A Stochastic Modeling Method for Three-Dimensional Corrosion Pits of Bridge Cable Wires and Its Application
Corrosion, ISSN: 0010-9312, Vol: 80, Issue: 8, Page: 808-817
2024
- 1Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures1
- Readers1
Article Description
Cables have usually served as critical and vulnerable structural components in long-span cable-supported bridges. Cable inspections revealed that corrosion, fatigue, or coupled corrosion-fatigue were the ones of the main failure mechanisms. This paper proposed a stochastic modeling method for three-dimensional (3D) corrosion pits of high-strength bridge wires, which can be applied to rapid fatigue life evaluation according to mass loss caused by surface corrosion pits of bridge wires nondestructively. High-strength steel wire specimens dismantled from the cable-stayed bridge served for 15 y were scanned to obtain the original surface corrosion data. The spatial position coordinates of corrosion pits were considered as a random variable and can be well-fitted by uniform distribution. While the number of corrosion pits can be fitted with a generalized extreme value distribution. The uniform corrosion depth d, which can be equivalent to mass loss rate, was calculated as the input corrosion parameter for 3D corrosion pit modeling. The maximum pitting depth d for the steel wire was found to be associated with d. The geometric parameters for individual corrosion pits were recognized as pit depth d, depth-to-width ratio d/b, and aspect ratio b/a, which were fitted with different probability distributions. What follows is 3D spatial corrosion pits simulation based on the individual corrosion parameters that were sampled and combined from the corresponding probabilistic distributions. Hereafter, the fatigue life evaluation of corroded wires was conducted based on an equivalent surface defect method and compared with the experimental results, verifying the effectiveness of the proposed modeling approaches.
Bibliographic Details
Association for Materials Protection and Performance (AMPP)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know