Abstract
Our ability to predict the deterioration of concrete structures would be greatly increased if better use could be made of the large amount of data generated by condition surveys and long-term natural exposure trials. However, this information generally involves a large number of variables and other features rendering it very difficult to utilise using conventional methods of data analysis. Neural networks (NN) would seem to offer a way forward.
The potential benefits of NN are presented and areas where NN may be applicable are discussed. As an example, the development of a NN to predict carbonation depth is outlined and applications of the NN are discussed. This NN is based on around 6600 carbonation depth measurements from 68 papers and takes into account 18 concrete variables and 11 variables relating to the exposure conditions.
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References
7.1 General
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7.2 Sources of Testing Facts
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© 1996 Springer Science+Business Media Dordrecht
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Buenfeld, N.R., Hassanein, N.M. (1996). Neural Networks for Predicting the Deterioration of Concrete Structures. In: Jennings, H., Kropp, J., Scrivener, K. (eds) The Modelling of Microstructure and its Potential for Studying Transport Properties and Durability. NATO ASI Series, vol 304. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8646-7_22
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DOI: https://doi.org/10.1007/978-94-015-8646-7_22
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