation techniques and grid search are used to tune the parameters for better model performance. Python 3.8 programming language is used to conduct the experiment. The Performance evaluation result reveals that the gradient boosting regressor has better performance as compared to other models using root mean square error (RMSE).

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“…The previous researchers recommended the use of a variety of AI models for solving engineering problems [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Modern engineering values numerical [ 42 , 43 , 44 ] and artificial intelligence (AI) models for solving complex and nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models

et al. 2022

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An accurate calculation of the flexural capacity of flexural members is vital for the safe and economical design of FRP reinforced structures. The existing empirical models are not accurately calculating the flexural capacity of beams and columns. This study investigated the estimation of the flexural capacity of beams using non-linear capabilities of two Artificial Intelligence (AI) models, namely Artificial neural network (ANN) and Random Forest (RF) Regression. The models were trained using optimized hyperparameters obtained from the trial-and-error method. The coefficient of correlation (R), Mean Absolute Error, and Root Mean Square Error (RMSE) were observed as 0.99, 5.67 kN-m, and 7.37 kN-m, for ANN, while 0.97, 7.63 kN-m, and 8.02 kN-m for RF regression model, respectively. Both models showed close agreement between experimental and predicted results; however, the ANN model showed superior accuracy and flexural strength performance. The parametric and sensitivity analysis of the ANN models showed that an increase in bottom reinforcement, width and depth of the beam, and increase in compressive strength increased the bending moment capacity of the beam, which shows the predictions by the model are corroborated with the literature. The sensitivity analysis showed that variation in bottom flexural reinforcement is the most influential parameter in yielding flexural capacity, followed by the overall depth and width of the beam. The change in elastic modulus and ultimate strength of FRP manifested the least importance in contributing flexural capacity.

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Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach

Cited by 45 publications

References 87 publications

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

Estimation of concrete compression using regression models

Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models

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