The damage monitoring and identification of arch bridges provide an important means to ensure the safe operation of arch bridges. At present, many methods have been developed, and the applicability and effectiveness of these methods depend on the damage type, structural configuration and available data. To guide the practical application of these methods, a systematic review is implemented in this paper. Specifically, the damage monitoring and identification methods of arch bridges are divided into the damage monitoring of local diseases and damage identification of overall performance. Firstly, the research on the damage monitoring of the local diseases of arch bridges is reviewed. According to the disease type, it is divided into four categories, including suspender inspection, void monitoring, stress detection and corrosion detection. For each disease, this paper analyzes the principles, advantages and shortcomings of various methods. Then, the damage identification methods of the overall performance of arch bridges are reviewed, including masonry arch bridges, steel arch bridges, reinforced concrete arch bridges and concrete-filled steel tubular arch bridges. And the commonly used damage indexes of damage identification methods are summarized. This review aims to help researchers and practitioners in implementing existing damage detection methods effectively and developing more reliable and practical methods for arch bridges in the future.

To accurately evaluate the overall corrosion degree of reinforced concrete (RC) beams, on the basis of the SMFL technology, the overall random corrosion detection experiment of six RC beams was carried out. The relationship between the tangential component B_y and the normal component B_z of the magnetic induction intensity and corrosion degree was analyzed, and a multidefect magnetic dipole model was established. The correlation between the average corrosion ratio C of the RC beam and the magnetic characteristic index K_G was emphatically explored. The results showed that, with the increase in the average corrosion ratio C, the magnetic characteristic index K_G showed an increasing trend as a whole. The index K_G could weaken the influence of different historical magnetizations, but it had a certain dispersion. On the basis of the correlation and the Naive Bayesian model, the average corrosion ratio C was divided into four grades. The probability of C falling in different value ranges can be quantitatively evaluated using the K_G magnetic characteristic index. The reliability is as high as 97.4 and as low as 56.8 so as to realize the quantitative grading evaluation of the corrosion of the rebar in the RC beam.

Metal Magnetic Memory (MMM) exhibits the advantage of not requiring embedded sensors or external excitation, making it suitable for inspecting ferromagnetic components in engineering structures. This study introduced MMM into stress detection of steel strands. Graded tensile tests were conducted on the steel strands to investigate the correlation between Self-Magnetic Flux Leakage (SMFL) signals and stress levels. Different spatial detection positions with varying Lift-Off Values (LOV) and Rotation Angle Values (RAV) were set to examine the distribution of spatial SMFL field under load. Furthermore, a magnetic characteristic parameter A_N was proposed to assess the stress level of the steel strands. The results indicate that the rate of change in the middle region of the SMFL curve was lower than that at the beginning and the end. Additionally, with increased applied load, the SMFL curve exhibited systematic variations, and the dispersion of the normal component curve gradually decreased. By utilizing the magnetic characteristic parameter A_N, the stress in the steel strands can be calculated, with the parameters determined based on LOV and RAV. This achievement expanded the nondestructive testing methods for steel strands and holds significant research value.

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 BB_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 BB_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 η 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.