Abstract: In order to study the complex relationships among tunneling parameters of superlarge diameter slurry shield in composite strata, a prediction model of tunneling parameters based on BP neural network and 700ring tunneling data of Shantou Bay Tunnel Project is established. By optimizing function category and network structure, the prediction model can predict cutterhead torque, cutterhead energy consumption and average slurry pressure more accurately and quantitatively. The results show that: (1) In composite strata, the absolute value of the Pearson productmoment correlation coefficient between penetration and tunneling speed, that between penetration and cutterhead rotation speed, and that between cuttehead current and cutterhead rotation speed are all above 0.75, which have good linear correlation; the relationships among other tunneling parameters are not clear. (2) In composite strata, the average errors of cutterhead torque and energy consumption between the predicted values and monitored data are about 5%, that of slurry pressure between predicted value and monitored data is 1.31, which indicates that the prediction results have high precision and can meet the requirements of shield tunneling. (3) The prediction results of tunneling parameters in soft soil stratum have been further improved by the prediction model based on BP neural network. (4) By quantitatively predicting other tunneling parameters in prediction model based on BP neural network, the operation is simplified, and the prediction effect is good, which can provide reference for shield driver, improve the construction efficiency and even realize intelligentized shield tunneling.

Key words: composite strata, superlarge diameter slurry shield, tunneling parameters, Pearson productmoment correlation coefficient, BP neural network, prediction model