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Time-Dependent Prediction on the Localized Corrosion of Steel Structure Using Spatial Statistical Simulation

The structural failure induced by the localized corrosion of steel members frequently occurred. In this study, a spatial statistical technique was developed to predict the time-dependent localized corrosion initiated at the boundary zone of steel and concrete. 20 specimens were prepared by embedding...

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Bibliographic Details
Published in:International journal of steel structures 2021, 21(3), , pp.987-1003
Main Authors: Kainuma, Shigenobu, Yang, Muye, Xie, Jiajing, Jeong, Young-Soo
Format: Article
Language:English
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Summary:The structural failure induced by the localized corrosion of steel members frequently occurred. In this study, a spatial statistical technique was developed to predict the time-dependent localized corrosion initiated at the boundary zone of steel and concrete. 20 specimens were prepared by embedding steel plate into concrete block, the accelerated corrosion tests of 600, 1200, 1800, and 2400 cycles were conducted to obtain the actual surface figuration with uniform and localized corrosion. After performing the regression tree analysis to divide the corrosion surface into localized and uniformed corrosion regions, a semi-variogram was used as the statistical technique to generate the corrosion prediction model, and an ordinary Kriging model to estimate the probable corrosion depth at arbitrary locations. The simulated results show that the probability distribution of corrosion depth and surface topography of both localized and uniform corrosion surfaces are highly consistent with the test. Also, the estimated surfaces owned a similar stress concentration effect to the actual surface, although their localized corrosion distributes differently. The prediction results of 2400–9600 cycles show that the top five SCF values of simulation almost within the 95% confidence interval of the fitting curves of test results. Therefore, the accuracy of the estimated corrosion surface is considered reasonable using the spatial statistical simulation method.
ISSN:1598-2351
2093-6311
DOI:10.1007/s13296-021-00485-y