| 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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Fakharian, Pouyan
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2023Experimental Study on Mechanical Properties and Durability of Polymer Silica Fume Concrete with Vinyl Ester Resincitations
- 2022Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP
- 2021Shear Capacity Prediction of FRP Reinforced Concrete Beams Using Hybrid GMDH–GA
- 2021Mechanical properties of roller-compacted concrete pavement containing recycled brick aggregates and silica fumecitations
- 2020Application of Artificial Intelligence Methods to Estimate Shear Strength of Reinforced Concrete Shear Wall
- 2019Innovative Models for Prediction of Compressive Strength of FRP-Confined Circular Reinforced Concrete Columns Using Soft Computing Methodscitations
- 2018Compressive strength prediction of environmentally friendly concrete using artificial neural networkscitations
Places of action
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article
Compressive strength prediction of environmentally friendly concrete using artificial neural networks
Abstract
Solid waste in the form of construction debris is one of the major environmental concerns in the world. Over 20 million tons of construction waste materials are generated in Tehran each year. A large amount of these materials can be recycled and reused as recycled aggregate concrete (RAC) for general construction, pavement and a growing number of other works that drive the demand for RAC. This paper aims to predict RAC compressive strength by using Artificial Neural Network (ANN). The training and testing data for ANN model development were prepared using 139 existing sets of data derived from 14 published literature sources. The developed ANN model uses six input features namely water cement ratio, water absorption, fine aggregate, natural coarse aggregate, recycled coarse aggregate, water-total material ratio. The ANN is modelled in MATLAB and applied to predict the compressive strength of RAC given the foregoing input features. The results indicate that the ANN is an efficient model to be used as a tool in order to predict the compressive strength of RAC which is comprised of different types and sources of recycled aggregates.