Iron-based shape memory alloys (Fe-SMAs) have considerable potential for the active strengthening of concrete structures, yet convenient externally applicable non-destructive methods for identifying local fatigue damage under cyclic loading remain limited. To investigate the weak magnetic response of notched Fe-SMA and its correspondence with local damage evolution, static tensile tests and constant-amplitude fatigue tests were conducted on Fe-SMA specimens with a semi-circular notch. Weak magnetic signals were continuously monitored at a fixed point throughout loading, and surface magnetic-field scanning was performed after fracture. Under static loading, the magnetic signal evolved consistently with the deformation response. Under fatigue loading, the fixed-point magnetic signal exhibited a clear three-stage evolution corresponding to the development of residual deformation. Compared with deformation hysteresis loops, magnetic hysteresis loops contained richer information on local damage evolution. After fracture, abrupt changes in the scanned magnetic field coincided with the actual fracture location, and the magnetic anomaly gradually attenuated as the scanning path moved away from the notch. These results indicate that weak magnetic signals can effectively characterise the evolution of local fatigue damage in notched Fe-SMA, with the normal magnetic component showing greater sensitivity to damage localisation and state assessment.

The structural integrity of precast concrete composite beams heavily relies on the new-to-old concrete interface. Existing numerical models and traditional analytical methods struggle to capture the complex, multi-variable interfacial degradation mechanisms. This study proposes an integrated framework combining high-fidelity 3D nonlinear finite element (FE) simulations with explainable machine learning (XML). A mixed interface constitutive model, seamlessly coupling surface-based cohesive behavior with residual Coulomb friction, was established and validated to accurately replicate the full-range progressive damage and frictional slip. Using an orthogonal experimental design, a 53-sample database was generated to evaluate key design variables, including material strengths and interfacial roughness. An eXtreme Gradient Boosting (XGBoost)-based surrogate model successfully mapped the nonlinear relationships between these features and core flexural indicators, achieving an R² exceeding 0.965. The SHapley Additive exPlanations (SHAP) framework was subsequently introduced to decode the algorithmic black box, providing transparent traceability of feature importance. Results reveal that cast-in-place concrete strength dominates initial flexural stiffness, whereas tensile reinforcement dictates yield and ultimate capacities. Crucially, interfacial roughness governs the post-peak response, enhancing energy dissipation by over 400%. This data-driven strategy validates that rationally matching material strengths with optimal interfacial friction maximizes the comprehensive flexural potential of composite members.