Affiliation:
1. Korea Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Republic of Korea
2. Department of Civil and Environmental Engineering, Hannam University, Daejeon 34430, Republic of Korea
3. Department of Applied Artificial Intelligence, Sung Kyun Kwan University, Seoul 03063, Republic of Korea
Abstract
In this study, accelerated chloride diffusion tests are performed on ordinary Portland cement (OPC), ground granulated blast furnace slag (GGBFS), and fly ash (FA) concretes aged 4–6 years. Passed charge is evaluated according to ASTM-C-1202 for 12 mixtures, considering water–binder (W/B) ratios (0.37, 0.42, and 0.47), GGBFS replacement rates (0%, 30%, 50%), and FA replacement rates (0% and 30%). The effects of aged days on passed charge reduction behavior are quantified through repetitive regression analysis. Among existing machine learning (ML) models, linear, lasso, and ridge models are used to analyze the correlation of aged days and mix properties with passed charge. Passed charge analysis considering long-term age shows a significant variability decrease of passed charge by W/B ratio with increasing age and added admixtures (GGBFS and FA). Furthermore, the higher the water–binder ratio in GGBFS and FA concretes, the greater the decrease in passed charge due to aged days. The ML model-based regression analysis shows high correlation when compressive strength and independent variables are considered together. Future work includes a correlational analysis between mixture properties and chloride ingress durability performance using deep learning models based on the time series properties of evaluation data.
Funder
Ministry of Science and ICT
Subject
General Materials Science
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