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A practical TBM cutter wear prediction model for disc cutter life and rock wear ability
Tunnelling and Underground Space Technology 85 (2019) 92–99
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Tunnelling and Underground Space Technology journal homepage: www.elsevier.com/locate/tust
A practical TBM cutter wear prediction model for disc cutter life and rock wear ability
T
Zhenchuan Suna,b,1, Hailei Zhaoa,b,1, , Kairong Honga,b, Kui Chena,b, Jianjun Zhoua,b, Fengyuan Lia,b, Bing Zhanga,b, Faliang Songa,b, Yandong Yanga,b, Rongyan Hea ⁎
a b
China Railway Tunnel Group Co., Guangzhou 510000, China State Key Laboratory of Shield Machine and Boring Technology, Zhengzhou, China
ARTICLE INFO
ABSTRACT
Keywords: Prediction model Wear experiment Small size cutter Cutter wear Life prediction Rock wear ability Engineering application
Tunnel Boring Machine (TBM) is an effective machine for building hard-rock tunnel. However, the interaction between the cutter of TBM and rock is the key factor before engineering construction, which will cause a lot of unnecessary losses and trouble in the construction process. The reason is that the disc cutter, the core tool of TBM broken easily during the construction process. Engineering practice shows that TBM cutters’ overhaul (including inspection, replacement and repairment) in high abrasive stratum takes almost one-third time of tunnelling as well as the cost of digging construction. To address the problems mentioned above, it is necessary to study the cutter wear prediction of the high abrasive stratum. In this paper, based on the working principle of TBM, the experimental disc cutters were scaled to 1/10 of the actual cutter, and a composite wear test device model of the cutter had been manufactured. Furthermore, the engineering data was collected from the Yin Han Ji Wei project as an example to establish a TBM cutter wear prediction method. The results show that the model can be used to make wear experiments of small size cutters and the cutter life prediction of TBM.
1. Introduction Nowadays, we are witnessing to an ever-increasing need for tunnels in China because of their unique characteristics and potential applications. Tunnels are artificial underground space with a capacity for particular goals such as water transfer, road tunnels, and mine. Therefore, tunnelling techniques play a significant role in varying infrastructure projects. Among them, Tunnel Boring Machine (TBM) is one of the most common methods for tunnelling due to its high excavation rate (Farrokh and Rostami, 2009). Compared with the traditional drilling and blasting method of tunnel construction, TBM has many advantages of safety, high speed, high quality, etc., and is conducive to environmental protection, saving labour and improving work efficiency. However, TBM designs must be changed with different stratum types, because the interaction mechanism should be studied between the rock and machine in varying stratum. Otherwise, it will cause a lot of unnecessary losses and trouble in the construction process. As the core broken rock tool of TBM, disc cutter is a key in the TBM construction process. Engineering practice shows that replacing and repairing TBM cutters in a high abrasive stratum costs one-third of the
total time of tunnelling, while the cost of cutting tools is about one-third of the digging construction cost (Ren, Shen and Arulrajah, 2018). In addition, the usage amount of TBM cutters before construction is difficult to anticipate, which will lead to hardly controllable TBM construction schedule, construction cost and construction risk (Zhao and Song, 2014). Therefore, it is necessary to study the cutter wear prediction of the high abrasive stratum deeply. Parviz (1975) proved that wear would impair the disc cutters cutting performance by testing. Wijk (1992) proposed an equation for TBM cutter wear using the rock strength index, the Cerchar abrasivity index (CAI), disc cutter geometry parameters, cutterhead thrust, and TBM boring distance. The CSM model (Rostami, 1997; Yagiz, 2002) developed by the Colorado School of Mines used the CAI values, calculated TBM advance rate, and TBM working time or excavation rock volume to predict the CCS disc cutter change time. A Norwegian Institute of Technology (NTU) model (1998) used a specialized abrasiveness value (AV) to estimate cutter life. Japan Komatsu (1998) estimated cutter wear conditions by correlating experience with an empirical curve of cutter size, cutterhead rotation speed, and TBM boring speed. Hassanpour et al. (2015) studied TBM performance and disc cutter wear prediction based on ten years experience of TBM tunnelling in Iran. Yang et al. (2018) studied the
Corresponding author. E-mail address: [email protected] (H. Zhao). 1 The authors contributed equally to this work and should be considered co-first authors. ⁎
https://doi.org/10.1016/j.tust.2018.12.010 Received 22 December 2017; Received in revised form 11 December 2018; Accepted 11 December 2018 0886-7798/ © 2018 Elsevier Ltd. All rights reserved.
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fitting to determine overall cutter wear. Alber (2008) focused on the influence of on-site stress on the CAI index. Zhang and Ji (2009) utilized an analytic formula for the arc length of the rock breakage point and the arc length wear-out coefficient to calculate the wear-out of disc cutters. Alber (2008) studied the stress dependency of the Cerchar abrasivity index (CAI) and its effects on wear of selected rock cutting tools. Hassanpour (2018) development of an empirical model to estimate disc cutter wear for sedimentary and low to medium grade metamorphic rocks. Li and Su (2010) predicted that overall cutter wear by using the Elman neural network model based on the shield excavation parameters data. Chen et al. (2011) analyzed the form and reason for disc cutter wear and described a series of measures to reduce disc cutter wear. Wang et al. (2012) established the energy method to predict the disc cutter wear extent based on a mechanical analysis of the disc cutter. Frenzel (2012) established a statistical model for performance and wear prediction based on a previously developed model. Hassanpour et al. (2014) made the introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rocks, which based on a case history of Karaj water conveyance tunnel, Iran. Wang et al. (2015) propounded a disc cutter wear prediction for a hard rock TBM cutterhead based on energy analysis. Despite the importance of cutter wear and its high impact on performance of mechanized excavators, it has been rarely reported on developing practical models for predicting cutter wear and related cost. Analytical or judgmental methods provided above were cited to characterize cutter wear. These methods can be summarized as analysing cutter load empirical formulae, engineering data fitting analyses, experimental parameters derived from the grinding of rock, and qualitative analysis of cutter wear. In this paper, a composite wear test device model was manufactured to predict the wear of cutter. Furthermore, engineering data were used from the Yin Han Ji Wei project as an example to establish a TBM cutter wear prediction method. Combined the above two points, the model can be designed to make wear experiments of small size cutters and the cutter life prediction of TBM.
Fig. 1. The whole structure drawing of the composite wear test device model.
