Li’e Yin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Li’e Yin has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Fangbo Xu
School of Civil Engineering, Xijing University, Xi’an 710123, China
2 shared publications - Mengqi Xu
School of Civil Engineering, Xijing University, Xi’an 710123, China
2 shared publications - Ming Xie
School of Civil Engineering, Xijing University, Xi’an 710123, China
2 shared publications - Xiangdong Wu
School of Civil Engineering, Xijing University, Xi’an 710123, China
2 shared publications - Zhangdong Wang
School of Civil Engineering, Xijing University, Xi’an 710123, China
2 shared publications
Latest Publications
- Open Access
Research on the Mechanical Properties of Elliptical Negative Poisson’s Ratio Structures
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.
Open AccessResearch on the Mechanical Properties of Elliptical Negative Poisson’s Ratio Structures
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.
- Open Access
Study on Fractal Damage of Concrete Cracks Based on U-Net
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.
Open AccessStudy on Fractal Damage of Concrete Cracks Based on U-Net
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.