Journal
ULTRASONICS
Volume 49, Issue 2, Pages 231-237Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ultras.2008.08.006
Keywords
Ultrasonic signal processing; Material characterization; NDT; Neural network
Funding
- Spanish Ministry of Education and Science [BIA 2006-15188-C03-01]
- Spanish Ministry of Public Works [C14/2006]
- Mexican National Council for Science and Technology CONACYT [186384]
- European Social Fund
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The quality and degradation state of building materials can be determined by nondestructive testing (NDT). These materials are composed of a cementitious matrix and particles or fragments of aggregates. Sand/cement ratio (s/c) provides the final material quality; however, the sand content can mask the matrix properties in a nondestructive measurement. Therefore, s/c ratio estimation is needed in nondestructive characterization of cementitious materials. In this study, a methodology to classify the sand content in mortar is presented. The methodology is based on ultrasonic transmission inspection, data reduction, and features extraction by principal components analysis (PCA), and neural network classification. This evaluation is carried out with several mortar samples, which were made while taking into account different cement types and s/c ratios. The estimated s/c ratio is determined by ultrasonic spectral attenuation with three different broadband transducers (0.5, 1, and 2 MHz). Statistical PCA to reduce the dimension of the captured traces has been applied. Feed-forward neural networks (NNs) are trained using principal components (PCs) and their outputs are used to display the estimated s/c ratios in false color images, showing the s/c ratio distribution of the mortar samples. (C) 2008 Elsevier B.V. All rights reserved.
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