Concrete properties evaluation by statistical fusion of NDT techniques

• DOI: 10.1016/j.conbuildmat.2012.09.064
• Publication Date: 2012-11-12T21:55:44Z
• Authors: Zoubir Mehdi Sbartaï; Stéphane Laurens; Sidi Mohammed Ela...; Cédric Payan
• Keywords: Non-destructive testing; RSM; 02 engineering and technology; [SPI.MECA]Engineering Sciences [physics]/Mechanics [physics.med-ph]; [SPI.MECA] Engineering Sciences [physics]/Mechanics [physics.med-ph]; RADAR; 543; Concrete durability evaluation; 0201 civil engineering; NONDESTRUCTIVE EVALUATION; STRENGTH; NEURAL-NETWORKS; Fusion; ANN

Abstract Measurements from Non-Destructive Testing (NDT) techniques are affected in different ways by concrete properties such as porosity, complexity of the pore network, water content, strength, etc. Therefore, extracting one concrete property from one NDT measurement appears to result in uncertainties. This highlights the benefit of NDT data fusion to evaluate accurately concrete properties. In this paper, NDT measurements from GPR, electrical resistivity and ultrasonic pulse velocity were combined to predict more accurately concrete properties such as strength and water content. Two techniques of data fusion were used namely Response Surface Method (RSM) and artificial neural networks (ANNs). The results obtained show the effectiveness of the statistical modeling to predict the properties of concretes by fusion of NDT measurements. In the context of this study, the performances of the two techniques of fusion appear relevant in terms of water content and concrete strength prediction. ANN models exhibit better predictive ability than RSM ones.