Tracking of disc cutter wear in TBM tunneling: a case study of Kerman water conveyance tunnel

Karami, Morteza; Zare, Shokrollah; Rostami, Jamal

doi:10.1007/s10064-020-01931-7

Cited by 25 publications

(10 citation statements)

References 32 publications

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“…Through collecting data such as the cutter maintenance record sheets and the daily TBM tunneling reports, combined with the cutter information in Figure 3, a targeted statistical analysis of field data is performed to obtain the radial wear extent of cutter rings and the replacement number of cutter rings at different cutter positions, as shown in Figure 5. It can be seen from Figure 5 that the number of the replaced rings and the accumulated radial wear extent of cutter rings both increase with the increase in installation radius for the whole cutter head, which is consistent with the results of existing studies [35][36][37][38][39][40][41]. In addition, the TBM drivers fill out a daily TBM tunneling report to record information such as the TBM's daily tunneling distance, the penetration of the cutter head per rotation, and the thrust value and the torque value during a stable period of each stroke.…”

Section: Data Acquisitionsupporting

confidence: 90%

Analysis and Enlightenment on the Relationships between Two Kinds of Cutter Life Evaluation Indexes and Installation Radius: A Case Study

Liu,

He,

Zhou

et al. 2024

Buildings

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Accurate evaluation of cutter life at different cutter positions on the cutter head is helpful to determine the time of cutter change and reduce the time of cutter wear measurement, which is of great significance to improve the tunneling efficiency of tunnel boring machine (TBM) projects. Unfortunately, there is no unified cutter life evaluation index now. The field data of cutter wear are collected from a section of a long TBM tunneling water conveyance tunnel in China. Two kinds of cutter life evaluation indexes (based on the radial wear extent of cutter rings and replacement number of cutter rings) are selected and the variation rule between these two kinds of indexes with cutter installation radius is statistically analyzed. The results show that the regression relationships between the two kinds of cutter life evaluation indexes and installation radius mainly present linear functions and quadratic functions. Those regression relationships are affected by factors such as wear type, installation angle, cutter spacing, influence width, and allowable limit wear extent of cutter rings. Considering the calculation accuracy of the evaluation index, the actual working conditions of the disc cutter, and ignoring the influence of tunnel diameter, it is recommended to preferentially choose the radial wear extent of cutter rings per unit rolling distance as the evaluation index of cutter life. The research results can provide a reference for the selection of cutter life evaluation index, prediction of disc cutter life at different cutter positions, and establishment of cutter life prediction mode.

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Section: Data Acquisitionsupporting

confidence: 90%

Analysis and Enlightenment on the Relationships between Two Kinds of Cutter Life Evaluation Indexes and Installation Radius: A Case Study

Liu,

He,

Zhou

et al. 2024

Buildings

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show abstract

“…Several other new methods have been published, e.g. [28][29][30][31]. The problem is, however, that these methods are often based on the evaluation of only one or two tunnel drives.…”

Section: Wear Prediction Modelsmentioning

confidence: 99%

State of the Art der Verschleißprognose im maschinellen Tunnelbau

Stopp,

Feinendegen

2023

Geomechanics and Tunnelling

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The wear of cutting tools in mechanised tunnelling is closely linked to the abrasiveness of the present subsoil. Therefore, most of the models used for wear prediction take an abrasiveness index into account. The most common index used for hard rock is the Cerchar Abrasiveness Index (CAI); whereas such a standard does not yet exist for soil. Both the literature research and the results of calculation examples presented in the article show that significantly more practical models are currently available for wear prediction in hard rock. One reason for this is that far more wear‐relevant influencing factors have to be quantified for soft ground. Another reason is that the scientific studies concerning this topic only started in the course of mechanised tunnelling. Since especially the calculations carried out for low‐abrasive rocks differ widely among the different models for tool life, further research is needed also for wear prediction in hard rock

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“…where 𝑐 is a constant penalty factor. By adjusting the classification cost matrix in Equation (18), the SVM model becomes cost-sensitive and can focus on failure samples.…”

Section: Project Summarymentioning

confidence: 99%

“…The data-driven method can predict disc cutter consumption and make maintenance plans before tunneling starts. Generally, the data-driven method can be classified into three kinds-empirical methods [18,19], mechanism-based methods [5,9,[20][21][22][23][24][25][26], and tunneling-parameters-based methods [8,12,27,28].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Tunnel Boring Machine Disc Cutter Replacement Using Kernel Support Vector Machine

et al. 2022

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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.

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Tracking of disc cutter wear in TBM tunneling: a case study of Kerman water conveyance tunnel

Cited by 25 publications

References 32 publications

Analysis and Enlightenment on the Relationships between Two Kinds of Cutter Life Evaluation Indexes and Installation Radius: A Case Study

Analysis and Enlightenment on the Relationships between Two Kinds of Cutter Life Evaluation Indexes and Installation Radius: A Case Study

State of the Art der Verschleißprognose im maschinellen Tunnelbau

Prediction Model of Tunnel Boring Machine Disc Cutter Replacement Using Kernel Support Vector Machine

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