Assessment of the reliability of concrete evaluation by multi-physical inversion of NDT measurements – A probabilistic approach
Inversion
Uncertainty
0211 other engineering and technologies
Probabilistic
02 engineering and technology
624
[SPI.MAT] Engineering Sciences [physics]/Materials
[SPI.MAT]Engineering Sciences [physics]/Materials
543
0201 civil engineering
NDT
Combination
Fusion
Evaluation
Concrete
DOI:
10.1016/j.conbuildmat.2021.124371
Publication Date:
2021-08-09T02:49:53Z
AUTHORS (6)
ABSTRACT
Abstract The evaluation of the spatial variability of concrete properties is an important issue for a better diagnosis of reinforced concrete structures. The combination between destructive techniques and nondestructive techniques (NDT) is a common practice to establish relationships between concrete properties and NDT measurements. Concrete properties can then be estimated at each test location using the corresponding NDT values on the basis of the calibration and inversion of these relationships. However, NDT measurements include many sources of uncertainties that can lead to biased or even inaccurate estimation. Thus, the improvement of the reliability of this estimation requires to specify and control the principal influencing factors on these uncertainties. The main objective of this paper is to propose a calibration methodology of conversion models and to study the reliability of concrete properties assessment considering the effect of the number of measurements, the uncertainty of NDT measurements and the combination of NDT techniques. Three conversion models linking the ultrasonic pulse velocity, the electrical resistivity, and the dielectric permittivity to two physical concrete properties, the porosity and saturation degree, are considered. The results show that the inversion of the proposed analytical models enables an accurate evaluation of concrete properties. In addition, it has been shown that there is a minimal number of measurements needed for an efficient non-destructive evaluation of concrete properties considering their variabilities.
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