Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks
Highlights ► The concrete uses aggregates from construction and demolition waste. ► The ANN was used to construct an equation for predicting the compressive strength. ► The compressive strength is predicted at 3, 7, 28 and 91days. ► The results show the potential of using ANN for predicting the compressive strength.
Abstract In this study Artificial Neural Networks (ANNs) models were developed for predicting the compressive strength, at the age of 3, 7, 28 and 91days, of concretes containing Construction and Demolition Waste (CDW). The experimental results used to construct the models were gathered from literature. A total of 1178 data was used for modeling ANN, 77.76% in the training phase, and 22.24% in the testing phase. To construct the model, 17 input parameters were used to achieve one output parameter, referred to as the compressive strength of concrete containing CDW. The results obtained in both, the training and testing phases strongly show the potential use of ANN to predict 3, 7, 28 and 91days compressive strength of concretes containing CDW.
Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks
Highlights ► The concrete uses aggregates from construction and demolition waste. ► The ANN was used to construct an equation for predicting the compressive strength. ► The compressive strength is predicted at 3, 7, 28 and 91days. ► The results show the potential of using ANN for predicting the compressive strength.
Abstract In this study Artificial Neural Networks (ANNs) models were developed for predicting the compressive strength, at the age of 3, 7, 28 and 91days, of concretes containing Construction and Demolition Waste (CDW). The experimental results used to construct the models were gathered from literature. A total of 1178 data was used for modeling ANN, 77.76% in the training phase, and 22.24% in the testing phase. To construct the model, 17 input parameters were used to achieve one output parameter, referred to as the compressive strength of concrete containing CDW. The results obtained in both, the training and testing phases strongly show the potential use of ANN to predict 3, 7, 28 and 91days compressive strength of concretes containing CDW.
Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks
Dantas, Adriana Trocoli Abdon (Autor:in) / Batista Leite, Mônica (Autor:in) / de Jesus Nagahama, Koji (Autor:in)
Construction and Building Materials ; 38 ; 717-722
23.09.2012
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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