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2021 | OriginalPaper | Chapter

11. Model Identification and Calibration

Authors : Zoubir Mehdi Sbartaï, Maitham Alwash, Denys Breysse, Arlindo Gonçalves, Michael Grantham, Xavier Romão, Jean-Paul Balayssac

Published in: Non-Destructive In Situ Strength Assessment of Concrete

Publisher: Springer International Publishing

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Abstract

This chapter provides additional information about the “identification of conversion model” step and the “strength estimation” step as defined in the flowchart summarizing the RILEM recommendation. The advantages and limits of the various options are illustrated by analyzing the results of synthetic simulations. Based on the developed synthetic database, a comparison of the performance of different possible univariate conversion models identified and bi-objective method using linear regression is presented and discussed. It is shown here that the issue of the “best conversion model” is secondary, and that the choice of the conversion model has only negligible effects on the final uncertainty of the final strength. In fact, prediction error is the only way to address correctly. It is also shown why the bi-objective approach must be privileged as it provides, without any additional cost, a better estimation of concrete variability, without reducing the performance regarding the assessment of mean strength and local strength.

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Footnotes
1
This is the general expression. As the conversion model for strength estimations is concerned, n corresponds to the number of cores Nc used for calibrating the model.
 
2
Expert E3a is an additional case that is considered in order to point at the crucial issue of correctly checking the real TRP value of NDT results. This Expert has in fact the same TRP than Expert E3, i.e. medium precision test results (TRP2), but he wrongly assumes that he has TRP1, and thus takes a lower number of cores.
 
3
It should be remembered that these results have been obtained thanks to simulations and that, in real practice they can only be estimated by the “leave-one-out procedure” (see §1.5.6.4 for theory and §8.3.9 for practical examples).
 
4
This conclusion applies to most cases. Using a non-linear model (exponential or power) may become interesting only when the pair datasets exhibit a visibly strong non-linear tendency. This requires the range of variation of test results to be very wide (i.e.in a highly contrasted dataset that mixes results from test locations with very bad properties with others that have good properties).
 
Literature
1.
go back to reference Breysse, D., Fernández-Martínez, J.L.: Assessing concrete strength with rebound hammer: review of key issues and ideas for more reliable conclusions. Mater. Struct. 47, 1589–1604 (2014)CrossRef Breysse, D., Fernández-Martínez, J.L.: Assessing concrete strength with rebound hammer: review of key issues and ideas for more reliable conclusions. Mater. Struct. 47, 1589–1604 (2014)CrossRef
2.
go back to reference Fernandez-Martinez, J.L., Fernandez-Muniz, Z., Breysse, D.: Uncertainty analysis in linear and nonlinear regression revisited: application to concrete strength estimation. Inverse. Probl. Sci. Eng. 27(12), 1740–1764 (2019)MathSciNetCrossRef Fernandez-Martinez, J.L., Fernandez-Muniz, Z., Breysse, D.: Uncertainty analysis in linear and nonlinear regression revisited: application to concrete strength estimation. Inverse. Probl. Sci. Eng. 27(12), 1740–1764 (2019)MathSciNetCrossRef
4.
go back to reference Breysse, D., Villain, G., Sbartaï, Z.M., Garnier, V.: Construction of conversion models of observables into indicators. In: J.-P. Balayssac, V. Garnier (eds.), Non-Destructive Testing and Evaluation of Civil Engineering Structures (ISTE Press, Elsevier, 2018), Chapter 7, pp. 231–257 Breysse, D., Villain, G., Sbartaï, Z.M., Garnier, V.: Construction of conversion models of observables into indicators. In: J.-P. Balayssac, V. Garnier (eds.), Non-Destructive Testing and Evaluation of Civil Engineering Structures (ISTE Press, Elsevier, 2018), Chapter 7, pp. 231–257
Metadata
Title
Model Identification and Calibration
Authors
Zoubir Mehdi Sbartaï
Maitham Alwash
Denys Breysse
Arlindo Gonçalves
Michael Grantham
Xavier Romão
Jean-Paul Balayssac
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-64900-5_11