The damage degree of a reinforced concrete structure is closely related to the generation and expansion of cracks. However, the traditional damage assessment methods of reinforced concrete structures have defects, including low efficiency of crack detection, low accuracy of crack extraction, and dependence on the experience of inspectors to evaluate the damage of structures. Because of the above problems, this paper proposes a damage assessment method for concrete members combining the U-Net convolutional neural network and crack fractal features. Firstly, the collected test crack images are input into U-Net for segmenting and extracting the output cracks. The damage to the concrete structure is then classified into four empirical levels according to the damage index (DI). Subsequently, a linear regression equation is constructed between the fractal dimension (D) of the cracks and the damage index (DI) of the reinforced concrete members. The damage assessment is then performed by predicting the damage index using linear regression. The method was subsequently employed to predict the damage level of a reinforced concrete shear wall–beam combination specimen, which was then compared with the actual damage level. The results demonstrate that the damage assessment method for concrete members proposed in this study is capable of effectively identifying the damage degree of the concrete members, indicating that the method is both robust and generalizable.

A negative Poisson’s ratio structure has special deformation behavior and energy absorption characteristics, and is a new structure with broad application prospects. However, most of the current research is still at the micro theoretical level, and there is less research on the macro mechanical properties. Therefore, this paper proposes a polyurethane elliptical negative Poisson’s ratio structure (PES), uses the methods of experimental simulation and numerical simulation to carry out a mechanical comparison with a concave negative Poisson’s ratio structure (PCS) and analyze the influence of length–width ratio on the structure, highlighting the advantages of the PES in energy absorption, and using parameter analysis to study the influence of the structural form and material properties on the Poisson’s ratio and elastic modulus of the PES, so as to provide a scientific basis and technical support for the application of the structure in high-end equipment manufacturing, seismic isolation and other fields.

The negative Poisson’s ratio structure has special deformation behavior and energy absorption characteristics and is a new structure with broad application prospects. However, most of the current research is still at the theoretical level, while research on its practical performance is sparse. Therefore, this paper proposes an elliptical negative Poisson’s ratio structural isolation bearing (NRB) for application in the field of seismic isolation engineering. The finite element simulation method is used to conduct a mechanical comparison with the traditional high damping isolation bearing (HDR), highlighting the advantages of the NRB in isolation and energy absorption. At the same time, parameter analysis is used to study the influence of the number and angle of structural holes on the stress of the NRB structure, which is 80% higher than that of the traditional isolation bearing, and incremental dynamic analysis (IDA) is also used. The overall average damage rate decreased by 70.3%, showing significant advantages in seismic energy dissipation, control of component damage, and other aspects, providing a strong data basis for the application of seismic isolation technology in practical engineering.