Khudhair, M.; Gucunski, N. Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck. Signals2023, 4, 836-858.
Khudhair, M.; Gucunski, N. Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck. Signals 2023, 4, 836-858.
Khudhair, M.; Gucunski, N. Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck. Signals2023, 4, 836-858.
Khudhair, M.; Gucunski, N. Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck. Signals 2023, 4, 836-858.
Abstract
Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on the Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of ER and HCP measurements using data from multiple NDE technologies.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
