| People | Locations | Statistics |
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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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Amar, Mouhamadou
IMT Nord Europe
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2024A novel approach based on microstructural modeling and a multi-scale model to predicting the mechanical-elastic properties of cement pastecitations
- 2022The Use of Callovo-Oxfordian Argillite as a Raw Material for Portland Cement Clinker Productioncitations
- 2022Flash calcined sediment used in the CEM III cement production and the potential production of hydraulic binder for the road construction – Part I: Characterization of CEM III cements
- 2022Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Networkcitations
- 2022Effect of flash-calcined sediment substitution in sulfoaluminate cement mortarcitations
- 2022The Pozzolanic Activity of Sediments Treated by the Flash Calcination Methodcitations
- 2022High performance mortar using flash calcined materials
- 2022Designing Efficient Flash-Calcined Sediment-Based Ecobinderscitations
- 2021From dredged sediment to supplementary cementitious material: characterization, treatment, and reusecitations
- 2018Durability of a cementitious matrix based on treated sedimentscitations
Places of action
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article
Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network
Abstract
International audience ; In the 21st century, numerous numerical calculation techniques have been discovered and used in several fields of science and technology. The purpose of this study was to use an artificial neural network (ANN) to forecast the compressive strength of waste-based concretes. The specimens studied include different kinds of mineral additions: metakaolin, silica fume, fly ash, limestone filler, marble waste, recycled aggregates, and ground granulated blast furnace slag. This method is based on the experimental results available for 1303 different mixtures gathered from 22 bibliographic sources for the ANN learning process. Based on a multilayer feedforward neural network model, the data were arranged and prepared to train and test the model. The model consists of 18 inputs following the type of cement, water content, water to binder ratio, replacement ratio, the quantity of superplasticizer, etc. The ANN model was built and applied with MATLAB software using the neural network module. According to the results forecast by the proposed neural network model, the ANN shows a strong capacity for predicting the compressive strength of concrete and is particularly precise with satisfactory accuracy (R² = 0.9888, MAPE = 2.87%).