Active and passive acoustic emission-based detection of corrosion damage in steel rods and tendons
Date
2023-08-08
Authors
Mahmoudkhani, Sadegh
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Abstract
This thesis was focused on two aims: first, using acoustic emission (AE) for corrosion damage monitoring of steel tendons, and second, using AE for corrosion damage monitoring of steel grounding rods.
First, the Fuzzy c-means clustering algorithm was employed to differentiate AEs of breaking wires of steel tendons from environmental and grout crack AEs. The signals were post-processed to simulate different ranges of acoustic attenuation. To optimize the speed and reliability of the clustering algorithm, a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was used to find the minimum number of acoustic features needed. The NSGA-II algorithm found 12 combinations of features that resulted in more than 80% wire break detection accuracy. In contrast, less than 3% of grout cracks and 0% of environmental signals were detected as wire breaks. The proposed method has sufficiently low-computational requirements and is reliable and insensitive to attenuation. This work was extended to monitor damage progression in pre-stressed concrete beams exposed to accelerated corrosion. At the termination of the accelerated corrosion experiment, the beam was sliced into sixty-two cross-sections to inspect and correlate corrosion and tendon slippage with AE signals. This work points to the use of AE to track the progression of damage in cases where corrosion has already resulted in tendon fracture and progression is proceeding by loss of bond.
Second, the fuzzy c-means was applied to guided wave pulse-echo detection of corrosion damage in grounding rods. A database of realistic acoustic guided-wave pulse-echo signals was created using accelerated corrosion on steel grounding rods to create corrosion defects with 50% cross-sectional loss. The rods were covered with different thicknesses of clay to simulate different ranges of acoustic attenuation. The NSGA-II was used to select an optimal acoustic feature set. The defect detection method produced low false positives and achieved a depth resolution of approximately 0.3 m. Monte Carlo analysis showed the proposed method detects >99% of damaged segments as damaged, and 92% of intact segments as intact, with a 90% probability. The proposed algorithm is insensitive to attenuation due to varying soil conditions.
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Keywords
Damage Detection, Acoustic Emission