The conventional design approaches for achieving perfect absorption of electromagnetic (EM) waves using metasurface absorbers (MSAs) are limited to absorbing waves in one direction while reflecting waves in the other. In this study, a novel ultrathin narrowband MSA with bidirectional perfect absorption properties has been proposed, based on a tri-layer metal square-circular-square patch (SCSP) structure. The simulation results demonstrate that the proposed MSA exhibits a remarkable absorbance of 98.1%, which is consistent with the experimental and theoretical calculations. The equivalent constitutive parameters that were retrieved, as well as the simulated surface current and the power loss density distributions, reveal that the perfect absorption of the designed MSA originates from the fundamental dipolar resonance. Furthermore, the proposed MSA demonstrates stable wide-angle absorption properties for both transverse electric (TE) and transverse magnetic (TM) waves under various oblique incidence angles. The absorption characteristics of the MSA can be fine-tuned by adjusting the structural parameters. Additionally, the proposed MSA boasts excellent ultrathin thickness, bidirectional, polarization-insensitive, and wide-angle properties, making it highly suitable for a range of potential applications such as imaging, detection, and sensing.

With the development of various emerging structures, concrete-filled steel tubular (CFST) columns have become critical load-bearing components in key infrastructures such as subways and underground utility tunnels. Accurately predicting their ultimate bearing capacity (N_u) is essential for guaranteeing structural safety. To address the limitations of traditional empirical formulas and code-based calculation approaches, this paper proposes a prediction model for ultimate bearing capacity based on the CatBoost algorithm optimized by Random Search. Furthermore, the marginal contribution of each key feature to the prediction results is measured through interpretability analysis. First, a database containing 438 CFST column ultimate bearing capacity test cases was established, with key parameters such as geometric dimensions and material properties as input variables. Second, the predictive performance of six machine learning algorithms—CatBoost, LightGBM, Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and XGBoost—was compared. A five-fold cross-validation integrated with a Random Search strategy was employed for joint hyperparameter optimization. The results show that the optimized CatBoost model significantly outperforms other algorithms and conventional design codes, achieving a coefficient of determination (R²) as high as 0.99 and a root mean square error (RMSE) of 174.29 kN. Furthermore, the SHAP (Shapley Additive exPlanations) method was used to perform global and local interpretability analyses of the prediction model. This not only quantified the individual contribution and interaction effects of each feature parameter on the bearing capacity but also revealed that geometric parameters are the primary influencing factor. This finding confirms a high degree of consistency between the prediction mechanism of the data-driven model and classical mechanical theories, effectively validating the model’s reliability. This study provides an efficient and reliable tool for the optimal design and rapid evaluation of CFST columns and establishes a new data-driven paradigm for the design and reinforcement of key components in underground structures.