Resumen en Inglés:

Abstract As one of the new protection technologies in the field of anti-explosion protection of masonry walls, polyurea spraying has attracted wide attention of researchers. This paper established a refined numerical model to predict the dynamic response of unreinforced hollow masonry walls (UWs) and polyurea-reinforced hollow masonry walls (PWs) under blast loading. The numerical simulation results were in good agreement with the reference test results. The progressive failure process of UWs and PWs under blast load was clarified by numerical simulation and failure theory model of wall. According to a series of numerical simulation results, the dimensionless damage parameters (maximum deflection rate and mass loss rate) of the wall were defined, the comprehensive influence of polyurea coating method and blast scaled distance on the damage degree of the wall was summarized, and the damage mode retrieval diagram of polyurea reinforced hollow masonry wall under blast load was established. The results showed that the existence of polyurea coating can provide reliable protection for the wall without catastrophic damage when the blast scaled distance was not less than 1.47 m/kg1/3. The research results of this paper can provide an engineering reference for the polyurea reinforcement of hollow masonry walls.

Resumen en Inglés:

Abstract An indentation contact problem between a cylinder stamp and a bonded elastic layer is investigated in this paper. Based on the Navier equation and boundary conditions, the contact problem is transformed into a governing formula, which is in the form of singular integral equation of the first kind, through Fourier transform techniques. By using dimensionless parameterization tricks, a numerical scheme for inverse contact problem is built, and the contact stresses could be obtained directly through the coupled solution of governing equation and equilibrium relationship. Thereinto, the contact pressure is approximated by Chebyshev polynomials, and the governing equation is solved numerically. The reliability of the numerical results is demonstrated through comparison with existing analytical solutions, and the influences of Poisson’s ratio and layer thickness on contact stresses are discussed. Results show that, contact pressure converges to a Hertzian type when the layer thickness increases, and both increasing Poisson's ratio and reducing the layer thickness can sharpen the contact stress.

Resumen en Inglés:

Abstract This study presents a machine learning framework to predict the crashworthiness of multi-cell tubes. Five distinct cross-sectional designs are selected, and various structural configurations are generated by sampling predefined parameters. The training dataset is generated through finite element (FE) simulations. Using the autoencoder, structural features of the voxelized FE simulations are encoded into a one-dimensional latent space. When combined with thickness information, this latent representation provides a comprehensive description of the tube structure. The prediction model in this study is built using a MLP neural network, selected after a comparative analysis of multiple algorithms. The MLP demonstrates strong predictive capability, achieving errors of 14.21% for mean crushing force and 14.49% for peak crushing force. The method based on an autoencoder and MLP enables rapid and accurate prediction of the crashworthiness of multi-cell tubes. Compared to traditional finite element simulations, which require approximately 2.5 hours to evaluate a single sample, the MLP reduces the prediction time to just 0.079 seconds, significantly lowering computational costs and greatly accelerating the structural optimization process.