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Real-time warning method and application of microseismic multi-feature parameters rockburst based on PSO-GRNN model
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Abstract
Rockburst disasters in the construction of deep-buried tunnels seriously threaten the safety of underground projects. Traditional monitoring methods have limitations in the analysis of nonlinear and small-sample microseismic data. To effectively reduce the risk of rockburst disasters, a microseismic multi-parameter monitoring method based on the PSO-GRNN model is proposed. Meanwhile, a field sound and light alarm system is independently developed to provide real-time feedback on the prediction results. This method collects the characteristic parameters of microseismic signals in real time and combines the dynamic comprehensive hazard index
W
Z
(
t
) of the grey correlation degree method to construct a multi-parameter early warning criterion standard, effectively solving the scenarios of nonlinearity and small samples of microseismic data in deep-buried tunnels. The developed method and systems are applied on-site in the DJ Tunnel in western China, with good results. The early warning accuracy rate is 92.8%. A complete closed loop of data collection-intelligent analysis-multi-level early warning- emergency response is constructed, providing valuable references for on-site rockburst early warning.
Springer Science and Business Media LLC
Title: Real-time warning method and application of microseismic multi-feature parameters rockburst based on PSO-GRNN model
Description:
Abstract
Rockburst disasters in the construction of deep-buried tunnels seriously threaten the safety of underground projects.
Traditional monitoring methods have limitations in the analysis of nonlinear and small-sample microseismic data.
To effectively reduce the risk of rockburst disasters, a microseismic multi-parameter monitoring method based on the PSO-GRNN model is proposed.
Meanwhile, a field sound and light alarm system is independently developed to provide real-time feedback on the prediction results.
This method collects the characteristic parameters of microseismic signals in real time and combines the dynamic comprehensive hazard index
W
Z
(
t
) of the grey correlation degree method to construct a multi-parameter early warning criterion standard, effectively solving the scenarios of nonlinearity and small samples of microseismic data in deep-buried tunnels.
The developed method and systems are applied on-site in the DJ Tunnel in western China, with good results.
The early warning accuracy rate is 92.
8%.
A complete closed loop of data collection-intelligent analysis-multi-level early warning- emergency response is constructed, providing valuable references for on-site rockburst early warning.
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