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Advanced Prediction Research Of Unfavorable Geologic Bodies In High-risk Long Railway Tunnel With Large Section

Posted on:2015-03-21Degree:MasterType:Thesis
Country:ChinaCandidate:W Q LiangFull Text:PDF
GTID:2272330434453872Subject:Civil engineering
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Abstract:With high-risk long tunnel with large section as engineering background, the Paper studies advanced prediction of unfavorable geologic bodies such as fault fracture zones and karst by the reference of a large number of geological prediction literature, field practices and summary of experience. Achievements are as follows:(1)Given the analysis of the strengths and weaknesses as well as the applicable scope of common geological prediction methods and complex geological conditions of tunnel, propose a prediction method to divide geological prediction into four levels according to the seriousness of geologic anomalies, so as to make related reasonable arrangements. Moreover, it’s applied to geological prediction of high-risk long tunnel with large section to verify its feasibility and effectiveness.(2)Summarize the experience of geological prediction of some high-risk long tunnel with large section, and propose four principles of geological prediction, namely, geology as the core, comprehensive prediction, organic combination and dynamic adjustment, which are verified in the project.(3)Analyze the disaster-causing mechanism of faults and karst to systematically study their features and precursory signs, which turn out to be effective after being applied to and verified in high-risk long tunnel with large section.(4)Class A prediction method:geological track prediction, TSP, geological radar, infrared detection and advanced drilling can predict various information of faults and karst accurately, but they’re only recommended for high-risk large faults and karst development zones given the cost and impact on the construction progress.
Keywords/Search Tags:unfavorable geologic bodies, prediction level, karstprediction, fault prediction, prediction characteristics, predictionprinciples
PDF Full Text Request
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