Abstract:

The standards for shield tunneling parameter setting are not clear, and there are few experienced shield drivers. Accordingly, to control the shield posture when tunneling, a feasible and popular prediction model of shield tunneling parameterposture based on big data of shield tunneling and support vector machine (SVM) in machine learning algorithm is proposed. The collected data consist of 43 030 samples of shield tunneling parameters and associated strata information within 299 days of shield tunneling in new tunnel of Shanghai metro line 14. The 75% of the samples are used for training, while the 25% are used for testing. The testing results show that the goodnessoffit of the SVM algorithm model reaches 0.863, and the accuracy of prediction with 15% tolerance error reaches 94.5%, which is quite promising comparing to the traditional regression analysis. The trained model is finally used for the optimization of construction parameters to control shield posture automatically in an example and turned to be useful for quality control.

Key words: shield method, construction parameter, shield posture prediction, parameter optimization, machine learning, support vector machine