With the increasing number of violent terrorist attacks around the world, it is quite a common to improve the anti-blast performance of structures by reinforcing the exterior of the structure. In order to explore the dynamic performance of polyurea reinforced concrete arch structures, a three-dimensional finite element model was established by LS-DYNA software in this paper. Under the condition of ensuring the validity of the simulation model, the dynamic response of the arch structure under the blast load is investigated. Deflection and vibration of the structure under different reinforcement models are discussed. The optimum thickness of reinforcement (approximately 5 mm) and the strengthening method for the model were found by deformation analysis. The vibration analysis shows that the vibration damping effect of the sandwich arch structure is relatively excellent, but increasing the thickness and number of layers of the polyurea does not necessarily achieve a better vibration damping function for the structure. By reasonable design of the polyurea reinforcement layer and concrete arch structure, a protective structure with excellent performance of anti-blast and vibration damping can be created. Polyurea can be used as a new form of reinforcement in practical applications.

Under axial compression, multi-cell tubes are considered more effective than single-cell tubes. Regular hexagonal multi-cell tubes (HMT) were designed, tested, and analyzed by finite element modeling (FEM). The crushing mechanism of the HMT was revealed by compression testing and FEM. Experiments and FEM revealed that the mean crushing force of the HMT can be increased by 14% by adopting multi-cell topology, which shortens the folding wavelength and enables HMT progressive crushing. Thus, the HMT is more efficient in energy absorption compared with the conventional regular hexagonal thin-walled tube (HST). More triangular cells result in HMTs with much greater mean crushing force and specific energy absorption. Three evaluation methods were proposed and discussed to determine the effective crushing distance. A plastic model established according to classical simplified super-folding elements was shown to consistently predict the mean crushing force of the HMTs.

The application of corrugated steel sandwich panels on ships requires excellent structural performance in impact resistance, which is often achieved by increasing the weight without giving full play to the characteristics of the structure. Considering the mechanical properties of sandwich panels under static and impact loading, a multi-objective optimal method based on a back-propagation (BP) neural network and a genetic algorithm developed in MATLAB is proposed herein. The evaluation criteria for this method included structural mass, static and dynamic stress, static and dynamic deformation, and energy absorption. Before optimization, representative sample points were obtained through numerical simulation calculations. Then, the functional relationship between the design and output variables was generated using the BP neural network. Finally, a standard genetic algorithm (SGA) and an adaptive genetic algorithm (AGA) were used for multi-objective optimization analysis with the established function to obtain the best result. Through this study, a new design concept with high efficiency and reliability was developed to determine the structural parameters that provide the best impact resistance using limited sample points.