Sodium-ion batteries (SIBs) have emerged as a promising candidate for energy storage applications owing to their material abundance and cost-effectiveness; however, safety issues under mechanical abuse conditions remain insufficiently understood. This study systematically investigates the failure mechanisms of commercial 18650 sodium-ion batteries subjected to radial compression by integrating experimental and numerical approaches. Experiments were conducted using an electronic universal testing machine to characterize the mechanical-electrical-thermal responses at different compression speeds and states of charge (SOC), with synchronous measurements of load, voltage, and temperature. A homogenized finite element model was established to simulate the dynamic crushing behavior at impact velocities ranging from 1 m/s to 35 m/s. The failure mechanisms were interpreted based on stress wave theory, and the failure criteria were calibrated using the experimental results. The results indicate that under quasi-static loading, the battery exhibits a four-stage deformation process, in which the peak load coincides with the onset of failure. With increasing compression velocity, both the peak load and the failure displacement increase, while the temperature rise of batteries at 0% SOC is only weakly affected. In contrast, higher SOC significantly intensifies the temperature rise and advances the occurrence of failure. Under dynamic impact conditions, the failure displacement decreases with increasing impact velocity and shows a pronounced reduction beyond 20 m/s, whereas the load-displacement curve exhibits a distinct plateau at high velocities. The crack initiation location displays a strong dependence on impact velocity: it originates in the central region at low velocities (<15 m/s), shifts to the bottom at approximately 20 m/s, and moves to the impact end when the velocity exceeds 30 m/s. This transition is mainly governed by the propagation, reflection, and superposition of stress waves. Overall, the results indicate that failure of sodium-ion batteries is triggered by structural instability leading to internal short circuits. The SOC primarily controls the thermal response under low-speed compression, whereas stress wave effects dominate the failure behavior at high impact velocities. The proposed model demonstrates good predictive capability for the macroscopic mechanical response and provides valuable insights for the safety design of sodium-ion batteries.

Reinforced concrete (RC) columns are critical load-bearing members in engineering structures. Under explosive loading, they may sustain severe damage and potentially trigger the collapse of the entire structure. Therefore, rapid and accurate assessment of the damage state of RC columns subjected to explosive loading is essential for ensuring structural safety and implementing effective protective measures. Accordingly, a data-driven machine learning (ML) model was proposed to predict the damage indices of RC columns under explosive loading. To train the model, a comprehensive database comprising 3133 samples was established by integrating 259 samples collected from the literature and 2874 supplementary samples generated through numerical simulations. The database includes both RC columns with uniformly spaced stirrups and those with densified stirrups satisfying seismic design requirements. Eleven key parameters were selected as input features, with the RC column damage index taken as the output. Six ML models were employed to predict the damage indices under explosive loading, and their predictive performance was comparatively evaluated using four regression metrics. The results indicate that the tabular prior-data fitted network (TabPFN) achieved the best predictive accuracy and generalization performance, with a coefficient of determination of 0.989 on the test set and a mean absolute error and a root mean square error of 0.018 and 0.030, respectively. The interpretability of the TabPFN model was further analyzed using SHAP (Shapley additive explanations). The results show that charge mass, standoff distance, and column cross-sectional depth are the key features governing the damage severity of RC columns. Notably, the volumetric stirrup reinforcement ratio contributes more significantly to improving the blast resistance of RC columns than the longitudinal reinforcement ratio. Finally, comparison with independent finite element simulation results confirmed that the TabPFN model exhibits good predictive stability as a surrogate model under unseen loading conditions. In terms of computational efficiency, the proposed model achieves a single-prediction inference time of approximately 0.5 s, representing an efficiency improvement of nearly four orders of magnitude compared with conventional numerical simulation methods. The established model enables rapid surrogate prediction of RC column damage under explosive loading and provides guidance for preliminary parameter analysis in blast-resistant structural optimization design.

Prefabricated building structures have been widely applied in civil engineering due to their advantages of energy conservation, environmental protection, controllable quality, and efficient construction. As the core load-bearing components of prefabricated building structures, precast reinforced concrete (PC) slabs are vulnerable to threats from gas explosions, industrial explosions, and terrorist attacks. To accurately assess the damage state of PC slabs under explosion, enhance structural blast resistance, and reduce casualties, an explosion response dataset of PC slabs was constructed. Six geometric parameters (slab thickness/length/width, steel reinforcement ratio, compressive strength of concrete, etc.) and two explosion load parameters (explosive weight and explosive distance) were selected as input features. Three machine learning algorithms (GPR, RF, and XGBoost) were used to predict the maximum displacement of PC slabs, and their prediction accuracies are compared by root mean square error, coefficient of determination, mean absolute error, scattering index, and comprehensive performance objective function. Furthermore, a damage classification evaluation model based on the support rotation angle damage criterion is proposed. The performance differences of the model under three criteria are analyzed by confusion matrix and five classification indices (accuracy, precision, recall, F₁-score, and Kappa coefficient), and compared with simplified models and empirical prediction methods. The research results indicate that in terms of maximum displacement prediction for PC slabs under explosion loads, the XGBoost model demonstrates the best performance among the three machine learning models (GPR, RF and XGBoost). Specifically, the fitting degree of XGBoost is superior to those of GPR and RF models. Meanwhile, and the XGBoost shows the most outstanding comprehensive performance, with a damage recognition accuracy of 92.5%, which demonstrates its high-efficiency in identifying different damage types. The XGBoost-based damage classification evaluation model for PC slabs under explosion loads exhibits powerful performance, providing important references for structural blast resistance design and rapid post-blast damage assessment.