Data analysis for the assessment of roadheader performance in carbonate rocks

Toluwase Daniel Olaiya; Saffet Yagiz; Zhan-ping Song; Zhilin Cao; De-Sai Guo

doi:10.68732/741a4e016c95e896

Paper contribution

Data analysis for the assessment of roadheader performance in carbonate rocks

Toluwase Daniel Olaiya; Saffet Yagiz; Zhan-ping Song; Zhilin Cao; De-Sai Guo

DOI: https://doi.org/10.68732/741a4e016c95e896
Proceedings: EUROCK 2026
Pages: 907-913
Submitted: 28 January 2026
Accepted: 15 June 2026
Published: 12 August 2026

Summary

Abstract

Utilizing roadheaders (RH) and estimating their cutting rate in rock excavation are crucial for effective scheduling in mechanized mining and civil construction projects. Roadheaders are widely used for tunnelling in civil engineering and are commonly applied in coal mining for production and for driving drifts to access ore bodies. They are typically suitable for excavating weak to medium-strength rocks (20–160 MPa); however, with advances in technology, they can now be used in non-abrasive rocks with uniaxial compressive strength (UCS) of up to 200 MPa. In this study, a 4 km-long tunnel excavated in China using an axial roadheader is examined. The analysis considers intact rock properties (UCS and Brazilian tensile strength), rock mass properties (frequency and features of discontinuities), and machine specifications (e.g., cutting power) to evaluate RH performance. A dataset comprising 1,755 data points, including field and laboratory measurements as well as machine parameters, was compiled and analyzed. The relationships between the input variables and the instantaneous cutting rate (ICR) were then investigated, and empirical models are proposed for practical applications. The results indicate that ICR can be predicted more accurately when intact rock properties, rock mass characteristics, and machine parameters are considered together, thereby improving project scheduling in both mining and civil engineering applications.

Contributors

Authors

Toluwase Daniel Olaiya School of Mining and Geoscience, Nazarbayev University, Astana 01000 Kazakhstan; Rock Excavation and Automation Research (REAR) Group, Nazarbayev University, Astana 010000 Kazakhstan
Saffet Yagiz School of Mining and Geoscience, Nazarbayev University, Astana 01000 Kazakhstan; Rock Excavation and Automation Research (REAR) Group, Nazarbayev University, Astana 010000 Kazakhstan
Zhan-ping Song Institute for Interdisciplinary and Innovation Research, Xi'an University of Architecture and Technology, China; School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055 China; Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering, Xi’an Shaanxi 710055 China
Zhilin Cao Institute for Interdisciplinary and Innovation Research, Xi'an University of Architecture and Technology, China; Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering, Xi’an Shaanxi 710055 China
De-Sai Guo College of Civil Engineering, Hunan University, Changha 410082 China

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Works cited

References

1
UCS is the only parameters. No specific rocks given
250 kW
Axial RH
Gehring (1989)
2
Different type of rocks, k is 0.45 for Axial RH and 0.55 for T-RH in general
LCM tests
Rostami et al. (1994)
3
Sedimentary rocks from siltstone, mudstone to sandstone with some dyke
95 kW
Axial RH
Bilgin et al. (1996)
4
(Eq.1)
(Eq.2)
d=5 mm for (Eq. 1)
d=9 mm for (Eq. 2)
variety of chromite, copper ore, serpentinite, clay, sand, lime, silt stones, tuff, trona, gypsum etc.
LCM tests
Balci et al. (2004)
5
Clayey lime, claystone, siltstone, coal, marl, tuff
112 kW
Axial RH
Keles (2005)
6
Limestone, sandstone, siltstone, andesite, diabase
90 kW
Axial RH
Tumac et al. (2007)
7
Siltstone, sandstone, mudstone interfaces with coal seam
82 kW
Axial RH
Ebrahimabadi et al. (2011)
8
Clayey limestone, coal, claystone, mudstone
112 kW
Axial RH
Kahraman et al.(2019)
9
Ignimbrites, pyroclastic volcano-sedimentary rocks
200 kW
Axial RH
Comakli (2019)
10
Limestone and Dolomite
260 kW
Axial RH
This Study (2026)
Note:ICR: Instantaneous cutting rate; UCS: Uniaxial compressive strength; RMCI: Rock mass cuttability index; P: Power; SEopt: Optimum Specific Energy; k: Constant coefficient; RPI: Roadheader penetration index; NPI: Needle penetration index; RMBI: Rock mass brittleness index; BTS: Brazilian tensile strength; LCM: Linear Cutting Test; RH: Roadheader; RQD: Rock Quality Designation; α is the angle between the plane of weakness and RH direction.
Among the earliest attempts to estimate roadheader cutting performance using intact rock parameters, Gehring (1989) introduced a prediction model for an axial and transverse type roadheader. Thuro and Plinninger (1999) investigated the influence of geological and geotechnical parameters on the cutting performance of 132 kW roadheader in the Zeulenroda sewage tunnel, introducing an ICR model as a function of the UCS. Çopur et al. (2001) developed a model for optimum specific energy of a RH in a chromite mine in Turkey. Tumac et al. (2007) also proposed some ICR models based on hardness values and intact rock UCS. In a comparative study on the performance of different excavation methods, Ocak and Bilgin (2010) also developed an ICR model based on UCS. Their findings further emphasized that performance prediction models should consider orientation and frequency of discontinuity in rock mass. The model developed by Rostami et al. (1994), which estimates ICR based on machine cutting power and SEopt, is often regarded as an accurate model. Also, Comakli et al. (2014) examined the RH performance and conical pick cutter consumption for different ore types.
Most models developed in the literature are based on RH power and intact rock strength, such as UCS and BTS. However, it is well known that fracture frequency and orientation of discontinuities within the rock mass also have a significant influence on RH performance, cutter consumption, and project scheduling, and therefore should be properly considered from the early stages of excavation projects. In this study, a preliminary data processing and RH performance evaluation framework is presented as a basis for the development of new empirical models for predicting the instantaneous cutting rate of RH.

