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| Back analysis of the TBM collapse section based on convolutional neural networks |
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Abstract This paper explores the possibility of training and predicting the collapse section of TBM excavation using the indexes TPI and FPI by the convolution neural network and time series prediction method based on the existing research work. On the basis of the principle that the input power is equivalent to the rock breaking efficiency, the authors put forward a technical approach by taking the field penetration index FPI and the torsional shear tunneling index TPI as the training and prediction elements. There are 199 columns of parameters and 17 collapse events recorded in the TBM database of Jilin pine diversion project. The total amount of data is large and the quality is high, so it has high scientific research value. Taking the above indexes as the training objects of machine learning, the normal driving section and large-scale collapse section 66 + 000-66 + 350 (chainage number) are analyzed and predicted. The results show that the measured values of FPI and TPI in the collapse section are significantly smaller, . with evaluation conclusion of "positive" based on three prediction criteria. The related research results provide a new method for big data machine learning in TBM field, and create favorable conditions for the realization of advanced geological early warning.
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Received: 23 November 2020
Published: 17 June 2021
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| Fund:Study of mechanism of double-shield TBM shield jamming disaster and active prevention and control technology in deep composite squeezing strata |
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Corresponding Authors:
Yu ZuChen
E-mail: chenzuyu@iwhr.com
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2021, Vol. 42 
