Khudhair, M.; Gucunski, N. Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements. Sensors2023, 23, 8052.
Khudhair, M.; Gucunski, N. Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements. Sensors 2023, 23, 8052.
Khudhair, M.; Gucunski, N. Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements. Sensors2023, 23, 8052.
Khudhair, M.; Gucunski, N. Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements. Sensors 2023, 23, 8052.
Abstract
This research aimed to improve the interpretation of electrical resistivity (ER) results in concrete bridge decks by utilizing results from multiple nondestructive evaluations (NDE) techniques in machine learning algorithms. To achieve this, a parametric study was first conducted using numerical simulations to investigate the effect of various parameters on ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and the moisture condition of delamination. A data set from this study was used to build a machine-learning algorithm based on the Random Forest methodology. This algorithm was then implemented on data collected from a bridge deck in the BEAST® facility. The presented results demonstrate an improvement in the interpretation of ER measurements using data from other NDE technologies.
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