基于光纤与土压力盒协同监测的路基病害识别方法及系统
By coordinating the deployment of fiber optic sensors and earth pressure cells in the roadbed, a stress-strain correlation model was established, which solved the problem of insufficient correlation of roadbed disease monitoring data in the existing technology and achieved highly accurate and reliable disease identification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANDONG UNIV
- Filing Date
- 2026-04-16
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for monitoring roadbed defects are insufficient to fully reflect the complex mechanical state inside the roadbed, and multi-source monitoring data have failed to effectively establish an internal correlation, resulting in a decline in the accuracy and reliability of defect identification.
By coordinating the deployment of fiber optic sensors and earth pressure cells in different soil layers of the roadbed, a correlation model between multi-source data is established. A stress-strain spatiotemporal mapping model is constructed using machine learning methods to achieve data fusion analysis and disease identification.
It improves the accuracy of subgrade disease identification and the reliability of system operation, and can effectively identify diseases such as settlement, uneven deformation, shear failure and voiding under complex working conditions, and dynamically adapt to the evolution of subgrade condition.
Smart Images
Figure CN122020077B_ABST