2. The design of the composite wear test device model The wear experimental device of small size cutter is mainly composed of three parts: power system, control and testing system, host system. The host system consists of a working device, a horizontal movement device and a vertical motion device. Fig. 1 shows the whole structure drawing of the composite wear test device model. The digital photo of the model is presented in Fig. 2.
Fig. 2. The digital photo of the model.
Derivation and Validation of TBM Disc Cutter Wear Prediction Model. Plinninger et al. (2003) analyzed the effects of experimental conditions and rock mass properties on the CAI index. Yang and Huang (2005) predicted cutter life by utilizing the rock mass classification. Michalakopoulos et al. (2006) analysed the impact of Steel styli on the CAI index. Ko et al. (2016) researched the effect of geometrical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunneling. Zhao et al. (2007) correlated the rules between the field penetration index f FPI and the cutting coefficient C by utilizing data
3. The operational principle of the composite wear test device model As is shown in Fig. 3, the model based on the operational principle of TBM, and experimental disc cutters are scaled to 1/10 of the actual cutter, Fig. 3a shows the TBM disc cutter of 43.2 cm. Fig. 3b shows the
Fig. 3. The TBM disc cutter of 17 in (a); the experimental disc cutter of 1.7 in (b).
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Fig. 4. The working window of host system (a); the rock sample after wear by the cutter (b); the rock slag (c).
Fig. 5. The location and TBM of Yin Han Ji Wei project.
experimental disc cutter of 4.32 cm. The small size cutter and rock samples are assembled in a fixed position on the host system of the model (Fig. 4a). Then, through synergy work among the host system, control system and power system, cutter began to erosion on the rock in the vertical direction, and the rock slag, showed in Fig. 4c was collected at the same time. The rock sample after wear by the cutter is showed in Fig. 4b. After the wear process, the small size cutter and the rock slag were weighed to determine the rock breaking efficiency and the cutter wear degree. At last, according to the different rock samples from the construction site, the engineer of cutter can predict the cutting-tool wear of TBM, to make accurate cost analysis for engineering construction.
4. Project description of Yin Han Ji Wei The project of Yin Han Ji Wei is a cross basin water transfer project and is composed of three parts: the golden gorge water control project, the Yin Han Ji Wei water-conveyance tunnel (Huang San tunnel and Qinling Mountains tunnel), and the three estuary water control project. As shown in Fig. 5, the tunnels are located at Qinling Mountains in the central and southerly Shaanxi province, which crosses two major river systems, the Yangtze River and the Yellow River, and also located at two nature reserve, southerly Shaanxi and Guanzhong. The tunnel construction uses TBM with 8.05 m in diameter. The excavation of the project through the quartz, granite and diorite accounted for about 75% of all rocks. The largest rock strength is up to 242 MPa, which resulted that cutter wear is serious further influencing the project tremendously. 94
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Fig. 6. Digital photographs of the different cutters.
Fig. 7. The experimental system installation of different diameter disc cutter.
5. The application of the composite wear test device model
different cutter edges with the width of 3 mm and 5 mm, respectively. On the other hand, according to the requirements, the model can be used to manufacture additional cutters with different width and shape of the cutter edge. Thirdly, the model can install with different number cutting edges of the cutter, one or double cutting edge (Fig. 6c). In addition to the above functions, the model can install with different cutter spacing of cutters. As showed in the Fig. 6d, the left one is 11 mm and the right one is 9 mm. In addition, the experimental system installation of different diameter disc cutter and assembly process just as Fig. 7 shown.
5.1. Install different cutters The composite wear test device model has the function of installing different types of cutters. Firstly, the model can install with different diameter cutters. As showed in Fig. 6(a), the diameter of left cutter is 4.32 cm when the right cutter is 3.81 cm. Secondly, the model can install with different width and shape of the cutter edge. Fig. 6(b) shows two 95
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diameter N of sliding steel needle was measured under high definition digital microscope, the abrasiveness index CAI is obtained by the following formula:
CAI =
N * 2000 80.591 * 100
(1)
Fig. 10c is the tip of a steel tool under high definition digital microscope before the test, Fig. 10d is the tip of a steel tool under high definition digital microscope after the test. The parameters of small size cutter: Diameter 4.32 cm; cutter edge width 1.9 mm; flat cutter edge; hardness: the surface hardness of cutter is 56-60HRC, when the hardness of the heart of the cutter is 50-56HRC. The experimental scheme is to set the penetration of each stroke of the cutter is 0.05 mm, each stroke is 250 mm, the wear weight of cutter is measured once every 400 S (namely the total stroke is 100 m, the final penetration is 5 mm), after weighted three times and got the average value of wear weight. The obtained data were shown in Table 2. By the above rock abrasion servo experimental instrument experimental (CAI test) and the cutter wear experiment (One hundred meters wear testing), the CAI values of different rock samples and the wear weight per 100 m of cutter (replace with W) were obtained. Through calculation and linear fitting, the relationship between W and CAI is obtained:
Fig. 8. The rock samples with different CAI: (a) diorite; (b) limestone; (c) griotte; (d) granite; (e) sandstone; (f) rock of Yin Han Ji Wei project.
W= 0.7845 × CAI2
(2)
Namely, the wear weight per 100 m of cutter is proportional to the square of CAI value. The formula is based on Table 2 data obtained by linear fitting and the linear fitting curve as showed in Fig. 11. However, it is inaccurate to predict the wear degree of the cutter only by CAI for the rock faced in practical engineering. The effects of uniaxial compressive strength (UCS), tensile strength (TS) and equivalent quartz content (EQC) should be considered simultaneously. In view of this, we have considered the effect of UCS and EQC at the same time. According to reference (Prieto, 2012), the product of EQC and UCS of rock is Rock Abrasivity Index (RAI), namely
Fig. 9. The ATA-IGG I type rock abrasion servo experimental instrument of State Key Laboratory of Shield Machine and Boring Technology.
RAI = UCS × EQC Table 1 The CAI data of different rock samples. Rock name
griotte
diorite
limestone
sandstone
granite
sample f
CAI
1.48
1.58
1.67
2.51
4.31
3.87
The mineral composition of rock can be obtained through rock slice identification experiment or material composition diffraction experiment. EQC of rocks are calculated by the method of reference (Yang et al., 2012). The UCS of rocks were obtained by uniaxial compression experiment with MTS experimental machine. The CAI of rocks were tested by ATA-IGG I type rock abrasion servo experimental instrument. 27 groups of rock samples were taken from the construction site of a diversion tunnel in Qinling Mountains, a railway tunnel in Guangzhou and a diversion tunnel in Northeast China. The results of the tests are shown in Table 3, and the relationship curve between CAI and RAI is fitted as shown in Fig. 12. As can be seen from Fig. 12, the rock abrasion index CAI has a positive logarithmic relationship with RAI, and the two indexes are highly correlated, the correlation coefficient is 0.956. That is to say, the prediction model seems to only consider the impact of CAI, but actually consider the comprehensive impact of CAI, UCS, EQC, TS and so on.
