Predicting Recycled Aggregates Compressive Strength in High-Performance Concrete Using Artificial Neural Networks

The civil construction industry has a significant challenge in contributing to environmental protection by developing cement and natural aggregates alternatives. Because of global warming caused by the use of cement in concrete, it is essential to provide sustainable alternatives for producing waste...

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Hauptverfasser: Alyaseen, Ahmad, Poddar, Arunava, Almohammed, Fadi, Tajjour, Salwan, Hammadeh, Karam, Alahmad, Hussain
Format: Buchkapitel
Sprache:eng
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Zusammenfassung:The civil construction industry has a significant challenge in contributing to environmental protection by developing cement and natural aggregates alternatives. Because of global warming caused by the use of cement in concrete, it is essential to provide sustainable alternatives for producing waste-containing concrete. For the building's time, cost and dependability, this chapter is being conducted. As a result of the use of artificial neural networks (ANN), the concrete component proportions of several grades of concrete have been corrected to include a different aggregate replacement with recycled one. The k-fold cross-validation method was also used in order to assess the model's overall performance. According to the results, when one hidden layer and seven neurons were employed, the model produced an R-value of 0.95431, while the ANN model produced an R-value of 0.91 when one hidden layer and up to 50 neurons were used.
DOI:10.1201/9781003184331-16