Artificial Intelligence and Data Science
Online ISSN : 2435-9262
DATABASE IMPROVEMENT FOR MACHINE LEARNING AND APPLICATION TO STRUCTURAL CAPACITY ESTIMATION OF DETERIORATED RC MEMBERS USING OBSERVATIONAL CORROSION CRACK DISTRIBUTIONS
Satoru NAKAMURATaiki YAMADAMina SHINTANISupasit SRIVARANUNMitsuyoshi AKIYAMA
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JOURNAL OPEN ACCESS

2022 Volume 3 Issue J2 Pages 117-127

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Abstract

Reinforced concrete (RC) structures in chloride-laden environment may have steel corrosion and corre-sponding corrosion cracks due to chloride attack. Although observed corrosion cracks provide an effective information on the status of steel corrosion inside concrete, their relationship is very complex because of many associated parameters such as structural details (e.g. cover and rebar arrangement). In this study, steel corrosion distributions in longitudinal and transverse directions have been obtained through corrosion ex-periments of RC members with different structural details. Parameters to represent the 2D-stochastic field associated with the steel corrosion distribution were identified. With the aid of 3D-finite element analysis, a database consisting of the relationship between steel corrosion and corrosion cracks for machine learning (pix2pix) was developed numerically. Finally, given corrosion crack distributions observed in the deterio-rated RC members and structural details, the structural capacity can be estimated.

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© 2022 Japan Society of Civil Engineers
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