Assessment of Scaling Durability of Concrete with CFBC Ash by Automatic Classification Rules
Publication: Journal of Materials in Civil Engineering
Volume 24, Issue 7
Abstract
The objective of this investigation was to develop rules for automatic assessment of concrete quality by using selected artificial intelligence methods based on machine learning. The range of tested materials included concrete containing nonstandard waste material—the solid residue from coal combustion in circulating fluidized bed combustion boilers (CFBC ash) used as an additive. Performed experimental tests on the surface scaling resistance provided data for learning and verification of rules discovered by machine learning techniques. It has been found that machine learning is a tool that can be applied to classify concrete durability. The rules generated by computer programs AQ21 and WEKA by using the J48 algorithm provided a means for adequate categorization of plain concrete and concrete modified with CFBC fly ash as materials resistant or not resistant to the surface scaling.
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Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 24 • Issue 7 • July 2012
Pages: 860 - 867
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Jun 27, 2011
Accepted: Dec 20, 2011
Published online: Jun 15, 2012
Published in print: Jul 1, 2012
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