Study on the Probability Distribution of Pitting for Naturally Corroded Prestressing Strands Accounting for Surface Defects
Autor(en): |
Lorenzo Franceschini
Beatrice Belletti Francesco Tondolo Javier Sánchez |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Buildings, 20 September 2022, n. 10, v. 12 |
Seite(n): | 1732 |
DOI: | 10.3390/buildings12101732 |
Abstrakt: |
One of the most urgent scientific needs from a technical and economic engineering point of view is the assessment of concrete structures suffering corrosion deterioration. However, the pursuit of this target in the case of corroded prestressed concrete (PC) members is hindered by the lack of (i) consolidated simplified formulations to be used in the engineering daily practice and (ii) works investigating the uncertainties in the correlation between the damage induced by corrosion and the structural resistance. To this aim, the present study adopts a 3D-scanning technique for the pitting morphology evaluation of several corroded prestressing strands retrieved from 10-year-old PC beams. First, the probabilistic distributions of penetration depths have been investigated. Second, the pitting factors α and Ωi have been proposed and discussed to quantify the level of corrosion in longitudinal and transversal direction, respectively. Finally, correlations have been derived between the maximum and average penetration depth as a function of the level of corrosion and the surface defects mapping has been carried out on the corroded PC beams. The results show that the penetration depth of strands subjected to chloride-induced corrosion can be best fitted by a lognormal distribution function. Additionally, the simultaneous consideration of longitudinal and transversal pitting factor is found out to be essential for an exhaustive comprehension of pitting corrosion. Moreover, the outcomes highlight that the presence of longitudinal splitting cracks plays a fundamental role in the corrosion spatial variability of prestressing strands. |
Copyright: | © 2022 by the authors; licensee MDPI, Basel, Switzerland. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
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Datenseite - Reference-ID
10699757 - Veröffentlicht am:
11.12.2022 - Geändert am:
15.02.2023