Grouting project spewing prediction method and system

By using a bidirectional long short-term memory network model with multi-source data fusion and self-attention mechanism, combined with confidence scoring and dynamic correction factors, the problem of poor adaptive capability of gushing early warning in grouting projects is solved, and collaborative perception and intelligent early warning of grout rheological properties and formation response are realized.

CN121556938AActive Publication Date: 2026-02-24CHINA CONSTR SEVENTH ENG DIVISION CORP LTD +2
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Patent Information

Application Number
CN202610091917.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-23
Publication Date
2026-02-24
Estimated Expiration
2046-01-23

AI Technical Summary

Technical Problem

Existing grouting engineering gushing early warning methods have poor adaptability, cannot effectively assess prediction reliability, have a single perception dimension, cannot adapt to complex geological and construction dynamics, and lack perception of the dynamic characteristics of the interaction between grout and strata.

Method used

A bidirectional long short-term memory network model with a self-attention mechanism is used to predict gushing risk by fusing slurry resistance index and microseismic event cluster density from multiple sources. A model confidence scoring mechanism is introduced, and parameters are adjusted by dynamic correction factors to construct a dual-path decision logic to achieve adaptive early warning.

Benefits of technology

It improves the ability to identify and warn of complex nonlinear surge precursors, ensures the reliability of early warnings under uncertain data conditions, and realizes real-time credibility monitoring of prediction models and iterative optimization of feature calculation logic.

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Abstract

The invention provides a gushing prediction method and system for grouting engineering, and belongs to the technical field of tunnel and underground engineering construction safety monitoring. The method comprises the following steps: collecting grouting pressure, flow, slurry viscosity and micro-seismic event data; calculating a slurry resistance index and a micro-seismic event aggregation density; inputting the initial gushing risk index into a pre-trained machine learning model to obtain an initial gushing risk index and a confidence score; according to whether the confidence score is lower than a preset threshold, selecting direct early warning or entering a parameter correction path; when the confidence coefficient is low, dynamic correction factors are generated based on scores and risk changes, the calculation weight of the slurry resistance index is adjusted, corrected parameters are obtained, and secondary prediction is carried out; and integrating the initial prediction result and the secondary prediction result to obtain a final risk index, and executing graded early warning. According to the invention, the accuracy and reliability of gushing risk prediction are improved through a confidence-driven double-path decision and parameter on-line adaptive correction, and a constructed early warning mechanism.
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