Characterisation and propagation of spatial fields in deterioration models: application to concrete carbonation

Abstract

Characterising spatial variability, which is of utter importance in inspection and maintenance strategies, requires comprehensive spatially distributed databases. However, in real practice, spatially distributed inspection is costly and could damage the structure if a large number of destructive tests are carried out. Therefore, the first objective of this work is to propose a methodology to extract as much informations as possible from available spatially distributed databases, in order to characterise the spatial correlation. Moreover, a preventive maintenance strategy should be supported by deterioration models able to propagate uncertainty and spatial variability. Then, the second objective of the paper is to evaluate the ability of these models to propagate uncertainties and spatial variability. The methodology is illustrated with data collected through destructive tests in a concrete wall exposed to carbonation. The database encompasses information about the concrete porosity, saturation degree, density, and carbonation depth. Recommendations are hence provided in this work for the choice of input parameters that should be modelled as random fields. These recommendations were applied and then confirmed by comparing measured and modelled spatially distributed carbonation depths. The results highlight that uncertainties in measurements and statistical uncertainties have significant impact when dealing with spatial variability.

KEYWORDS:

• Reinforced concrete
• modelling
• carbonation
• spatial variability
• random field
• uncertainty quantification

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was made possible thanks to the French Research Agency ANR ‘Building and Sustainable Cities’, which funded the research project ANR-EVADEOS. We are grateful to CEA-Saclay for allowing its installations to be used. Partners of the ANR-EVADEOS project are warmly thanked for the data that have been acquired and shared out (CEA Saclay, IFSTTAR Nantes, LMA Univ. Aix-en-Provence, I2M Univ. Bordeaux, EDF Chatou, LMDC Univ. Toulouse, GeM Univ. Nantes). We also acknowledge the useful comments of the anonymous reviewers to improve the final version of the paper.

Note

¹Non-destructive evaluation of the structures for damage prediction and optimisation of the follow-up. Website: http://www-lmdc.insa-toulouse.fr/evadeos/accueilevadeos.htm