Concrete properties evaluation by statistical fusion of NDT techniques

• Published in: Construction & building materials Vol. 37; pp. 943 - 950
• Main Authors: Sbartaï, Zoubir Mehdi, Laurens, Stéphane, Elachachi, Sidi Mohammed, Payan, Cédric
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2012; Elsevier B.V; Elsevier
• Subjects: Analysis; ANN; Concrete; Concrete durability evaluation; Electric properties; Engineering Sciences; Fusion; Mechanical properties; Mechanics; Methods; Neural networks; Non-destructive testing; Porosity; RSM; United States; ANN; Concrete durability evaluation; Non-destructive testing; Fusion; RSM; NEURAL-NETWORKS; RADAR; NONDESTRUCTIVE EVALUATION; STRENGTH
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2012.09.064

► Fusion of several NDT features is relevant for improving the evaluation of concrete. ► RSM have the advantage of being simple and the models are rapidly adjusted. ► ANN has more interesting results in term of predictive capacity on new data. 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.