Research on the Corrosion Detection of Rebar in Reinforced Concrete Based on SMFL Technology

Author:

Tian Hongsong1,Kong Yujiang1,Liu Bin1,Ouyang Bin1,He Zhenfeng23,Liao Leng23

Affiliation:

1. Guizhou Bridge Construction Group Co., Ltd., Guiyang 550001, China

2. State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China

3. School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Abstract

The corrosion damage of rebars is a leading cause of structural failure in reinforced concrete structures. Timely detection and evaluation of corrosion damage are crucial for ensuring structural safety. The self-magnetic flux leakage (SMFL) technology is often used due to its unique advantages in detecting corrosion damage of rebars. However, challenges persist in theoretically characterizing corrosion damage and exploring influencing factors. Therefore, the magnetic dipole theory model coupled with multiple-shaped defects is proposed and the influence of corrosion expansion force on the detection of corrosion damage is analyzed. The results show that the standard deviation of the magnetic field intensity induced by corrosion varied by up to 833%, while that induced by corrosion expansion force did not exceed 10%. So the changes in the SMFL field induced by corrosion damage play the dominant role and the influence of corrosion expansion force can be ignored. In addition, corrosion damage experiments on reinforced concrete based on the SMFL technology were conducted. The results indicate that the SFML curves of rebars change monotonically with the increasing corrosion degree. Significant variations in the curves correspond well with the locations of severe corrosion on the rebars. There is a positive relationship between the proposed magnetic parameters and the corrosion degree of the rebars. Furthermore, a corrosion damage evaluation model considering multiple parameters is developed to predict the corrosion degree of rebars. The prediction results demonstrate high accuracy, with an average absolute error of only 8.33%, which is within 10%.

Funder

National Natural Science Foundation of China

Chongqing Natural Science Foundation of China

Natural Science Foundation of Chongqing

Science and Technology Project of Guizhou Provincial Transportation Department

Research and Innovation Program for Graduate Students in Chongqing

Publisher

MDPI AG

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