Quantitative Evaluation of Corrosion Degrees of Steel Bars Based on Self-Magnetic Flux Leakage

Yang, Ding; Qiu, Junli; Di, Haibo; Zhao, Siyu; Zhou, Jianting; Yang, Feixiong

doi:10.3390/met9090952

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…However, due to the local electrochemical accelerated corrosion method, the corrosion area of the reinforcement is mainly concentrated in the middle area of the reinforcement covered by the moist towel. The higher the resistance of the reinforcement farther away from the negative pole in the specimen [21]. The NaCl solution needs to be infiltrated for a longer time in the corrosion process, which makes the corrosion degree of the reinforcement far away from the negative pole smaller.…”

Section: Internal Steel Bar Corrosion Degreementioning

confidence: 99%

“…The effect of rebar length on quantitative detection is unclear. Yang et al [21] based on the test data, an index "K" was established, and the test index "K" was linearly related to the corrosion degree of the reinforcement. Xia et al [22] proposed a new calculation method to determine the corrosion of steel strands quantitatively and non-destructively by analyzing the SMFL signal of steel strands.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Detection of Corrosion State of Concrete Internal Reinforcement Based on Metal Magnetic Memory

Tang¹,

Zhuo²,

Li³

et al. 2023

Structural Durability &Amp; Health Monitoring

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Corrosion can be very harmful to the service life and several properties of reinforced concrete structures. The metal magnetic memory (MMM) method, as a newly developed spontaneous magnetic flux leakage (SMFL) non-destructive testing (NDT) technique, is considered a potentially viable method for detecting corrosion damage in reinforced concrete members. To this end, in this paper, the indoor electrochemical method was employed to accelerate the corrosion of outsourced concrete specimens with different steel bar diameters, and the normal components B z and its gradient of the SMFL fields on the specimen surfaces were investigated based on the metal magnetic memory (MMM) method. The experimental results showed that the SMFL experimental B z curves are consistent with the analytical results of the theoretical model. Furthermore, the crest-to-trough behavior on the B z signal curve and its zero-point gradient spacing can more accurately indicate the corroded area's extent. Then, a magnetic characteristic parameter W based on a large amount of experimental data was established to characterize the degree of corrosion of the steel bars. The magnetic characteristic parameter W is linearly related to the maximum cross-sectional area loss rate of the corroded reinforcement. This paper will lay the foundation for future research on corrosion detection of reinforced concrete structures based on the MMM method and provide a feasible way for non-destructive detection of corrosion independent of the influence of reinforcement diameter and magnetization history.

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Section: Internal Steel Bar Corrosion Degreementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Detection of Corrosion State of Concrete Internal Reinforcement Based on Metal Magnetic Memory

Tang¹,

Zhuo²,

Li³

et al. 2023

Structural Durability &Amp; Health Monitoring

View full text Add to dashboard Cite

show abstract

“…A two-dimensional magnetic dipole model of the V-groove as shown in figure 1 was developed based on the typical defect shape in the localized rusted area of single rebar [31]. A V-shaped groove defect exists at the central position of rebar of diameter 2r and length 2l.…”

Section: Theoretical Background 21 Corrosion Assessment Index Based O...mentioning

confidence: 99%

Quantitative corrosion detection of reinforced concrete based on self-magnetic flux leakage and rust spot area

Chen¹,

Liu²,

Lin³

et al. 2022

Eng. Res. Express

Self Cite

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The internal rebar corrosion of reinforced concrete (RC) structures harm the bearing capacity and durability of structures. Existing methods can measure rebar corrosion but are unsuitable for RC structures. Given this, the self-magnetic flux leakage (SMFL) field of V-shaped rebar corrosion damage was analyzed according to the magnetic dipole model, and the parameter K was proposed to characterize the corrosion degree. Using the naive Bayes algorithm, the SMFL method and the rust spot area analysis method were correlated to propose a rebar corrosion detection method. A corrosion detection experiment was conducted on RC specimens. The results showed that the parameter K was linearly correlated with the maximum cross-sectional rust loss rate ƞ. Using the parameter K to evaluate the rust degree, the accuracy of rebar rust classification was 70%. After introducing the rust spot area ratio S as a supplementary parameter, the accuracy of rebar rust classification increased by 12.5% to 82.5%. This indicates that the proposed method could quantitatively detect the corrosion of the rebars within the concrete.

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“…However, the quantitative detection of steel corrosion is a major difficulty in this field, and the 'natural magnetic field' is easily affected by other factors. A series of literature [2,[16][17][18] studied factors such as initial magnetic field, environmental magnetic field, steel bar size, and lifting height, and proposed methods to reduce the impact of these factors. The magnetic indexes were constructed based on the magnetic field curve, and they were positively correlated with the mass loss rate of the steel bar, but the indexes were non-linear and discrete.…”

Section: Introductionmentioning

confidence: 99%

Quantitative detection of rebar corrosion by magnetic memory based on first-principles

Yang,

Zhang,

et al. 2024

Eng. Res. Express

Self Cite

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Reinforcement corrosion seriously impacts the bearing capacity and durability of reinforced concrete(RC) structures. It is very important to detect reinforcement’s corrosion state in concrete timely and effective. This paper introduced the magnetic memory method to the quantitative detection of reinforcement corrosion. Based onfirst- principles, the causes of material magnetism were explained from the point of view of particles. The microscopic models of reinforcement corrosion were established and the correlation between the absolute value of magnetization M and mass loss rate α had been analyzed. The experiment of magnetic memory testing of the rebar corrosion was carried out, and the magnetic induction curves of the rebars at different mass loss rates were obtained. Finally, the random forest algorithm was used to realize the quantitative recognition of steel corrosion. The results of microscopic models showed that |M| increased nonlinearly with α. The tangential and normal magnetic induction curves obtained by the experiment showed a trend of overall movement and increasing volatility with the increase of α, then four magnetic indexes (I 1xn , I 1zn , I 2xn , I 2zn ) were proposed based on tangential and normal magnetic induction curves to characterize the mass loss rate α. The regularity of I-α curves was consistent with that of |M|-α curves obtained by the microscopic model. The random forest algorithm was introduced to solve the nonlinear and discrete problems of magnetic indexes, and a hierarchical prediction model of rebar corrosion was established. The prediction accuracy of the model was 85.7%, which can realize the state recognition of steel bars under low corrosion degrees.

show abstract

Quantitative Evaluation of Corrosion Degrees of Steel Bars Based on Self-Magnetic Flux Leakage

Cited by 13 publications

References 23 publications

Quantitative Detection of Corrosion State of Concrete Internal Reinforcement Based on Metal Magnetic Memory

Quantitative Detection of Corrosion State of Concrete Internal Reinforcement Based on Metal Magnetic Memory

Quantitative corrosion detection of reinforced concrete based on self-magnetic flux leakage and rust spot area

Quantitative detection of rebar corrosion by magnetic memory based on first-principles

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