Chen Shang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Chen Shang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Bowen Du
Hebei Provincial Collaborative Innovation Center of Transportation Power Grid Intelligent Integration Technology and Equipment, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
1 shared publication - Guang Han
Shijiazhuang Tiedao University
1 shared publication - Liang Geng
State Grid Hebei Electric Power Company, Ltd., Shijiazhuang 050023, China
1 shared publication - Xiaoyun Sun
Hebei Provincial Collaborative Innovation Center of Transportation Power Grid Intelligent Integration Technology and Equipment, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
1 shared publication - Xu Yang
Hebei Provincial Collaborative Innovation Center of Transportation Power Grid Intelligent Integration Technology and Equipment, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
1 shared publication - Zhigang Tao
School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing, China
1 shared publication
Latest Publications
- Open Access
Intelligent Identification of Micro-NPR Bolt Shear Deformation Based on Modular Convolutional Neural Network
As an important means of reinforcement and support, the bolt can effectively resolve the problem of slope instability. Micro-Negative Poisson Ratio (Micro-NPR) bolts are superior to conventional bolts in mitigating large deformations caused by geological shifts. A large number of bolt anchoring systems require non-destructive testing technology for quality inspection. This technology utilizes time-domain signal characteristics to detect internal defects in the bolt anchoring systems of support engineering. The combination of stress wave nondestructive detection technology and modular convolutional neural network method can identify the shear deformation in the case of the anchor slope support. Integrating the identification results of both the shear angle and shear location sub-modules improves the accuracy of detecting shear deformation in micro-NPR bolt anchoring system, which will be of great assistance in our future engineering applications.
Open AccessIntelligent Identification of Micro-NPR Bolt Shear Deformation Based on Modular Convolutional Neural Network
As an important means of reinforcement and support, the bolt can effectively resolve the problem of slope instability. Micro-Negative Poisson Ratio (Micro-NPR) bolts are superior to conventional bolts in mitigating large deformations caused by geological shifts. A large number of bolt anchoring systems require non-destructive testing technology for quality inspection. This technology utilizes time-domain signal characteristics to detect internal defects in the bolt anchoring systems of support engineering. The combination of stress wave nondestructive detection technology and modular convolutional neural network method can identify the shear deformation in the case of the anchor slope support. Integrating the identification results of both the shear angle and shear location sub-modules improves the accuracy of detecting shear deformation in micro-NPR bolt anchoring system, which will be of great assistance in our future engineering applications.