ISSN 2096-4498
CN 44-1745/U
Tunnel Construction ›› 2019, Vol. 39 ›› Issue (7): 1132-1140.DOI: 10.3973/j.issn.2096-4498.2019.07.009
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LIU Jiandong, GUO Jingbo, WANG Xudong
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Abstract:
In order to recognize the boulder geology accurately during shield tunneling and ensure construction safety, the recognition methods are studied by theoretical analysis and engineering data validation. And then the modulated specific energy(SM) is put forward and the SM recognition model and recognition matrix of boulder geology are constructed based on the shield tunneling specific energy and main tunneling parameters. By using the BP neural network technology, the monitored tunneling parameters are identified as training samples to establish neural network recognition model of the boulder geology, which has very high recognition accuracy. Finally, the recognition methods are verified by using shield tunneling data. The results show that: (1) The SM method has high fault tolerance, stability and specificity, whose prejudgment effect is better than that of the tunneling specific energy method. (2) The coincidence rates of neural network prejudgment to recognition matrix under 2 sets of monitored data reach 98.3% and 98.8%, respectively. (3) The dual recognition method for boulder geology by taking SM method as basis and neural network method as an auxiliary and reference has a good practical significance.
Key words: shield, tunneling parameters, boulder geology recognition, modulated specific energy, neural network
CLC Number:
U 455
LIU Jiandong, GUO Jingbo, WANG Xudong. Recognition Methods for Boulder Geology Based on Shield Tunneling Parameters[J]. Tunnel Construction, 2019, 39(7): 1132-1140.
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