A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Neural networks analysis of compressive strength of lightweight concrete after high temperatures
Highlights ANN model gives more accurate results than the multiple regression model. ANN model can be used to predict the compressive strength. The most effective parameter on the compressive strength is the pumice aggregate ratio.
Abstract When concrete, one of the most important structural materials, is exposed to elevated temperatures generally strength loss is observed. Decrease ratio in the compressive strength depends on many materials and experimental factors. An artificial neural network (ANN) approach was used to model the compressive strength of lightweight and semi lightweight concretes with pumice aggregate subjected to high temperatures. Model inputs were the target temperature, pumice aggregate ratio and heating duration and the output was the compressive strength of pumice aggregate concrete. Data on the compressive strength of pumice aggregate concrete after the effects of high temperatures was obtained from a previous experimental study. The predicted values of the ANN are in accordance with the experimental data. The results indicate that the model can predict the compressive strength with adequate accuracy.
Neural networks analysis of compressive strength of lightweight concrete after high temperatures
Highlights ANN model gives more accurate results than the multiple regression model. ANN model can be used to predict the compressive strength. The most effective parameter on the compressive strength is the pumice aggregate ratio.
Abstract When concrete, one of the most important structural materials, is exposed to elevated temperatures generally strength loss is observed. Decrease ratio in the compressive strength depends on many materials and experimental factors. An artificial neural network (ANN) approach was used to model the compressive strength of lightweight and semi lightweight concretes with pumice aggregate subjected to high temperatures. Model inputs were the target temperature, pumice aggregate ratio and heating duration and the output was the compressive strength of pumice aggregate concrete. Data on the compressive strength of pumice aggregate concrete after the effects of high temperatures was obtained from a previous experimental study. The predicted values of the ANN are in accordance with the experimental data. The results indicate that the model can predict the compressive strength with adequate accuracy.
Neural networks analysis of compressive strength of lightweight concrete after high temperatures
Bingöl, A. Ferhat (author) / Tortum, Ahmet (author) / Gül, Rüstem (author)
2013-05-09
7 pages
Article (Journal)
Electronic Resource
English
Neural networks analysis of compressive strength of lightweight concrete after high temperatures
| British Library Online Contents | 2013
Neural networks analysis of compressive strength of lightweight concrete after high temperatures
| British Library Online Contents | 2013
Neural networks analysis of compressive strength of lightweight concrete after high temperatures
| British Library Online Contents | 2013
Neural networks analysis of compressive strength of lightweight concrete after high temperatures
| British Library Online Contents | 2013
Residual Compressive Strength of Lightweight Foamed Concrete after Exposure to High Temperatures
| Trans Tech Publications | 2015