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Assessment of Concrete Strength Using the Combination of NDT—Review and Performance Analysis

Article dans une revue avec comité de lecture
Author
ccKOUDDANE, Bouchra
1002421 Institut de Mécanique et d'Ingénierie [I2M]
SBARTAÏ, Zoubir Mehdi
1002421 Institut de Mécanique et d'Ingénierie [I2M]
ccALWASH, Maitham
ccALI-BENYAHIA, Khoudja
ccELACHACHI, Sidi Mohammed
259761 Université de Bordeaux [UB]
1002421 Institut de Mécanique et d'Ingénierie [I2M]
LAMDOUAR, Nouzha
325799 Ecole Mohammadia d'Ingénieurs [EMI]
KENAI, Said
302904 Université Saâd Dahlab Blida 1 [UB1]
501575 Faculté De Technologie Blida1

URI
http://hdl.handle.net/10985/23070
DOI
10.3390/app122312190
Date
2022-11-28
Journal
Applied Sciences

Abstract

This paper presents a review on combining NDT techniques, such as rebound hammer and ultrasonic pulse velocity, for assessing concrete compressive strength. These methods, though being favorably not invasive and easy to be extended to a larger number of elements, are affected by many contingency factors. The SonReb technique suggests combining the two methods to partially offset their low reliability if considered separately. For years, this concept was introduced in order to improve the evaluation compared with the use of one NDT. In order to combine the ultrasonic pulse velocity and rebound hammer, many empirical, multiparametric models were proposed in the literature as linear, power, exponential, or polynomial. However, the variety of these models emphasizes that they can give a correct strength prediction only for the particular cases that they are derived for. Therefore, to assess concrete on site, the strength should be predicted using a calibration procedure due to the variability of existing concrete mixes. This paper presents a brief outline of the key aspects of strength assessment, including the different approaches used to build the SonReb model and a calibration procedure for assessing concrete strength. A comparison study between the different approaches is proposed, and a performance analysis using Monte Carlo simulations is discussed. Finally, the estimation capacity of the existing model identification approaches is investigated, and the effect of the “trade-off” is analyzed for different random sampling with varying the number of cores.

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