To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models

de‐Prado‐Gil, Jesús; Palencia, Covadonga; Silva-Monteiro, Neemias; Martínez‐García, Rebeca

doi:10.1016/j.cscm.2022.e01046

Cited by 49 publications

(31 citation statements)

References 82 publications

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“…With the building sector’s rapid growth, the demolition rates are growing daily, necessitating the effective reuse of DCW [ 12 , 13 , 14 ]. Fine aggregates (sand) and coarse aggregates (stone) make up most of the concrete, accounting for roughly 75% of the overall volume [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

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A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).

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Section: Introductionmentioning

confidence: 99%

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

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“…In their study, de-Prado-Gil et al dealt with the application of the ensemble methods: random forest (RF), K-nearest neighbor (KNN), extremely randomized trees (ERT), extreme gradient boosting (XGB), gradient boosting (GB), light gradient boosting machine (LGBM), category boosting (CB) and the generalized additive models (GAMs), and for the development of the models, 515 samples were collected. The results indicated that the RF models have a strong potential to predict the CS of SCC with recycled aggregates [19]. LGBM, CB, GAM 515 2022 de-Prado-Gil et al [19] The novelty of this research is the use of a significant number of state-of-the-art ML methods trained on a significant set of experimental data.…”

Section: Introductionmentioning

confidence: 97%

“…The results indicated that the RF models have a strong potential to predict the CS of SCC with recycled aggregates [19]. LGBM, CB, GAM 515 2022 de-Prado-Gil et al [19] The novelty of this research is the use of a significant number of state-of-the-art ML methods trained on a significant set of experimental data. This paper is also important as it defines the optimal model for predicting the CS of SCC of different sample ages.…”

Section: Introductionmentioning

confidence: 97%

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash

Kovačević¹,

Lozančić

Nyarko

et al. 2022

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Replacing a specified quantity of cement with Class F fly ash contributes to sustainable development and reducing the greenhouse effect. In order to use Class F fly ash in self-compacting concrete (SCC), a prediction model that will give a satisfactory accuracy value for the compressive strength of such concrete is required. This paper considers a number of machine learning models created on a dataset of 327 experimentally tested samples in order to create an optimal predictive model. The set of input variables for all models consists of seven input variables, among which six are constituent components of SCC, and the seventh model variable represents the age of the sample. Models based on regression trees (RTs), Gaussian process regression (GPR), support vector regression (SVR) and artificial neural networks (ANNs) are considered. The accuracy of individual models and ensemble models are analyzed. The research shows that the model with the highest accuracy is an ensemble of ANNs. This accuracy expressed through the mean absolute error (MAE) and correlation coefficient (R) criteria is 4.37 MPa and 0.96, respectively. This paper also compares the accuracy of individual prediction models and determines their accuracy. Compared to theindividual ANN model, the more transparent multi-gene genetic programming (MGPP) model and the individual regression tree (RT) model have comparable or better prediction accuracy. The accuracy of the MGGP and RT models expressed through the MAE and R criteria is 5.70 MPa and 0.93, and 6.64 MPa and 0.89, respectively.

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“…Considering this, a trend has gained a surge in recent years by using ML techniques to anticipate the CS of concrete material [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. These methods can be utilized for a number of applications, including regression, classification, correlation, and clustering [ 36 , 37 , 38 , 39 , 40 ]. With the advancement of ML approaches, it is consequently uncomplicated to investigate the CS of SCC along with the concrete’s other properties [ 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete

et al. 2022

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This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles. The data set was based on 11 input parameters, such as cement, limestone, fly ash, ground granulated blast-furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, superplasticizers, water, viscosity modifying admixtures, and one output with compressive strength of SCC. In terms of properly predicting the CS of SCC, the BR technique outperformed both the SVM and MLP models, as determined by the research results. In contrast to SVM and MLP, the coefficient of determination (R2) for the BR model was 0.95, whereas for SVM and MLP, the R2 was 0.90 and 0.86, respectively. In addition, a k-fold cross-validation approach was adopted to check the accuracy of the employed models. The statistical measures mean absolute percent error, mean absolute error, and root mean square error ensure the validity of the model. Using sensitivity analysis, the influence of input factors on the intended CS of SCC was also explored. This analysis reveals that the highest contributing parameter towards the CS of SCC was cement with 16.2%, while rice husk ash contributed the least with 4.25% among all the input variables.

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To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models

Cited by 49 publications

References 82 publications

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash

Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete

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