Abstract
During tunneling processes, disc cutters of a tunnel boring machine (TBM) usually need to be frequently and unexpectedly replaced. Regular inspections are needed to check disc cutters’ status, which significantly reduces the work efficiency and increases the cost. This paper proposes a new prediction model based on TBM operational parameters and geological conditions that determines whether disc cutter replacement is needed. Firstly, an evaluation criterion for whether the cutters need to be replaced is constructed. Secondly, specific parameters related to the evaluation criterion are analyzed and 18 features are established on tunneling monitoring information. Then, the mapping model between the cutter replacement judgement and the established features is built based on a kernel support vector machine (KSVM). Finally, the data obtained from a Jilin water transport tunnel project is utilized to verify the performance of the proposed model. Test results show that the new model can obtain an average accuracy of 90.0% and an average F1 score of 86.2% on field data prediction based on data from past tunneling days. Therefore, the proposed data-predictive model can be used in tunneling to accurately predict whether disc cutters need to be replaced before human judgment, and thereby greatly improve tunneling safety and efficiency.
Highlights
Academic Editor: Daniel DiasTunnel boring machines (TBMs) have been widely used in hard rock tunneling due to their high tunneling safety, efficiency, and cost-effectiveness [1,2]
This paper presents a method to predict whether TBM disc cutters need replacement based on both operational and geological data
Through the study of historical disc cutter replacement data, this method can automatically predict whether a cutter replacement is needed after a current stroke, without installation of more sensors
Summary
Tunnel boring machines (TBMs) have been widely used in hard rock tunneling due to their high tunneling safety, efficiency, and cost-effectiveness [1,2]. Since sensors are installed on the cutterhead, they are required to work in a harsh working environment of high temperature (70–90 ◦ C), high humidity (80–90% Relative Humidity), and strong vibration (10–20 times the acceleration of gravity), resulting in bad performance with detection techniques based on light [13], lubricant addition [14], the magnetoresistive effect [15], or the eddy-current effect [16,17] Another disadvantage of the sensor monitoring method is that power supply, as well as communication and maintenance of the sensors, are difficult to implement. Established a map between the new health index and 412 data features based on a onedimensional convolutional neural network These models focus on predicting the wear of each disc cutter.
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