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.

In order to explore an accurate method to evaluate the corrosion of reinforced concrete structures, the spontaneous magnetic flux leakage (SMFL) signal distribution on the surface of reinforced concrete specimens under different corrosion degrees was scanned based on SMFL technology. The influence of steel bar length, steel bar diameter, and other parameters on the distribution of SMFL signal was studied. The correlation between steel bar corrosion and the characteristic magnetic index of concrete structure was explored. Based on the naive Bayesian model, the classification evaluation of the steel bar corrosion degree of concrete structure was carried out. The results show that the variation of SMFL signal is affected by the corrosion degree α. When the lift-off height and the thickness of concrete protective layer remain unchanged, the slope between the peak and trough of Bz (magnetic induction intensity along z direction) curve increases with the increase of α, and the trough of Bx (magnetic induction intensity along x direction) curve decreases with the increase of the corrosion degree α. The peak and trough of magnetic signal curve can be used as the basis for determining the corrosion position. There is a strong correlation between the magnetic characteristic index β, γ, and the steel corrosion degree α obtained by SMFL. Through the characterization relationship between α, β, and γ, the corresponding models of single and comprehensive index β and γ were established. The results showed that the accuracy of β and γ integrated discriminant Naive Bayesian model-III reached 90.7%, which proved that the evaluation method has high reliability. This study explores the application of SMFL in corrosion detection of concrete structures.

The reliability of high-strength bolted connections is critical to the safety of large-scale engineering structures. This study proposes a non-contact quantitative method for detecting bolt loosening based on the self-magnetic flux leakage (SMFL) effect. Systematic experiments were carried out on M14-12.9 bolts, using nine independent specimens tested under six torque levels, to reveal the intrinsic relationship between bolt preload and the “magnetic valley” feature of the surface leakage field. For quantitative evaluation, the absolute value of the differential peak magnetic field, |ΔPMF|, is defined as the core feature parameter. The results show that, in the reference specimen group, |ΔPMF| exhibits a pronounced linear relationship with the applied torque (R² > 0.96), and the corresponding linear regression parameters display good consistency across the nine specimens (RSD ≈ 4%). Comparative tests on two additional bolt specifications clarify how bolt strength grade and geometric size influence the detection sensitivity and linearity. To address lift-off effects, measurements on a representative specimen at four lift-off heights were used to construct a simplified bivariate linear compensation model, which significantly reduces lift-off-induced bias within the working range h = 10–16 mm. Finally, a hierarchical diagnostic scheme for bolt loosening that incorporates lift-off compensation is established on the basis of |ΔPMF|, providing a feasible approach for rapid assessment of bolt loosening under complex service conditions.