Evaluation of Bolt Corrosion Degree Based on Non-Destructive Testing and Neural Network
Source abstract
Abstract
Anchor bolt corrosion is a complex and dynamic system, and the prediction and identification of its corrosion degree are of significant importance for engineering safety. Currently, non-destructive testing using ultrasonic guided waves can be employed for its detection. Building upon the analysis of anchor bolt corrosion mechanisms, this paper proposes a method for evaluating the corrosion degree of anchor bolts based on multi-scale convolutional neural networks (MS-CNNs) that address the multi-mode propagation and dispersion effects of ultrasonic guided wave signals in non-destructive testing. Electrochemical experiments were conducted to simulate anchor bolt corrosion, and ultrasonic guided wave non-destructive testing was performed every 12 h to obtain waveform data. An MS-CNN was then utilized to accurately diagnose the corrosion degree of the anchor bolts. The test results demonstrate that this method effectively detects and diagnoses the extent of anchor bolt corrosion, facilitating timely troubleshooting and preventing potential safety accidents.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Non-Destructive Testing Techniques · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Hebei Provincial Collaborative Innovation Center of Transportation Power Grid Intelligent Integration Technology and Equipment, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
Funding
State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining Technology · Key independent research project of Hebei Provincial Collaborative Innovation Center of Transportation Power Grid Intelligent Integration Technology and Equipment · Hebei Provincial Science and Technology Plan Funded Project
References (21)
Show source-provided references
1. Modeling time-to-corrosion cracking in chloride contaminated reinforced concrete structures. Discussions and closure
2. Critical chloride content in reinforced concrete—A review10.1016/j.cemconres.2009.08.006 ↗
3. Experimental study on degradation behaviors of rock bolt under the coupled effect of stress and corrosion10.1016/j.conbuildmat.2019.03.335 ↗
4. Corrosion monitoring of rock bolt by using a low coherent fiber-optic interferometry10.1016/j.optlastec.2014.10.004 ↗
5. Experimental study on the shear performance of a prestressed anchored jointed rock-like mass under different corrosion levels10.1016/j.ijrmms.2022.105209 ↗
6. Barrias, A., Casas, J.R., and Villalba, S. (2016). A review of distributed optical fiber sensors for civil engineering applications. Sensors, 16.10.3390/s16050748 ↗
7. Inspection of reinforced concrete interface delamination using ultrasonic guided wave non-destructive test technique10.1007/s11431-012-4882-x ↗
8. Majhi, S., Asilo, L.K., Mukherjee, A., George, N.V., and Uy, B. (2022). Multimodal Monitoring of Corrosion in Reinforced Concrete for Effective Lifecycle Management of Built Facilities. Sustainability, 14.10.3390/su14159696 ↗
9. Detection and assessment of pitting corrosion in rebars using scattering of ultrasonic guided waves10.1016/j.ndteint.2018.10.005 ↗
10. Corrosion assessment of reinforced concrete structures based on equivalent structural parameters using electro-mechanical impedance technique10.1177/1045389X13498317 ↗
11. Study on Multiscale Entropy Evaluation Method of Bolt Anchorage Quality
12. Study on propagation characteristics of ultrasonic guided wave and detection of the defect in resin bolts10.1016/j.apacoust.2022.108843 ↗
13. Quantitative evaluation of steel corrosion induced deterioration in rubber concrete by integrating ultrasonic testing, machine learning and mesoscale simulation10.1016/j.cemconcomp.2022.104426 ↗
14. Convolutional neural networks for inverse problems in imaging: A review10.1109/MSP.2017.2739299 ↗
15. Methods of accelerating chloride-induced corrosion in steel-reinforced concrete: A comparative review10.1016/j.conbuildmat.2021.123165 ↗
16. Modeling of time to corrosion-induced cover cracking in reinforced concrete structures10.1016/j.cemconres.2005.06.007 ↗
17. Guided wave propagation in elongated cylinders and plates10.1016/B978-1-4832-2857-0.50008-3 ↗
18. A comprehensive review on convolutional neural network in machine fault diagnosis10.1016/j.neucom.2020.07.088 ↗
19. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load10.1016/j.ymssp.2017.06.022 ↗
20. StressNet: Hybrid model of LSTM and CNN for stress detection from electroencephalogram signal (EEG)10.1016/j.rico.2023.100231 ↗
21. Short-term wind power prediction based on modal reconstruction and CNN-BiLSTM10.1016/j.egyr.2023.06.005 ↗