[1] 1

[2] UCS is the only parameters. No specific rocks given

[3] 250 kW

[4] Axial RH

[5] Gehring (1989)

[6] 2

[7] Different type of rocks, k is 0.45 for Axial RH and 0.55 for T-RH in general

[8] LCM tests

[9] Rostami et al. (1994)

[10] 3

[11] Sedimentary rocks from siltstone, mudstone to sandstone with some dyke

[12] 95 kW

[13] Axial RH

[14] Bilgin et al. (1996)

[15] 4

[16] (Eq.1)

[17] (Eq.2)

[18] d=5 mm for (Eq. 1)

[19] d=9 mm for (Eq. 2)

[20] variety of chromite, copper ore, serpentinite, clay, sand, lime, silt stones, tuff, trona, gypsum etc.

[21] LCM tests

[22] Balci et al. (2004)

[23] 5

[24] Clayey lime, claystone, siltstone, coal, marl, tuff

[25] 112 kW

[26] Axial RH

[27] Keles (2005)

[28] 6

[29] Limestone, sandstone, siltstone, andesite, diabase

[30] 90 kW

[31] Axial RH

[32] Tumac et al. (2007)

[33] 7

[34] Siltstone, sandstone, mudstone interfaces with coal seam

[35] 82 kW

[36] Axial RH

[37] Ebrahimabadi et al. (2011)

[38] 8

[39] Clayey limestone, coal, claystone, mudstone

[40] 112 kW

[41] Axial RH

[42] Kahraman et al.(2019)

[43] 9

[44] Ignimbrites, pyroclastic volcano-sedimentary rocks

[45] 200 kW

[46] Axial RH

[47] Comakli (2019)

[48] 10

[49] Limestone and Dolomite

[50] 260 kW

[51] Axial RH

[52] This Study (2026)

[53] Note:ICR: Instantaneous cutting rate; UCS: Uniaxial compressive strength; RMCI: Rock mass cuttability index; P: Power; SEopt: Optimum Specific Energy; k: Constant coefficient; RPI: Roadheader penetration index; NPI: Needle penetration index; RMBI: Rock mass brittleness index; BTS: Brazilian tensile strength; LCM: Linear Cutting Test; RH: Roadheader; RQD: Rock Quality Designation; α is the angle between the plane of weakness and RH direction.

[54] Among the earliest attempts to estimate roadheader cutting performance using intact rock parameters, Gehring (1989) introduced a prediction model for an axial and transverse type roadheader. Thuro and Plinninger (1999) investigated the influence of geological and geotechnical parameters on the cutting performance of 132 kW roadheader in the Zeulenroda sewage tunnel, introducing an ICR model as a function of the UCS. Çopur et al. (2001) developed a model for optimum specific energy of a RH in a chromite mine in Turkey. Tumac et al. (2007) also proposed some ICR models based on hardness values and intact rock UCS. In a comparative study on the performance of different excavation methods, Ocak and Bilgin (2010) also developed an ICR model based on UCS. Their findings further emphasized that performance prediction models should consider orientation and frequency of discontinuity in rock mass. The model developed by Rostami et al. (1994), which estimates ICR based on machine cutting power and SEopt, is often regarded as an accurate model. Also, Comakli et al. (2014) examined the RH performance and conical pick cutter consumption for different ore types.

[55] Most models developed in the literature are based on RH power and intact rock strength, such as UCS and BTS. However, it is well known that fracture frequency and orientation of discontinuities within the rock mass also have a significant influence on RH performance, cutter consumption, and project scheduling, and therefore should be properly considered from the early stages of excavation projects. In this study, a preliminary data processing and RH performance evaluation framework is presented as a basis for the development of new empirical models for predicting the instantaneous cutting rate of RH.