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Bayesian probabilistic assessment of in-situ concrete strength

 Bayesian probabilistic assessment of in-situ concrete strength
Author(s): , ,
Presented at IABSE Symposium: Large Structures and Infrastructures for Environmentally Constrained and Urbanised Areas, Venice, Italy, 22-24 September 2010, published in , pp. 228-229
DOI: 10.2749/222137810796024321
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Core testing is considered the most accurate technique for the assessment of in-situ concrete strength. EN 13791:2007 gives guidance for estimating in-situ compressive strength in existing structur...
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Bibliographic Details

Author(s):


Medium: conference paper
Language(s): English
Conference: IABSE Symposium: Large Structures and Infrastructures for Environmentally Constrained and Urbanised Areas, Venice, Italy, 22-24 September 2010
Published in:
Page(s): 228-229 Total no. of pages: 7
Page(s): 228-229
Total no. of pages: 7
Year: 2010
DOI: 10.2749/222137810796024321
Abstract:

Core testing is considered the most accurate technique for the assessment of in-situ concrete strength. EN 13791:2007 gives guidance for estimating in-situ compressive strength in existing structures and states that core testing is the reference method. However, the number of cores that can be taken from a structure is usually limited, so it may be advantageous to supplement the core tests with some type of indirect test. The standard mentioned above establishes two alternatives for the calibration of indirect tests, both based on core tests results taken from the structure being assessed. One of them requires at least 18 core tests. But if it is available 18 core tests, it is only natural to ask if it is really necessary to supplement those core tests with an indirect test. This question motivates the study here presented. Specifically, this study deals with the determination of the number of cores above which the use of an indirect test, as a supplement to core tests, is no longer attractive.

Keywords:
NDT concrete strength calibration statistical uncertainty core tests indirect tests Bayesian approach