Shield machines are currently the main tool for underground tunnel construction. Due to the complexity and variability of the underground construction environment, it is necessary to accurately identify the ground in ...Shield machines are currently the main tool for underground tunnel construction. Due to the complexity and variability of the underground construction environment, it is necessary to accurately identify the ground in real-time during the tunnel construction process to match and adjust the tunnel parameters according to the geological conditions to ensure construction safety. Compared with the traditional method of stratum identifcation based on staged drilling sampling, the real-time stratum identifcation method based on construction data has the advantages of low cost and high precision. Due to the huge amount of sensor data of the ultra-large diameter mud-water balance shield machine, in order to balance the identifcation time and recognition accuracy of the formation, it is necessary to screen the multivariate data features collected by hundreds of sensors. In response to this problem, this paper proposes a voting-based feature extraction method (VFS), which integrates multiple feature extraction algorithms FSM, and the frequency of each feature in all feature extraction algorithms is the basis for voting. At the same time, in order to verify the wide applicability of the method, several commonly used classifcation models are used to train and test the obtained efective feature data, and the model accuracy and recognition time are used as evaluation indicators, and the classifcation with the best combination with VFS is obtained. The experimental results of shield machine data of 6 diferent geological structures show that the average accuracy of 13 features obtained by VFS combined with diferent classifcation algorithms is 91%;among them, the random forest model takes less time and has the highest recognition accuracy, reaching 93%, showing best compatibility with VFS. Therefore, the VFS algorithm proposed in this paper has high reliability and wide applicability for stratum identifcation in the process of tunnel construction, and can be matched with a variety of classifer algorithms. By combining 13 features selected from shield machine data features with random forest, the identifcation of the construction stratum environment of shield tunnels can be well realized, and further theoretical guidance for underground engineering construction can be provided.展开更多
基金Supported by National Natural Science Foundation of China and Shanxi Coalbased Low Carbon Joint Fund(Grant No.U1910211)National Natural Science Foundation of China(Grant Nos.51975024 and 52105044)National Key Research and Development Project(Grant No.2019YFC0121700).
文摘Shield machines are currently the main tool for underground tunnel construction. Due to the complexity and variability of the underground construction environment, it is necessary to accurately identify the ground in real-time during the tunnel construction process to match and adjust the tunnel parameters according to the geological conditions to ensure construction safety. Compared with the traditional method of stratum identifcation based on staged drilling sampling, the real-time stratum identifcation method based on construction data has the advantages of low cost and high precision. Due to the huge amount of sensor data of the ultra-large diameter mud-water balance shield machine, in order to balance the identifcation time and recognition accuracy of the formation, it is necessary to screen the multivariate data features collected by hundreds of sensors. In response to this problem, this paper proposes a voting-based feature extraction method (VFS), which integrates multiple feature extraction algorithms FSM, and the frequency of each feature in all feature extraction algorithms is the basis for voting. At the same time, in order to verify the wide applicability of the method, several commonly used classifcation models are used to train and test the obtained efective feature data, and the model accuracy and recognition time are used as evaluation indicators, and the classifcation with the best combination with VFS is obtained. The experimental results of shield machine data of 6 diferent geological structures show that the average accuracy of 13 features obtained by VFS combined with diferent classifcation algorithms is 91%;among them, the random forest model takes less time and has the highest recognition accuracy, reaching 93%, showing best compatibility with VFS. Therefore, the VFS algorithm proposed in this paper has high reliability and wide applicability for stratum identifcation in the process of tunnel construction, and can be matched with a variety of classifer algorithms. By combining 13 features selected from shield machine data features with random forest, the identifcation of the construction stratum environment of shield tunnels can be well realized, and further theoretical guidance for underground engineering construction can be provided.
