Development of mathematical models/applied AI for estimation of compressive strength of concrete using non-destructive testing methods

The proposed project aims to apply artificial intelligence methods to augment in-place non-destructive testing technologies in order to reduce or eliminate the need for intrusive methods (i.e. concrete core extraction) for concrete strength estimation. The proposed approach is based on the SonReb method, which combines two non-destructive testing technologies, namely ultrasonic pulse velocity and rebound hammer, for assessing subsurface and near-surface concrete properties. The project will involve the development of a rich regional database that will be used to train an artificial neural network, which in turn will be used to develop algorithms/mathematical models that can be programmed into user-friendly software for rapid implementation in the field. It is expected that the project will ultimately result in the development of both a new software tool and a dual-purpose testing device that will result in new revenue streams and expanded market share for the project partner.

Intern: 
Seyed Alireza Alavi;Aws Hasak
Faculty Supervisor: 
Martin Noel
Province: 
Ontario
Partner University: 
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