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A platform for research: civil engineering, architecture and urbanism
Multi-sources information fusion analysis of water inrush disaster in tunnels based on improved theory of evidence
Highlights Model test for fault water inrush disaster in subsea tunnel were introduced. Evolution of multi-sources precursor information in water inrush were discussed. New fusion algorithm based on weighted average of evidence conflict probability. Comprehensive prediction for water inrush by fusion analysis of multi-information.
Abstract Water inrush is one of the most serious geological disasters threatening tunnel construction. Generally, complexity and multi-sources feature of physical information existing in tunnel construction make disaster prediction very difficult, how to accurately predict the disaster becomes a hot topic in the field of tunnel engineering. Dempster-Shafer (DS) theory of evidence is a widely used method for reasoning with multiple evidences, however some unbelievable results usually appear in dealing with highly conflicting evidences by its traditional combination rule. Thus an improved fusion algorithm based on weighted average of evidence conflict probability was firstly introduced into risk prediction of water inrush disaster. Through the improved algorithm, multi-sources precursor information measured from previous model test were fused to predict quantitative risk levels of water inrush for different excavation step of subsea tunnel in the model test. The predicted high risk at the 12th excavation step by improved algorithm agreed well with actual phenomenon of intensive seepage observed in the test, while the traditional method gave a lower level. Moreover, the improved algorithm predicted a more accuracy result in the phase of water inrush (at 16th excavation step shown in test). In brief, the improved algorithm can make more accuracy prediction for water inrush disasters and will provide valuable reference for similar engineering.
Multi-sources information fusion analysis of water inrush disaster in tunnels based on improved theory of evidence
Highlights Model test for fault water inrush disaster in subsea tunnel were introduced. Evolution of multi-sources precursor information in water inrush were discussed. New fusion algorithm based on weighted average of evidence conflict probability. Comprehensive prediction for water inrush by fusion analysis of multi-information.
Abstract Water inrush is one of the most serious geological disasters threatening tunnel construction. Generally, complexity and multi-sources feature of physical information existing in tunnel construction make disaster prediction very difficult, how to accurately predict the disaster becomes a hot topic in the field of tunnel engineering. Dempster-Shafer (DS) theory of evidence is a widely used method for reasoning with multiple evidences, however some unbelievable results usually appear in dealing with highly conflicting evidences by its traditional combination rule. Thus an improved fusion algorithm based on weighted average of evidence conflict probability was firstly introduced into risk prediction of water inrush disaster. Through the improved algorithm, multi-sources precursor information measured from previous model test were fused to predict quantitative risk levels of water inrush for different excavation step of subsea tunnel in the model test. The predicted high risk at the 12th excavation step by improved algorithm agreed well with actual phenomenon of intensive seepage observed in the test, while the traditional method gave a lower level. Moreover, the improved algorithm predicted a more accuracy result in the phase of water inrush (at 16th excavation step shown in test). In brief, the improved algorithm can make more accuracy prediction for water inrush disasters and will provide valuable reference for similar engineering.
Multi-sources information fusion analysis of water inrush disaster in tunnels based on improved theory of evidence
Li, Shucai (author) / Liu, Cong (author) / Zhou, Zongqing (author) / Li, Liping (author) / Shi, Shaoshuai (author) / Yuan, Yongcai (author)
2021-03-30
Article (Journal)
Electronic Resource
English
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