Technical Papers
Jul 5, 2024

Modeling of Chloride Spatial Variability in a Reinforced Concrete Wharf from Onsite Measurements

Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 3

Abstract

Chloride ingress by diffusion is the major deterioration process of reinforced concrete (RC) structures exposed to the marine environment. These structures have significant lengths or surfaces exposed to the outside environment. Due to the material variability (different concrete batches and vibration) and exposure variability, the material experiences a spatial variability of the deterioration process. This paper presents the geostatistical analysis of in situ chloride profiles, leading to the assessment of the spatial variability (SV) of both the chloride ingress itself and the parameters of the widely used Fick’s diffusion law (the average surface chloride content, Csa, and the average chloride diffusion coefficient, Da). 37 chloride profiles measured on both sides of the same spandrel beam of a RC wharf were studied, as well as the associated estimates of Csa and Da. From an initial selection of random field models, the geostatistical analysis consists in the evaluation of model parameters using a procedure that tests both data and model assumptions on the fly (ergodicity, stationarity, and random field modeling). Combined with the calculation of information criteria for each model, this procedure allows to provide relevant geostatistical models for chloride ingress, Csa and Da, which render SV as well as measurement error. It is noteworthy that the estimation error can be neglected when focusing on the SV for the range of chloride content studied in this paper. The SV of the chloride content seems to depend on the depth, with a large variability within the convection zone, and much less variability and more stability in the diffusion zone with a practical range of about 70 cm. This order of magnitude is consistent with the range of SV calculated for Csa (50–73 cm).

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Data Availability Statement

Some or all data, models, or code generated or used during the study are available from the corresponding author by request, including the chloride content data set and whole MATLAB code generated during the study.

Acknowledgments

The authors thank all the partners of the iMAREO2 project: Keops Automation (D. Follut and D. Olivier), Université de Nantes (M. Roche), and Nantes–Saint-Nazaire Port (P. Lijour and M. Labegorre). The authors thank the Pays de la Loire Region for its financial support.

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Go to ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10Issue 3September 2024

History

Received: Aug 22, 2023
Accepted: Jan 20, 2024
Published online: Jul 5, 2024
Published in print: Sep 1, 2024
Discussion open until: Dec 5, 2024

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Associate Professor, Nantes Université, École Centrale Nantes, CNRS, GeM, UMR 6183, Nantes F-44000, France. ORCID: https://orcid.org/0000-0003-3676-0995
Professor, Nantes Université, École Centrale Nantes, CNRS, GeM, UMR 6183, Nantes F-44000, France (corresponding author). ORCID: https://orcid.org/0000-0002-7559-6182. Email: [email protected]
Mestapha Oumouni
Researcher, Nantes Université, École Centrale Nantes, CNRS, GeM, UMR 6183, Nantes F-44000, France.
Inès Othmen
Researcher, Nantes Université, École Centrale Nantes, CNRS, GeM, UMR 6183, Saint-Nazaire F-44600, France.
Stéphanie Bonnet
Professor, Nantes Université, École Centrale Nantes, CNRS, GeM, UMR 6183, Saint-Nazaire F-44600, France.

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