5.2. Wear ability prediction of different rocks In order to verify the abrasiveness of rocks with different CAI values, Using the experimental model, had done the wear tests of granite, sandstone, diorite, limestone, griotte and rocks of Yin Han Ji Wei project, in this paper, rock of Yin Han Ji Wei project can replace by “sample f”(Fig. 8). The CAI values of different rocks were measured by ATA-IGG I type rock abrasion servo experimental instrument of State Key Laboratory of Shield Machine and Boring Technology. As showed in Fig. 9, the rock abrasion servo experimental instrument experimental process can be controlled by servo, and analysis of the interaction between rock and machine in the whole abrasion process. The test method is easy and non-destructive, and the experimental results can wonder reflect the wear ability of the rock to cutter. The CAI data was shown in Table 1. The method for obtaining the data of Table 1 as follows: First of all, put the rock sample in the fixture and makes the tip of a steel tool (4550HRC) close to it, just as the Fig. 10a shown. The tip of steel moves at the surface of the rock sample at a displacement rate of 10 mm/min, the total displacement is 10 mm, taking into account the anisotropy of rock, two scratch tests were made on the surface of the sample, and the angle between the two scratches was 90 degrees (Fig. 10b). The wear
5.3. The prediction methods of cutter wear 5.3.1. The prediction methods based on W Depending on the results of laboratory experiments, in the case of no abnormal wear, the mass loss is proportional to the cutter's rolling distance. Using this principle, through collecting rock samples from the proposed projects and projects have been built to carry out the small size cutter wear experiments, then measured their W respectively. Then through the actual use of the cutter in the construction site, the cutter consumption of the proposed project is predicted by analogy, just as the following Eq. (3). 96
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Fig. 10. The core test device of ATA-IGG I type rock abrasion servo experimental instrument (a); the rock sample after the test (b); the tip of steel tool under high definition digital microscope before the test (c); the tip of steel tool under high definition digital microscope after the test (d).
represents the wear weight per 100 m of small size cutter for the proposed projects.
Table 2 The wear weight per 100 m of cutter for different rock samples. Rock name
Griotte
Diorite
Limestone
Sandstone
Wear times first time second time third time average value
The wear weight per 100 m of cutter(mg) 1 2 3 3 1 1 2 3 2 1 2 4 1.3 1.7 2.3 3.4
Granite
Sample f
9 9 10 9.3
8 8 8 8.0
5.3.2. The auxiliary prediction methods based on CAI According to the experimental results of rock abrasion servo experimental and small size cutter wear test, W is proportional to the square of CAI. By using this rule, first we can test the CAI values of rock Table 3 Test results of geological sensitive indexes.
Fig. 11. The fitting curve of W and CAI2.
W1 = W2
2 2
(3)
In the above equation, W1 represents the service life of a single cutter for the projects which have been built, can be measured by rockbroken volume of per cutter. W2 represents the service life of a single cutter for the proposed projects; ω1 represents the wear weight per 100 m of small size cutter for the projects which have been built, ω2
Number
Rock properties
UCS/MPa
EQC/%
CAI/(×10−1 mm)
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 C1 C2 C3 C4 C5
granite diorite granite granite granite granite granite granite granite schist granite pelitic siltstone pelitic siltstone pelitic siltstone pelitic siltstone sandstone sandstone sandstone sandstone sandy mudstone sandy mudstone sandy mudstone diorite granite limestone limestone limestone
103 180 210 123 125 140 193 185 160 192 173 16 23 24 26 34 45 40 44 8 11 13 59 37 69 52 89
44.2 51.9 45.0 44.2 48.4 45.0 46.5 45.0 49.2 36.9 50.0 49.0 48.9 33.9 41.8 63.0 66.0 89.6 58.0 24.1 29.9 32.1 30.2 40.3 26.1 28.0 15.8
3.35 3.68 3.87 3.51 3.53 3.59 3.54 3.73 3.42 3.63 3.76 1.00 1.40 1.11 1.32 2.82 3.04 3.08 2.98 0.34 0.59 0.78 2.39 2.11 2.10 1.81 1.77
Notes: A1-A11 from a diversion tunnel in Qinling Mountains; B1-B11 from a railway tunnel in Guangzhou; C1-C5 from a diversion tunnel in Northeast China. 97
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each cutter is 124 m3. Based on Table 5, the results of wear test of small size cutter for different projects rock, when the penetration of each stroke of the small size cutter is 0.05 mm, the average W of Beijing nuclear industry rock samples is 9.0 mg, the average W of Gao Li Gong Shan tunnel is 3.3 mg, when the average W of Yin Han Ji Wei is 18.8 mg. Assume that for the same rock sample, the W is proportional to the rock-broken volume, the following result can be obtained: By predicting the weight of cutter wear, it is preliminarily proved that the service life of a cutter in the nuclear engineering proposed project will be x1 (259 m3). Excavation diameter of the proposed project is 7.6 m. The prediction consumption of cutter in the nuclear engineering proposed project will be 5.7 m/handful by calculating. The average service life of a cutter in the Gao Li Gong Shan tunnel project will be x2 (706 m3) rock-broken. This prediction result is very consistent with the service life of the cutter at the site, and the error is only 3.9%. As shown in Fig. 13. Fig. 13 (a) is the “Caiyun TBM” with diameter 9.03 m in the tunnel of Gao Li Gong Shan. Fig. 13 (b) for statistical cutter cumulative wear of TBM digging 1500 m, by calculating the actual rock-broken volume of the single cutter is 735 m3. Therefore, by comparing the predicted results with the actual working conditions, it is proved that the model has high accuracy and strong operability.
Fig. 12. The fitting relationship between CAI and RAI.
samples, which be collected from the projects have been built and the proposed projects. Then through the actual use of the cutter in the construction site, the cutter consumption of the proposed project is predicted by analogy, just as the following Eq. (4).
CAI22 W1 = W2 CAI12
6. Conclusions
(4)
The composite wear test device model was successfully designed and manufactured in this paper; The operational principle of the composite wear test device model was analyzed; At the same time, the support project of Yin Han Ji Wei was introduced. In addition, the wear tests of small size cutters of different sizes and different types rock samples were carried out. The quantitative relationship between the wear rate of the cutter and the CAI, UCS and EQC value of rock abrasiveness was established. Based on the wear weight of small size cutter and the index of abrasiveness of rock, two prediction methods of cutter life were established, and which were applied by a commissioned experiment of the proposed project of Beijing nuclear industry and Gao Li Gong Shan tunnel project. Finally, by comparing the predicted results with the actual working conditions, it is proved that the model has high accuracy and strong operability.
