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

Abstract

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.

Graphical abstract

Keywords

concrete / fly ash / carbonation / neural networks / experimental validation / service life

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Acknowledgements

This research was sponsored by the General Directorate for Scientific Research and Technological Development (DGRSDT) of the Algerian Minister of Higher Education and Scientific Research. The authors would like to thank the entire team of the alternative materials laboratory at Sherbrook University, especially Dr. Arezki Taghnit-Hamou, who welcomed us to his laboratory, and Dr. Ablam Zidol, for his help in carrying out the experimental program.

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