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Developing a prediction model for segment joint opening in an underwater shield tunnel
The opening of segment joints is vital to structural stability and leakage prevention for the underwater shield tunnels and it is governed by many environmental variables, e.g., water pressure, earth pressure, and temperature. Identifying the key variables and selecting a reliable predictive model for the joint opening are challenging but important tasks but they are usually done according to subjective judgments. To develop a reliable predictive model, the Nanjing Yangtze River tunnel was introduced as a case study for field monitoring and theoretical analysis. First, the structural health monitoring system in this shield tunnel was used to construct a database of measurements including joint opening, water pressure, earth pressure, and temperatures in the escape layer and inside the segment. Then, Bayesian model class selection was conducted for the variable selection and model construction. Consequently, the water pressure and temperature inside the segment were identified as the input variables for joint opening evaluation. This two-parameter model was validated by using the measurement data. It is useful for the evaluation of similar projects in tunneling engineering.
Developing a prediction model for segment joint opening in an underwater shield tunnel
The opening of segment joints is vital to structural stability and leakage prevention for the underwater shield tunnels and it is governed by many environmental variables, e.g., water pressure, earth pressure, and temperature. Identifying the key variables and selecting a reliable predictive model for the joint opening are challenging but important tasks but they are usually done according to subjective judgments. To develop a reliable predictive model, the Nanjing Yangtze River tunnel was introduced as a case study for field monitoring and theoretical analysis. First, the structural health monitoring system in this shield tunnel was used to construct a database of measurements including joint opening, water pressure, earth pressure, and temperatures in the escape layer and inside the segment. Then, Bayesian model class selection was conducted for the variable selection and model construction. Consequently, the water pressure and temperature inside the segment were identified as the input variables for joint opening evaluation. This two-parameter model was validated by using the measurement data. It is useful for the evaluation of similar projects in tunneling engineering.
Developing a prediction model for segment joint opening in an underwater shield tunnel
Liu, Jin-Quan (author) / Yuen, Ka-Veng (author) / Ke, Jin-Fu (author) / Chen, Wei-Zhong (author)
Marine Georesources & Geotechnology ; 41 ; 132-141
2023-02-01
10 pages
Article (Journal)
Electronic Resource
Unknown
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