In the above equation, W1 represents the service life of a single cutter for the projects which have been built, can be measured by rockbroken volume of per cutter. W2 represents the service life of a single cutter for the proposed projects; CAI1 represents the index of rock abrasiveness for the projects which have been built; CAI2 represents the index of rock abrasiveness for the proposed projects. 5.3.3. The application of cutter wear prediction method The actual use situation of cutters in the front 2000 m test section of Yin Han Ji Wei TBM project just as showed in the Table 4. Through the analysis on Table 4, the actual consumption of cutter in front 2000 m of Yin Han Ji Wei TBM project is 2.46 m/handful. The excavation diameter of TBM is 8.03 m. The calculation results show that the life of
Table 4 The use situation of cutters in the front 2000 m test section about Yin Han Ji Wei project. Boring stage
Boring distance (m)
Number of cutter change
Use amount of new cutter (handful)
Replacement rate of new cutter (%)
Abnormal ware (time)
Abnormal ware rate
Boring distance of each cutter (m/handful)
Stage 1 Stage 2 Stage 3 Stage 4 Total value
500 500 500 500 2000
401 463 363 466 1693
208 196 185 225 814
51.9 42.3 50.1 48.2 48.1
92 46 16 56 210
22.9% 9.9% 4.4% 12% 12.4%
2.40 2.55 2.70 2.22 2.46
Table 5 The results of wear test of small size cutter for different projects. Projects
W (the first time) W (the second time) W (the third time) W (the fourth time) W (the fifth time) W (the sixth time) average value
Yin Han Ji Wei
Proposed project of Beijing nuclear industry
Proposed project of Gao Li Gong Shan tunnel
20 mg 16 mg 18 mg 21 mg 25 mg 13 mg 18.8 mg
10 mg 9 mg 10 mg 10 mg 7 mg 8 mg 9.0 mg
4 mg 3 mg 3 mg 4 mg 3 mg 3 mg 3.3 mg
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Fig. 13. The “Caiyun TBM” with diameter 9.03 m(a); Statistical cutter cumulative wear of TBM digging 1500 m.
Acknowledgements
Komatsu, 1998. Wear detecting method for disc cutter and wear detecting equipment. In Japan: JP10140981-A. Li, X., Su, X.J., 2010. A new method for forecasting shield’s disc-cutters wearing based on Elman neural network. J. Liaoning Techn. Univ. (Nat. Sci.) 29 (6), 1121–1124. Michalakopoulos, T.N., Anagnostou, V.G., Bassanou, M.E., et al., 2006. The influence of steel styli hardness on the Cerchar abrasiveness index value. Int. J. Rock Mech. Min. Sci. 43, 321–327. Parviz, F.R.A.D., 1975. Bluntness and wear of rolling disk cutters. Int. J. Rock Mech. Min. Sci. Geomech. Abstr. 12, 93–99. Plinninger, R., Asling, H.K., Thuro, K., et al., 2003. Testing conditions and geomechanical properties influencing the CERCHAR abrasiveness index (CAI) value. Int. J. Rock Mech. Min. Sci. 40, 259–263. Prieto L.A., 2012. The cerchar abrasivity index’s applicability to dredging rock [C] // Proceedings of the Western Dredging Association (WEDA XXXⅡ) Technical Conference and Texas A & M University (TAMU 43) Dredging Seminar. San Antonio: [s. n.], 212–219. Ren, D.J., Shen, S.L., Arulrajah, A., et al., 2018. Prediction model of TBM disc cutter wear during tunnelling in heterogeneous ground. Rock Mech. Rock Eng. 1–13. Rostami, J., 1997. Development of a Force Estimation Model for Rock Fragmentation with Disc Cutters through Theoretical Modeling and Physical Measurement of Crushed Zone Pressure [Doctorate Thesis]. Colorado School of Mines, Golden, Colorado, USA. Wang, L.H., Kang, Y.L., Cai, Z.X., et al., 2012. The energy method to predict disc cutter wear extent for hard rock TBMs. Tunn. Undergr. Space Technol. 28, 183–191. Wang, L., Kang, Y., Zhao, X., et al., 2015. Disc cutter wear prediction for a hard rock TBM cutterhead based on energy analysis. Tunn. Undergr. Space Technol. 50, 324–333. Wijk, G., 1992. A model of tunnel boring machine performance. Geotech. Geol. Eng. 10, 19–40. Yagiz, S., 2002. Development of Rock Fracture and Brittleness Indices to Quantify the Effects of Rock Mass Features and Toughness in the CSM Model Basic Penetration for Hard Rock Tunneling Machines [Doctorate Thesis]. Colorado School of Mines, Golden, Colorado, USA. Yang, Y., Hong, K., Sun, Z., et al., 2018. The derivation and validation of TBM disc cutter wear prediction model. Geotech. Geol. Eng. 2018, 1–8. Yang, Y.Y., Huang, H.W., 2005. Application of rock mass classification in cutter life prediction of TBM. Chin. J. Undergr. Space Eng. 1 (5), 721–724. Yang, Z., Jiang, Y., Feng, J., et al., 2012. Abrasivity characteristics of surrounding rock mass of Shiziyang Tunnel. J. PLA Univ. Sci. Technol. 13 (3), 311–315. Zhang, Z.H., Ji, C.M., 2009. Analytic solution and its usage of arc length of rock breakage point of disc edge on full face rock tunnel boring machine. J. Basic Sci. Eng. 17 (2), 265–273. Zhao, W.G., Liu, M.Y., Du, Y.L., et al., 2007. Abnormal cutter wear recognition of full face tunnel boring machine (TBM). Chin. Mech. Eng. 18 (2), 150–153. Zhao, N., Song, L.W., 2014. Research on breaking energy mechanism of tunnel boring machine disc cutter rock with CSM model. Appl. Mech. Mater. 651–653, 988–991.
