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Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical

Mustafa Khudhair¹, Nenad Gucunski¹

¹Department of Civil & Environmental Engineering, Rutgers University, Piscataway, NJ 08854, USA.

Sensors (Basel, Switzerland)

|October 14, 2023

Abstract:

This research aimed to improve the interpretation of electrical resistivity (ER) results in concrete bridge decks by utilizing machine-learning algorithms developed using data from multiple nondestructive evaluation (NDE) techniques. 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. Subsequently, this algorithm was applied to data collected from an actual bridge deck in the BEAST^® facility, showcasing a significant advancement in ER measurement interpretation through the incorporation of information from other NDE technologies. Such strides are pivotal in advancing the reliability of assessments of structural elements for their durability and safety.

Keywords:

bridge deck concrete corrosion electrical resistivity half-cell potential impact echo machine learning multi-NDE random forest

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