Abstract The key to determine a reasonable installation time for a tunnel′s secondary lining is to evaluate when the deformation of the surrounding rock reaches a stable state. Site measured data contain the information about the development and evolution of the surrounding rock, which directly reflects the state of stress and deformation. In this context, a monitoring index based dynamic comprehensive evaluation model for the deformation state of surrounding rock is set up, which overcomes the defects of a traditional evaluation method that only takes static evaluation at a certain time node. In this new model, evaluation indices are weighted dynamically based on the principle of maximum deviations, for which the indices′weights vary with its stability in certain time domains, and the larger the deviations,the larger the weights. Furthermore, the status and the trend of the variation speed of monitoring indices are taken into consideration by introducing the concept of an acceleration correction coefficient. And such concepts as time weight vector and time degree are also introduced to make sure the collected monitoring data are weighted properly according to different acquisition times (the weights of recent monitoring data are higher than those of long term monitoring data). Finally, the feasibility of the model is verified by a test section in the Zizhi Tunnel in Hangzhou.
Abstract：
The key to determine a reasonable installation time for a tunnel′s secondary lining is to evaluate when the deformation of the surrounding rock reaches a stable state. Site measured data contain the information about the development and evolution of the surrounding rock, which directly reflects the state of stress and deformation. In this context, a monitoring index based dynamic comprehensive evaluation model for the deformation state of surrounding rock is set up, which overcomes the defects of a traditional evaluation method that only takes static evaluation at a certain time node. In this new model, evaluation indices are weighted dynamically based on the principle of maximum deviations, for which the indices′weights vary with its stability in certain time domains, and the larger the deviations,the larger the weights. Furthermore, the status and the trend of the variation speed of monitoring indices are taken into consideration by introducing the concept of an acceleration correction coefficient. And such concepts as time weight vector and time degree are also introduced to make sure the collected monitoring data are weighted properly according to different acquisition times (the weights of recent monitoring data are higher than those of long term monitoring data). Finally, the feasibility of the model is verified by a test section in the Zizhi Tunnel in Hangzhou.
2018, Vol. 55 