We gratefully acknowledge the financial support by the Science and Technology Research and Development plan of China Railway Corporation (2016G004-A); Science and Technology Innovation Plan of China Railway Tunnel Group (2015-18; 2016-03; 2018-06); Integrated geological survey instrument for quantitative prediction of unfavorable geology in tunnel construction (51327802); Research on Shield Tunnelling Attitude Control Model under Complex Tunnel Linear Condition (51478146); Science and Technology Innovation Plan of China Railway Construction Investment Co., Ltd. (2016) 01-3; Research topics of Shenzhen Metro Group (ZHDT-KY035/2017); The Opening Subject of State Key Laboratory of Shield Machine and Boring Technology (SKLST-2018-k03); Henan Province Natural Science Foundation (182300410197). References Alber, M., 2008. Stress dependency of the Cerchar abrasivity index (CAI) and its effects on wear of selected rock cutting tools. Tunn. Undergr. Space Technol. 23 (4), 351–359. Chen, L., Yan, H., Hu, Z., et al., 2011. A surver on abrasion of disc cutters domestic shield machines. Heat Treat. Technol. Equip. 32 (3), 52–56. Farrokh, E., Rostami, J., 2009. Effect of adverse geological condition on TBM operation in Ghomroud tunnel conveyance project. Tunn. Undergr. Space Technol. Incorporp. Trench. Technol. Res. 24 (4), 436–446. Frenzel, C., 2012. Modeling uncertainty in cutter wear prediction for tunnel boring machines. GeoCongress. ASCE 2012, 3239–3247. Hassanpour, J., 2018. Development of an empirical model to estimate disc cutter wear for sedimentary and low to medium grade metamorphic rocks. Tunn. Undergr. Space Technol. 75, 90–99. Hassanpour, J., Rostami, J., Azali, S.T., et al., 2014. Introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rocks; a case history of Karaj water conveyance tunnel. Iran. Tunn. Undergr. Space Technol. 43 (7), 222–231. Hassanpour, J., Rostami, J., Zhao, J., et al., 2015. TBM performance and disc cutter wear prediction based on ten years experience of TBM tunnelling in Iran. Geomechanik Und Tunnelbau 8 (3), 239–247. Ko, T.Y., Kim, T.K., Son, Y., et al., 2016. Effect of geomechanical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunnelling. Tunn. Undergr. Space Technol. 57, 99–111.
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Tunnelling and Underground Space Technology journal homepage: www.elsevier.com/locate/tust
A practical TBM cutter wear prediction model for disc cutter life and rock wear ability
T
Zhenchuan Suna,b,1, Hailei Zhaoa,b,1, , Kairong Honga,b, Kui Chena,b, Jianjun Zhoua,b, Fengyuan Lia,b, Bing Zhanga,b, Faliang Songa,b, Yandong Yanga,b, Rongyan Hea ⁎
a b
China Railway Tunnel Group Co., Guangzhou 510000, China State Key Laboratory of Shield Machine and Boring Technology, Zhengzhou, China
ARTICLE INFO
ABSTRACT
Keywords: Prediction model Wear experiment Small size cutter Cutter wear Life prediction Rock wear ability Engineering application
Tunnel Boring Machine (TBM) is an effective machine for building hard-rock tunnel. However, the interaction between the cutter of TBM and rock is the key factor before engineering construction, which will cause a lot of unnecessary losses and trouble in the construction process. The reason is that the disc cutter, the core tool of TBM broken easily during the construction process. Engineering practice shows that TBM cutters’ overhaul (including inspection, replacement and repairment) in high abrasive stratum takes almost one-third time of tunnelling as well as the cost of digging construction. To address the problems mentioned above, it is necessary to study the cutter wear prediction of the high abrasive stratum. In this paper, based on the working principle of TBM, the experimental disc cutters were scaled to 1/10 of the actual cutter, and a composite wear test device model of the cutter had been manufactured. Furthermore, the engineering data was collected from the Yin Han Ji Wei project as an example to establish a TBM cutter wear prediction method. The results show that the model can be used to make wear experiments of small size cutters and the cutter life prediction of TBM.
1. Introduction Nowadays, we are witnessing to an ever-increasing need for tunnels in China because of their unique characteristics and potential applications. Tunnels are artificial underground space with a capacity for particular goals such as water transfer, road tunnels, and mine. Therefore, tunnelling techniques play a significant role in varying infrastructure projects. Among them, Tunnel Boring Machine (TBM) is one of the most common methods for tunnelling due to its high excavation rate (Farrokh and Rostami, 2009). Compared with the traditional drilling and blasting method of tunnel construction, TBM has many advantages of safety, high speed, high quality, etc., and is conducive to environmental protection, saving labour and improving work efficiency. However, TBM designs must be changed with different stratum types, because the interaction mechanism should be studied between the rock and machine in varying stratum. Otherwise, it will cause a lot of unnecessary losses and trouble in the construction process. As the core broken rock tool of TBM, disc cutter is a key in the TBM construction process. Engineering practice shows that replacing and repairing TBM cutters in a high abrasive stratum costs one-third of the
total time of tunnelling, while the cost of cutting tools is about one-third of the digging construction cost (Ren, Shen and Arulrajah, 2018). In addition, the usage amount of TBM cutters before construction is difficult to anticipate, which will lead to hardly controllable TBM construction schedule, construction cost and construction risk (Zhao and Song, 2014). Therefore, it is necessary to study the cutter wear prediction of the high abrasive stratum deeply. Parviz (1975) proved that wear would impair the disc cutters cutting performance by testing. Wijk (1992) proposed an equation for TBM cutter wear using the rock strength index, the Cerchar abrasivity index (CAI), disc cutter geometry parameters, cutterhead thrust, and TBM boring distance. The CSM model (Rostami, 1997; Yagiz, 2002) developed by the Colorado School of Mines used the CAI values, calculated TBM advance rate, and TBM working time or excavation rock volume to predict the CCS disc cutter change time. A Norwegian Institute of Technology (NTU) model (1998) used a specialized abrasiveness value (AV) to estimate cutter life. Japan Komatsu (1998) estimated cutter wear conditions by correlating experience with an empirical curve of cutter size, cutterhead rotation speed, and TBM boring speed. Hassanpour et al. (2015) studied TBM performance and disc cutter wear prediction based on ten years experience of TBM tunnelling in Iran. Yang et al. (2018) studied the
Corresponding author. E-mail address: [email protected] (H. Zhao). 1 The authors contributed equally to this work and should be considered co-first authors. ⁎
https://doi.org/10.1016/j.tust.2018.12.010 Received 22 December 2017; Received in revised form 11 December 2018; Accepted 11 December 2018 0886-7798/ © 2018 Elsevier Ltd. All rights reserved.
