地铁施工AI深基坑变形预警系统
By constructing an AI-based early warning system for deep foundation pit deformation in subway construction, and using the stiffness characteristics of physical support components as logical edge weights in the computational graph model, the system solves the problems of false alarms and insufficient sensitivity in existing early warning systems under complex working conditions, and achieves robust prediction and reliable early warning of deep foundation pit deformation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 浙江城乡工程研究有限公司
- Filing Date
- 2026-04-01
- Publication Date
- 2026-07-17
AI Technical Summary
Existing early warning systems for deep foundation pit deformation in subway construction are prone to false alarms or reduced response sensitivity under complex working conditions. They lack an internalized expression of the mechanical transmission mechanism and displacement coordination law of the underground engineering support system, resulting in a lack of engineering causal chain between the prediction results and the physical world, making it difficult to reflect the attenuation state of the local support effectiveness of the foundation pit.
A deep foundation pit deformation early warning system for subway construction was constructed. The system acquires multi-dimensional sequence data and performs noise reduction through the perception interface unit. The parameter mapping unit transforms the stiffness characteristics of the support components into logical edge weights of the computational graph model. The graph inference calculation module extracts temporal displacement features under topological constraints. The decision instruction generation module outputs early warning information. Combined with the compensation calibration unit, the perturbation analysis unit, and the edge processing gateway, the system achieves prediction driven by physical constraints.
It improves the operational robustness and prediction accuracy of the early warning system under complex interference conditions, ensures that the prediction output is always within the reasonable space of displacement coordination constraints, eliminates logical distortion, and enhances the response sensitivity to structural instability signs and the reliability of early warning judgment.
Smart Images
Figure CN121963443B_ABST