A Method for Assessing Probability of Tunnel Collapse Based on Artificial Intelligence Deformation Prediction
When a tunnel collapse occurs,decision makers often do not have enough reaction time to take appropriate reinforcement measures.Advance prediction of tunnel collapse failure probability has become a key issue in tunnel engineering construction.As for assessing the tunneling collapse failure probability and providing basic risk-controlling strategies,in this study it proposes a novel multi-source information fusion approach that combines the cloud model(CM),the multi-output gaussian process regression(MOGPR),and the improved D-S evidence theory.The fusion of multiple monitoring data(vault displacement,horizontal convergence displacement)reduces data uncertainty and improves the accuracy and robustness of assessment results.In addition,the surrounding rock deformation predicted by artificial intelligence is used as a source of information to obtain an advanced collapse failure probability assessment.As a result,decision makers have a longer response time before the collapse occurs.Applying the method to the Jinzhupa tunnel provides decision makers with more response time.In the end,only a small amount of deformation cracks were generated in the surrounding rock support,avoiding the tunnel collapse.
tunnel collapsefailure probability assessmentcloud modelD-S evidence theorymulti-output gaussian process regressionsafety engineeringengineering geology
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