A method and system for reducing frequency and amplitude of multi-source dynamic disturbance in deep buried tunnels

By constructing the disturbance wave feature map and neural network model of deep buried tunnels, the absorption material ratio corresponding to different types of disturbance waves is determined, and the surrounding rock rupture and rock burst problems caused by multi-source dynamic disturbance in deep buried tunnels under high ground stress environments are solved, and the safety and frequency reduction and amplitude reduction effect are improved.

CN119476059BActive Publication Date: 2025-03-21NORTHEASTERN UNIV CHINA
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Patent Information

Application Number
CN202510059524.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-01-15
Publication Date
2025-03-21
Estimated Expiration
2045-01-15

AI Technical Summary

Technical Problem

Deep buried tunnels are susceptible to multi-source dynamic disturbances in high ground stress environments, resulting in surrounding rock rupture and rock bursting. It is difficult for the existing technology to effectively prevent and control such dynamic disturbance trigger damage.

Method used

By obtaining the sensor measurement point position and disturbance wave data, the time domain and frequency domain feature maps are constructed, the disturbance wave type is determined, and the absorbance material data set is divided and neural network training is performed according to the type, and a model is obtained for outputting the absorbance material ratio corresponding to different types of disturbance waves.

Benefits of technology

The accuracy of the proportion of absorbing materials is improved, the frequency reduction and amplitude reduction effect of disturbance waves is enhanced, and the safety of deep buried tunnels in high ground stress environments is improved.

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Abstract

The present invention relates to the field of tunnel excavation construction, and to a method and system for reducing the frequency and amplitude of multi-source dynamic disturbance in a deep-buried tunnel. The method comprises obtaining first information and a wave-absorbing material data set; determining the installation position of a sensor according to the first information, and obtaining second information, the second information comprising disturbance wave data collected by the sensor; constructing a first characteristic graph according to the second information; determining third information according to the first characteristic graph, the third information comprising the type of disturbance wave; dividing the wave-absorbing material data set according to the third information to obtain at least one divided wave-absorbing material data set; training a neural network based on the divided wave-absorbing material data set to obtain at least one trained neural network model, the trained neural network model being used to output wave-absorbing material ratios corresponding to different types of disturbance waves. The present invention determines the ratio selection of grouting wave-absorbing materials based on a neural network, and fills the gap in dynamic disturbance-triggered damage prevention and control technology.
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