Service life prediction of fly ash concrete using an artificial neural network

被引:0
作者
Yasmina Kellouche
Mohamed Ghrici
Bakhta Boukhatem
机构
[1] Hassiba Benbouali University of Chlef,Geomaterials Laboratory
[2] University of Sherbrooke,Department of Civil Engineering
来源
Frontiers of Structural and Civil Engineering | 2021年 / 15卷
关键词
concrete; fly ash; carbonation; neural networks; experimental validation; service life;
D O I
暂无
中图分类号
学科分类号
摘要
Carbonation is one of the most aggressive phenomena affecting reinforced concrete structures and causing their degradation over time. Once reinforcement is altered by carbonation, the structure will no longer fulfill service requirements. For this purpose, the present work estimates the lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificial neural network technique. A collection of 300 data points was made from experimental results available in the published literature. Backpropagation training of a three-layer perceptron was selected for the calculation of weights and biases of the network to reach the desired performance. Six parameters affecting carbonation were used as input neurons: binder content, fly ash substitution rate, water/binder ratio, CO2 concentration, relative humidity, and concrete age. Moreover, experimental validation carried out for the developed model shows that the artificial neural network has strong potential as a feasible tool to accurately predict the carbonation depth of fly ash concrete. Finally, a mathematical formula is proposed that can be used to successfully estimate the service life of fly ash concrete.
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页码:793 / 805
页数:12
相关论文
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