ESTIMATION OF SPATIAL DISTRIBUTION OF MASS LOSS RATIO OF THE REBAR BASED ON CORROSION CRACK WIDTH OF RC STRUCTURE BY GAUSSIAN PROCESS REGRESSION

• Published in: Artificial Intelligence and Data Science Vol. 3; no. J2; pp. 389 - 397
• Main Authors: YOSHIDA, Ikumasa, TOMIZAWA, Yukihisa, SUZUKI, Shuuichi, NAKAMURA, Tomoka
• Format: Journal Article
• Language: Japanese
• Published: Japan Society of Civil Engineers 2022; 公益社団法人 土木学会
• Subjects: chloride induced deterioration; Gaussian process regression; mass loss ratio of the rebar; spatial distribution estimation
• ISSN: 2435-9262
• DOI: 10.11532/jsceiii.3.J2_389

The width of corrosive cracks caused by chloride induced deterioration in RC structures can be easily and massively measured by image processing technologies recently. If the spatial distribution of the corrosion of the internal reinforcing bars can be accurately estimated by these information, it would be useful not only for evaluating the current status of the structure but also for predicting the future of deterioration. In this study, we propose a methodology to estimate the spatial distribution of mass loss ratio of the rebar using Gaussian process regression, which is a probabilistic regression method, based on the corrosive crack width. In the estimation by Gaussian process regression, the model of cross-correlation is important, and the parameters of the model are estimated by the maximum likelihood method. In order to verify the proposed method, the estimated spatial distribution of mass loss ratio of the rebar is compared with the true one obatained by laboratory experiments. It is shown that the estimated distribution is generally consistent with the true one.