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fitting to determine overall cutter wear. Alber (2008) focused on the influence of on-site stress on the CAI index. Zhang and Ji (2009) utilized an analytic formula for the arc length of the rock breakage point and the arc length wear-out coefficient to calculate the wear-out of disc cutters. Alber (2008) studied the stress dependency of the Cerchar abrasivity index (CAI) and its effects on wear of selected rock cutting tools. Hassanpour (2018) development of an empirical model to estimate disc cutter wear for sedimentary and low to medium grade metamorphic rocks. Li and Su (2010) predicted that overall cutter wear by using the Elman neural network model based on the shield excavation parameters data. Chen et al. (2011) analyzed the form and reason for disc cutter wear and described a series of measures to reduce disc cutter wear. Wang et al. (2012) established the energy method to predict the disc cutter wear extent based on a mechanical analysis of the disc cutter. Frenzel (2012) established a statistical model for performance and wear prediction based on a previously developed model. Hassanpour et al. (2014) made the introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rocks, which based on a case history of Karaj water conveyance tunnel, Iran. Wang et al. (2015) propounded a disc cutter wear prediction for a hard rock TBM cutterhead based on energy analysis. Despite the importance of cutter wear and its high impact on performance of mechanized excavators, it has been rarely reported on developing practical models for predicting cutter wear and related cost. Analytical or judgmental methods provided above were cited to characterize cutter wear. These methods can be summarized as analysing cutter load empirical formulae, engineering data fitting analyses, experimental parameters derived from the grinding of rock, and qualitative analysis of cutter wear. In this paper, a composite wear test device model was manufactured to predict the wear of cutter. Furthermore, engineering data were used from the Yin Han Ji Wei project as an example to establish a TBM cutter wear prediction method. Combined the above two points, the model can be designed to make wear experiments of small size cutters and the cutter life prediction of TBM.
Fig. 1. The whole structure drawing of the composite wear test device model.
2. The design of the composite wear test device model The wear experimental device of small size cutter is mainly composed of three parts: power system, control and testing system, host system. The host system consists of a working device, a horizontal movement device and a vertical motion device. Fig. 1 shows the whole structure drawing of the composite wear test device model. The digital photo of the model is presented in Fig. 2.
Fig. 2. The digital photo of the model.
Derivation and Validation of TBM Disc Cutter Wear Prediction Model. Plinninger et al. (2003) analyzed the effects of experimental conditions and rock mass properties on the CAI index. Yang and Huang (2005) predicted cutter life by utilizing the rock mass classification. Michalakopoulos et al. (2006) analysed the impact of Steel styli on the CAI index. Ko et al. (2016) researched the effect of geometrical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunneling. Zhao et al. (2007) correlated the rules between the field penetration index f FPI and the cutting coefficient C by utilizing data
3. The operational principle of the composite wear test device model As is shown in Fig. 3, the model based on the operational principle of TBM, and experimental disc cutters are scaled to 1/10 of the actual cutter, Fig. 3a shows the TBM disc cutter of 43.2 cm. Fig. 3b shows the
Fig. 3. The TBM disc cutter of 17 in (a); the experimental disc cutter of 1.7 in (b).
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Fig. 4. The working window of host system (a); the rock sample after wear by the cutter (b); the rock slag (c).
Fig. 5. The location and TBM of Yin Han Ji Wei project.
experimental disc cutter of 4.32 cm. The small size cutter and rock samples are assembled in a fixed position on the host system of the model (Fig. 4a). Then, through synergy work among the host system, control system and power system, cutter began to erosion on the rock in the vertical direction, and the rock slag, showed in Fig. 4c was collected at the same time. The rock sample after wear by the cutter is showed in Fig. 4b. After the wear process, the small size cutter and the rock slag were weighed to determine the rock breaking efficiency and the cutter wear degree. At last, according to the different rock samples from the construction site, the engineer of cutter can predict the cutting-tool wear of TBM, to make accurate cost analysis for engineering construction.
4. Project description of Yin Han Ji Wei The project of Yin Han Ji Wei is a cross basin water transfer project and is composed of three parts: the golden gorge water control project, the Yin Han Ji Wei water-conveyance tunnel (Huang San tunnel and Qinling Mountains tunnel), and the three estuary water control project. As shown in Fig. 5, the tunnels are located at Qinling Mountains in the central and southerly Shaanxi province, which crosses two major river systems, the Yangtze River and the Yellow River, and also located at two nature reserve, southerly Shaanxi and Guanzhong. The tunnel construction uses TBM with 8.05 m in diameter. The excavation of the project through the quartz, granite and diorite accounted for about 75% of all rocks. The largest rock strength is up to 242 MPa, which resulted that cutter wear is serious further influencing the project tremendously. 94
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Fig. 6. Digital photographs of the different cutters.
Fig. 7. The experimental system installation of different diameter disc cutter.
5. The application of the composite wear test device model
different cutter edges with the width of 3 mm and 5 mm, respectively. On the other hand, according to the requirements, the model can be used to manufacture additional cutters with different width and shape of the cutter edge. Thirdly, the model can install with different number cutting edges of the cutter, one or double cutting edge (Fig. 6c). In addition to the above functions, the model can install with different cutter spacing of cutters. As showed in the Fig. 6d, the left one is 11 mm and the right one is 9 mm. In addition, the experimental system installation of different diameter disc cutter and assembly process just as Fig. 7 shown.
5.1. Install different cutters The composite wear test device model has the function of installing different types of cutters. Firstly, the model can install with different diameter cutters. As showed in Fig. 6(a), the diameter of left cutter is 4.32 cm when the right cutter is 3.81 cm. Secondly, the model can install with different width and shape of the cutter edge. Fig. 6(b) shows two 95
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diameter N of sliding steel needle was measured under high definition digital microscope, the abrasiveness index CAI is obtained by the following formula:
CAI =
N * 2000 80.591 * 100
(1)
Fig. 10c is the tip of a steel tool under high definition digital microscope before the test, Fig. 10d is the tip of a steel tool under high definition digital microscope after the test. The parameters of small size cutter: Diameter 4.32 cm; cutter edge width 1.9 mm; flat cutter edge; hardness: the surface hardness of cutter is 56-60HRC, when the hardness of the heart of the cutter is 50-56HRC. The experimental scheme is to set the penetration of each stroke of the cutter is 0.05 mm, each stroke is 250 mm, the wear weight of cutter is measured once every 400 S (namely the total stroke is 100 m, the final penetration is 5 mm), after weighted three times and got the average value of wear weight. The obtained data were shown in Table 2. By the above rock abrasion servo experimental instrument experimental (CAI test) and the cutter wear experiment (One hundred meters wear testing), the CAI values of different rock samples and the wear weight per 100 m of cutter (replace with W) were obtained. Through calculation and linear fitting, the relationship between W and CAI is obtained:
Fig. 8. The rock samples with different CAI: (a) diorite; (b) limestone; (c) griotte; (d) granite; (e) sandstone; (f) rock of Yin Han Ji Wei project.
