Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements
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 nume...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/19/8052 |
| _version_ | 1797575251777814528 |
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| author | Mustafa Khudhair Nenad Gucunski |
| author_facet | Mustafa Khudhair Nenad Gucunski |
| author_sort | Mustafa Khudhair |
| collection | DOAJ |
| description | 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<sup>®</sup> 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. |
| first_indexed | 2024-03-10T21:36:05Z |
| format | Article |
| id | doaj.art-b37aaf7780454d3085ae262967d58382 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:36:05Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b37aaf7780454d3085ae262967d583822023-11-19T15:02:01ZengMDPI AGSensors1424-82202023-09-012319805210.3390/s23198052Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity MeasurementsMustafa Khudhair0Nenad Gucunski1Department of Civil & Environmental Engineering, Rutgers University, Piscataway, NJ 08854, USADepartment of Civil & Environmental Engineering, Rutgers University, Piscataway, NJ 08854, USAThis 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<sup>®</sup> 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.https://www.mdpi.com/1424-8220/23/19/8052electrical resistivityhalf-cell potentialimpact echomachine learningmulti-NDEcorrosion |
| spellingShingle | Mustafa Khudhair Nenad Gucunski Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements Sensors electrical resistivity half-cell potential impact echo machine learning multi-NDE corrosion |
| title | Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements |
| title_full | Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements |
| title_fullStr | Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements |
| title_full_unstemmed | Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements |
| title_short | Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements |
| title_sort | multi nde technology approach to improve interpretation of corrosion in concrete bridge decks based on electrical resistivity measurements |
| topic | electrical resistivity half-cell potential impact echo machine learning multi-NDE corrosion |
| url | https://www.mdpi.com/1424-8220/23/19/8052 |
| work_keys_str_mv | AT mustafakhudhair multindetechnologyapproachtoimproveinterpretationofcorrosioninconcretebridgedecksbasedonelectricalresistivitymeasurements AT nenadgucunski multindetechnologyapproachtoimproveinterpretationofcorrosioninconcretebridgedecksbasedonelectricalresistivitymeasurements |