Health assessment methods for long-distance underground culvert structures
By fusing acoustic and optical data, correcting underwater rebound data, and modeling structural evolution, the problem of multi-source data fusion and evaluation framework for long-distance underground culverts was solved, enabling accurate dynamic assessment and risk warning of the culvert's health status.
Patent Information
- Application Number
- CN202511089371.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-05
- Publication Date
- 2025-10-31
- Estimated Expiration
- 2045-08-05
AI Technical Summary
Existing technologies for the detection and assessment of long-distance underground culverts suffer from low accuracy and unclear assessment results due to the deep fusion of multi-source heterogeneous data and the spatiotemporal evolution assessment of structural health status, making it difficult to achieve accurate and timely risk warnings.
By fusing acoustic and optical detection data, combining underwater rebound and core sampling test data to convert strength values and determine material degradation parameters, and combining structural defect data to perform structural evolution modeling and abrupt change risk identification, the system generates a full-field state assessment and risk warning result.
It enables precise, dynamic, and comprehensive assessment of the health status of culverts, improving the reliability of assessment results and early warning capabilities.