W= 0.7845 × CAI2
(2)
Namely, the wear weight per 100 m of cutter is proportional to the square of CAI value. The formula is based on Table 2 data obtained by linear fitting and the linear fitting curve as showed in Fig. 11. However, it is inaccurate to predict the wear degree of the cutter only by CAI for the rock faced in practical engineering. The effects of uniaxial compressive strength (UCS), tensile strength (TS) and equivalent quartz content (EQC) should be considered simultaneously. In view of this, we have considered the effect of UCS and EQC at the same time. According to reference (Prieto, 2012), the product of EQC and UCS of rock is Rock Abrasivity Index (RAI), namely
Fig. 9. The ATA-IGG I type rock abrasion servo experimental instrument of State Key Laboratory of Shield Machine and Boring Technology.
RAI = UCS × EQC Table 1 The CAI data of different rock samples. Rock name
griotte
diorite
limestone
sandstone
granite
sample f
CAI
1.48
1.58
1.67
2.51
4.31
3.87
The mineral composition of rock can be obtained through rock slice identification experiment or material composition diffraction experiment. EQC of rocks are calculated by the method of reference (Yang et al., 2012). The UCS of rocks were obtained by uniaxial compression experiment with MTS experimental machine. The CAI of rocks were tested by ATA-IGG I type rock abrasion servo experimental instrument. 27 groups of rock samples were taken from the construction site of a diversion tunnel in Qinling Mountains, a railway tunnel in Guangzhou and a diversion tunnel in Northeast China. The results of the tests are shown in Table 3, and the relationship curve between CAI and RAI is fitted as shown in Fig. 12. As can be seen from Fig. 12, the rock abrasion index CAI has a positive logarithmic relationship with RAI, and the two indexes are highly correlated, the correlation coefficient is 0.956. That is to say, the prediction model seems to only consider the impact of CAI, but actually consider the comprehensive impact of CAI, UCS, EQC, TS and so on.
5.2. Wear ability prediction of different rocks In order to verify the abrasiveness of rocks with different CAI values, Using the experimental model, had done the wear tests of granite, sandstone, diorite, limestone, griotte and rocks of Yin Han Ji Wei project, in this paper, rock of Yin Han Ji Wei project can replace by “sample f”(Fig. 8). The CAI values of different rocks were measured by ATA-IGG I type rock abrasion servo experimental instrument of State Key Laboratory of Shield Machine and Boring Technology. As showed in Fig. 9, the rock abrasion servo experimental instrument experimental process can be controlled by servo, and analysis of the interaction between rock and machine in the whole abrasion process. The test method is easy and non-destructive, and the experimental results can wonder reflect the wear ability of the rock to cutter. The CAI data was shown in Table 1. The method for obtaining the data of Table 1 as follows: First of all, put the rock sample in the fixture and makes the tip of a steel tool (4550HRC) close to it, just as the Fig. 10a shown. The tip of steel moves at the surface of the rock sample at a displacement rate of 10 mm/min, the total displacement is 10 mm, taking into account the anisotropy of rock, two scratch tests were made on the surface of the sample, and the angle between the two scratches was 90 degrees (Fig. 10b). The wear
5.3. The prediction methods of cutter wear 5.3.1. The prediction methods based on W Depending on the results of laboratory experiments, in the case of no abnormal wear, the mass loss is proportional to the cutter's rolling distance. Using this principle, through collecting rock samples from the proposed projects and projects have been built to carry out the small size cutter wear experiments, then measured their W respectively. Then through the actual use of the cutter in the construction site, the cutter consumption of the proposed project is predicted by analogy, just as the following Eq. (3). 96
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Fig. 10. The core test device of ATA-IGG I type rock abrasion servo experimental instrument (a); the rock sample after the test (b); the tip of steel tool under high definition digital microscope before the test (c); the tip of steel tool under high definition digital microscope after the test (d).
represents the wear weight per 100 m of small size cutter for the proposed projects.
Table 2 The wear weight per 100 m of cutter for different rock samples. Rock name
Griotte
Diorite
Limestone
Sandstone
Wear times first time second time third time average value
The wear weight per 100 m of cutter(mg) 1 2 3 3 1 1 2 3 2 1 2 4 1.3 1.7 2.3 3.4
Granite
Sample f
9 9 10 9.3
8 8 8 8.0
5.3.2. The auxiliary prediction methods based on CAI According to the experimental results of rock abrasion servo experimental and small size cutter wear test, W is proportional to the square of CAI. By using this rule, first we can test the CAI values of rock Table 3 Test results of geological sensitive indexes.
Fig. 11. The fitting curve of W and CAI2.
W1 = W2
2 2
(3)
In the above equation, W1 represents the service life of a single cutter for the projects which have been built, can be measured by rockbroken volume of per cutter. W2 represents the service life of a single cutter for the proposed projects; ω1 represents the wear weight per 100 m of small size cutter for the projects which have been built, ω2
Number
Rock properties
UCS/MPa
EQC/%
CAI/(×10−1 mm)
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 C1 C2 C3 C4 C5
granite diorite granite granite granite granite granite granite granite schist granite pelitic siltstone pelitic siltstone pelitic siltstone pelitic siltstone sandstone sandstone sandstone sandstone sandy mudstone sandy mudstone sandy mudstone diorite granite limestone limestone limestone
103 180 210 123 125 140 193 185 160 192 173 16 23 24 26 34 45 40 44 8 11 13 59 37 69 52 89
44.2 51.9 45.0 44.2 48.4 45.0 46.5 45.0 49.2 36.9 50.0 49.0 48.9 33.9 41.8 63.0 66.0 89.6 58.0 24.1 29.9 32.1 30.2 40.3 26.1 28.0 15.8
3.35 3.68 3.87 3.51 3.53 3.59 3.54 3.73 3.42 3.63 3.76 1.00 1.40 1.11 1.32 2.82 3.04 3.08 2.98 0.34 0.59 0.78 2.39 2.11 2.10 1.81 1.77
Notes: A1-A11 from a diversion tunnel in Qinling Mountains; B1-B11 from a railway tunnel in Guangzhou; C1-C5 from a diversion tunnel in Northeast China. 97
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each cutter is 124 m3. Based on Table 5, the results of wear test of small size cutter for different projects rock, when the penetration of each stroke of the small size cutter is 0.05 mm, the average W of Beijing nuclear industry rock samples is 9.0 mg, the average W of Gao Li Gong Shan tunnel is 3.3 mg, when the average W of Yin Han Ji Wei is 18.8 mg. Assume that for the same rock sample, the W is proportional to the rock-broken volume, the following result can be obtained: By predicting the weight of cutter wear, it is preliminarily proved that the service life of a cutter in the nuclear engineering proposed project will be x1 (259 m3). Excavation diameter of the proposed project is 7.6 m. The prediction consumption of cutter in the nuclear engineering proposed project will be 5.7 m/handful by calculating. The average service life of a cutter in the Gao Li Gong Shan tunnel project will be x2 (706 m3) rock-broken. This prediction result is very consistent with the service life of the cutter at the site, and the error is only 3.9%. As shown in Fig. 13. Fig. 13 (a) is the “Caiyun TBM” with diameter 9.03 m in the tunnel of Gao Li Gong Shan. Fig. 13 (b) for statistical cutter cumulative wear of TBM digging 1500 m, by calculating the actual rock-broken volume of the single cutter is 735 m3. Therefore, by comparing the predicted results with the actual working conditions, it is proved that the model has high accuracy and strong operability.
