Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete

Published on Dec 1, 2020in Measurement3.364
· DOI :10.1016/J.MEASUREMENT.2020.108141
Babatunde Abiodun Salami6
Estimated H-index: 6
(UPM: King Fahd University of Petroleum and Minerals),
Syed M Rahman6
Estimated H-index: 6
(UPM: King Fahd University of Petroleum and Minerals)
+ 2 AuthorsSalah U. Al Dulaijan1
Estimated H-index: 1
(UPM: King Fahd University of Petroleum and Minerals)
Abstract Corrosion initiation time of embedded steel is an important service life parameter, which depends on concrete material make-up, exposure environment, and duration of exposure. The determination of corrosion initiation time in the laboratory is arduous due to the activities involving the data gathering process. This study leverages the capability of machine learning (ML) models to estimate corrosion initiation time of embedded steel through corrosion potential measurement. Self-compacting concrete specimens were prepared with limestone powder addition to Portland cement. The corrosion potentials of the embedded steel in the specimens were measured during their exposure to 5% sodium chloride for an 8-month exposure period. Models were developed to estimate the corrosion potential of embedded steel using concrete mixtures and exposure periods as input variables. Comparing the performance results of the algorithms using correlation coefficient (CC) and root mean square error (RMSE) proved that random forest, an ensemble ML technique, to be the most efficient in estimating the corrosion initiation time of embedded steel in SCC.
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