A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Compressive Strength Prediction of Concrete Recycled Aggregates made from Ceramic Tiles using Feedforward Artificial Neural Network (FANN)
In this paper, Feed forward Artificial Neural Network (FANN) model has been used to predict concrete compressive strengths made from ceramics tiles. Multiple regression analysis (MRA) was used to compare the results obtained from FANN. Both models are trained and tested using the available test data of 72 different concrete mix using recycled aggregates derived from homogenous ceramic tiles. The data are arranged in a format of inputs parameters for fine and coarse aggregates that cover the percentage of replacement, water/cement ratio, compacting factor, curing time of 7 and 14 days respectively. The output considered was the compressive strength for curing time of 28 days of the recycle concrete. The r2 value for fine aggregates was 80% for MRA and 88 % for FANN respectively. In coarse aggregates, the r2 value was 64 % for MRA and 74 % for FANN respectively. The results showed that FANN is a suitable tool for predicting compressive strength values for different concrete mixtures.
Compressive Strength Prediction of Concrete Recycled Aggregates made from Ceramic Tiles using Feedforward Artificial Neural Network (FANN)
In this paper, Feed forward Artificial Neural Network (FANN) model has been used to predict concrete compressive strengths made from ceramics tiles. Multiple regression analysis (MRA) was used to compare the results obtained from FANN. Both models are trained and tested using the available test data of 72 different concrete mix using recycled aggregates derived from homogenous ceramic tiles. The data are arranged in a format of inputs parameters for fine and coarse aggregates that cover the percentage of replacement, water/cement ratio, compacting factor, curing time of 7 and 14 days respectively. The output considered was the compressive strength for curing time of 28 days of the recycle concrete. The r2 value for fine aggregates was 80% for MRA and 88 % for FANN respectively. In coarse aggregates, the r2 value was 64 % for MRA and 74 % for FANN respectively. The results showed that FANN is a suitable tool for predicting compressive strength values for different concrete mixtures.
Compressive Strength Prediction of Concrete Recycled Aggregates made from Ceramic Tiles using Feedforward Artificial Neural Network (FANN)
Kin, Chan-Chee (author) / Don, Mashitah Mat (author) / Ahmad, Z. (author)
2012
5 Seiten, 6 Quellen
Conference paper
English
Recycled Aggregates Concrete Compressive Strength Prediction Using Artificial Neural Networks (ANNs)
| DOAJ | 2021
Prediction of compressive strength of recycled aggregate concrete using artificial neural networks
| British Library Online Contents | 2013