Fig. 12. The fitting relationship between CAI and RAI.
samples, which be collected from the projects have been built and the proposed projects. Then through the actual use of the cutter in the construction site, the cutter consumption of the proposed project is predicted by analogy, just as the following Eq. (4).
CAI22 W1 = W2 CAI12
6. Conclusions
(4)
The composite wear test device model was successfully designed and manufactured in this paper; The operational principle of the composite wear test device model was analyzed; At the same time, the support project of Yin Han Ji Wei was introduced. In addition, the wear tests of small size cutters of different sizes and different types rock samples were carried out. The quantitative relationship between the wear rate of the cutter and the CAI, UCS and EQC value of rock abrasiveness was established. Based on the wear weight of small size cutter and the index of abrasiveness of rock, two prediction methods of cutter life were established, and which were applied by a commissioned experiment of the proposed project of Beijing nuclear industry and Gao Li Gong Shan tunnel project. Finally, by comparing the predicted results with the actual working conditions, it is proved that the model has high accuracy and strong operability.
In the above equation, W1 represents the service life of a single cutter for the projects which have been built, can be measured by rockbroken volume of per cutter. W2 represents the service life of a single cutter for the proposed projects; CAI1 represents the index of rock abrasiveness for the projects which have been built; CAI2 represents the index of rock abrasiveness for the proposed projects. 5.3.3. The application of cutter wear prediction method The actual use situation of cutters in the front 2000 m test section of Yin Han Ji Wei TBM project just as showed in the Table 4. Through the analysis on Table 4, the actual consumption of cutter in front 2000 m of Yin Han Ji Wei TBM project is 2.46 m/handful. The excavation diameter of TBM is 8.03 m. The calculation results show that the life of
Table 4 The use situation of cutters in the front 2000 m test section about Yin Han Ji Wei project. Boring stage
Boring distance (m)
Number of cutter change
Use amount of new cutter (handful)
Replacement rate of new cutter (%)
Abnormal ware (time)
Abnormal ware rate
Boring distance of each cutter (m/handful)
Stage 1 Stage 2 Stage 3 Stage 4 Total value
500 500 500 500 2000
401 463 363 466 1693
208 196 185 225 814
51.9 42.3 50.1 48.2 48.1
92 46 16 56 210
22.9% 9.9% 4.4% 12% 12.4%
2.40 2.55 2.70 2.22 2.46
Table 5 The results of wear test of small size cutter for different projects. Projects
W (the first time) W (the second time) W (the third time) W (the fourth time) W (the fifth time) W (the sixth time) average value
Yin Han Ji Wei
Proposed project of Beijing nuclear industry
Proposed project of Gao Li Gong Shan tunnel
20 mg 16 mg 18 mg 21 mg 25 mg 13 mg 18.8 mg
10 mg 9 mg 10 mg 10 mg 7 mg 8 mg 9.0 mg
4 mg 3 mg 3 mg 4 mg 3 mg 3 mg 3.3 mg
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Fig. 13. The “Caiyun TBM” with diameter 9.03 m(a); Statistical cutter cumulative wear of TBM digging 1500 m.
Acknowledgements
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We gratefully acknowledge the financial support by the Science and Technology Research and Development plan of China Railway Corporation (2016G004-A); Science and Technology Innovation Plan of China Railway Tunnel Group (2015-18; 2016-03; 2018-06); Integrated geological survey instrument for quantitative prediction of unfavorable geology in tunnel construction (51327802); Research on Shield Tunnelling Attitude Control Model under Complex Tunnel Linear Condition (51478146); Science and Technology Innovation Plan of China Railway Construction Investment Co., Ltd. (2016) 01-3; Research topics of Shenzhen Metro Group (ZHDT-KY035/2017); The Opening Subject of State Key Laboratory of Shield Machine and Boring Technology (SKLST-2018-k03); Henan Province Natural Science Foundation (182300410197). References Alber, M., 2008. Stress dependency of the Cerchar abrasivity index (CAI) and its effects on wear of selected rock cutting tools. Tunn. Undergr. Space Technol. 23 (4), 351–359. Chen, L., Yan, H., Hu, Z., et al., 2011. A surver on abrasion of disc cutters domestic shield machines. Heat Treat. Technol. Equip. 32 (3), 52–56. Farrokh, E., Rostami, J., 2009. Effect of adverse geological condition on TBM operation in Ghomroud tunnel conveyance project. Tunn. Undergr. Space Technol. Incorporp. Trench. Technol. Res. 24 (4), 436–446. Frenzel, C., 2012. Modeling uncertainty in cutter wear prediction for tunnel boring machines. GeoCongress. ASCE 2012, 3239–3247. Hassanpour, J., 2018. Development of an empirical model to estimate disc cutter wear for sedimentary and low to medium grade metamorphic rocks. Tunn. Undergr. Space Technol. 75, 90–99. Hassanpour, J., Rostami, J., Azali, S.T., et al., 2014. Introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rocks; a case history of Karaj water conveyance tunnel. Iran. Tunn. Undergr. Space Technol. 43 (7), 222–231. Hassanpour, J., Rostami, J., Zhao, J., et al., 2015. TBM performance and disc cutter wear prediction based on ten years experience of TBM tunnelling in Iran. Geomechanik Und Tunnelbau 8 (3), 239–247. Ko, T.Y., Kim, T.K., Son, Y., et al., 2016. Effect of geomechanical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunnelling. Tunn. Undergr. Space Technol. 57, 99–111.
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