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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Alyousef, Rayed
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2022Mix design of concretecitations
- 2021Geopolymer concrete as sustainable materialcitations
- 2021Predictive modeling for sustainable high-performance concrete from industrial wastescitations
- 2021Sugarcane bagasse ash-based engineered geopolymer mortar incorporating propylene fiberscitations
- 2020A comparative study on performance evaluation of hybrid GNPs/CNTs in conventional and self-compacting mortarcitations
- 2020New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubescitations
- 2019Effects of Incorporation of Marble Powder Obtained by Recycling Waste Sludge and Limestone Powder on Rheology, Compressive Strength, and Durability of Self-Compacting Concretecitations
- 2018Study of the Effects of Marble Powder Amount on the Self-Compacting Concretes Properties by Microstructure Analysis on Cement-Marble Powder Pastes
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article
Predictive modeling for sustainable high-performance concrete from industrial wastes
Abstract
The cementitious matrix of high-performance concrete (HPC) is highly complex, and ambiguity exists with its mix design. Compressive strength can vary with the composition and proportion of constituent material used. To predict the strength of such a complex matrix the use of robust and efficient machine learning approaches has become indispensable. This study uses machine intelligence algorithms with individual learners and ensemble learners (bagging, boosting) to predict the strength of (HPC) prepared with waste materials. This is done by employing Anaconda (Python). Ensemble learner bagging, adaptive boosting algorithm, and random forest as modified bagging algorithm are employed to construct strong ensemble learner by incorporating weak learner. The ensemble learners are used on individual learners or weak learners including support vector machine and decision tree through regression and multilayer perceptron neural network. The data consists of 1030 data samples in which eight parameters namely cement, water, sand, gravels, superplasticizer, concrete age, fly ash and granulated blast furnace slag were chosen to predict the output. Twenty bagging and boosting sub-models are trained on data and optimization was done to give maximum R<sup>2</sup>. The test data is also validated by means of K-Fold cross-validation using R<sup>2</sup>, MAE, and RMSE. Moreover, evaluation of ensemble models with individual one is also checked by statistical model performance index (e.g., MAE, MSE, RMSE, and RMLSE). The result suggested that the individual model response is enhanced by using the bagging and boosting learners. Overall, random forest and decision tree with bagging give the robust performance of the models with R<sup>2</sup> = 0.92 with the least errors. On average, the ensemble model in machine learning would enhance the performance of the model.