Effect of geomechanical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunnelling

Ko, Tae Young; Kim, Taek Kon; Son, Young–Jin; Jeon, Seokwon

doi:10.1016/j.tust.2016.02.006

Cited by 62 publications

(11 citation statements)

References 14 publications

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“…Similarly, several authors (i.e. [13,29,34,65]) investigated the relation between CAI and UCS and they found meaningful correlations between these two parameters. Hence, it is impossible to use both = arcsin(sin f × sin( t − s )) r = s xy ∕s x s y UCS and CAI in the same model.…”

Section: Multiple Regression Analysesmentioning

confidence: 90%

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Gökçeoğlu

2021

SN Appl. Sci.

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One of the most important issues in tunnels to be constructed with tunnel boring machines (TBMs) is to predict the excavation time. Excavation time directly affects tunnel costs and feasibility. For this reason, studies on the prediction of TBM performance have always been interesting for tunnel engineers. Therefore, the purpose of the study is to develop models to predict the rate of penetration (ROP) of TBMs. In accordance with the purpose of the study, a new database including 5334 cases is obtained from the longest railway tunnel of Turkey. Each case includes uniaxial compressive strength, Cerchar Abrasivity Index, α angle, weathering degree and water conditions as input or independent variables. Two multiple regression models and two ANN models are developed in the study. The performances of the ANN models are considerably better than those of the multiple regression equations. Before deep tunnel construction in a metamorphic rock medium, the ANN models developed in the study are reliable and can be used. In contrast, the performances of the multiple regression equations are promising, but they predict lower ROP values than the measured ROP values. Consequently, the prediction models for ROP are open to development depending on the new data and new prediction algorithms.

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Section: Multiple Regression Analysesmentioning

confidence: 90%

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Gökçeoğlu

2021

SN Appl. Sci.

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“…158 [30] Calcareous schist 50.80 1.22 198 [31] Gneiss (30) 66.00 2.07 159 [30] Calcareous schist 71.60 1.42 199 [29] Gneiss (35) 125.60 4.00 160 [30] Calcareous schist 31.90 1.14 200 [32] Gneiss (27) 149.50 4.20 161 [28] Ortho-Gneiss 114.00 3.00 201 [32] Gneiss (20) 204.90 4.50 162 [28] Ortho-Gneiss 125.00 3.50 202 [32] Schist (50) 68.60 4.30 163 [28] Meta-Andesite 128.00 3.90 203 [32] Schist (19) 34.70 2.30 164 [28] Granitic Gneiss 146.00 3.70 204 [32] Schist (37) 55. 20 3.60 165 [28] Para-Gneiss 117.00 3.80 205 [32] Schist (26) 50.00 2.90 166 [28] Para-Gneiss 135.00 3.50 206 [32] Schist (35) 39.90 2.90 167 [28] Calc Silicate 201.00 4.10 207 [32] Schist (39) 19.80 2.50 168 [28] Calc Silicate 153.00 3.90 208 [32] Schist ( 14) 47.70 3.40…”

Section: Tab 3 Sedimentary Rocks Datamentioning

confidence: 99%

“…169 [33] Slate (31) 164.60 0.86 29 [32] Schist (35) 13.60 2.20 170 [27] Marble 38.00 1.00 210 [32] Schist (30) 108.90 4.00 171 [18] Gneiss 115.80 2.68 211 [32] Schist (30) 48.70 2.50 172 [18] Gneiss 162.20 2.95 212 [32] Schist (55) 20.60 2.70 173 [18] Gneiss 153.10 3.03 213 [32] Schist (23) 27.00 2.90 174 [18] Amphibole 121.50 3.18 214 [32] Schist ( 14) 80.70 3.10 175 [18] Propylite 163.50 3.22 215 [32] Schist (17) 46.80 2.40 176 [18] Quartzite 164.80 4.90 216 [32] Schist (26) 65.00 2.90 177 [8] Phyllite 54.30 1.43 217 [32] Schist ( 16) 46.70 2.90 178 [8] Granitic Gneiss 69.20 2.87 218 [32] Schist (18) 53.30 3.00 179 [8] Granitic Gneiss 54.50 4.95 219 [32] Schist (60) 44.80 2.70 180 [8] Quartzite 56.40 4.39 220 [32] Schist (56) 39.00 2.20…”

Section: Tab 3 Sedimentary Rocks Datamentioning

confidence: 99%

“…181 [8] Quartzite 147.00 4.13 221 [32] Schist (57) 27.70 2.20 182 [34] Marble 102.90 1.73 222 [32] Schist (29) 21.60 1.50 183 [34]] Marble 90.50 1.25 223 [32] Schist (25) 112.00 3.60 184 [34] Marble 112.45 1.36 224 [32] Schist (16) 107.50 3.00 185 [34] Marble 119.50 1.52 225 [32] Schist (50) 118.80 3.30 186 [34] Marble 123.40 2.20 226 [32] Schist (12) 112.40 3.00 187 [34] Marble 131.10 2.28 227 [32] Schist (21) 108.60 3.00 188 [34] Marble 110.50 1.78 228 [32] Schist (16) 72.50 2.50 189 [34] Marble 149.90 2.21 229 [32] Schist (12) 112.80 2.70 190 [34] Marble 87.30 1.13 230 [32] Schist (19) 83.70 3.00 191 [34] Marble 45.70 0.79 231 [32] Schist (17) 112.80 3.80…”

Section: Tab 3 Sedimentary Rocks Datamentioning

confidence: 99%

“…192 [34] Marble 59.40 0.80 232 [32] Schist (38) 62.10 2.80 193 [34] Marble 130.90 2.13 233 [32] Schist (22) 78.60 2.30…”

Section: Tab 3 Sedimentary Rocks Datamentioning

confidence: 99%

See 2 more Smart Citations

A statistical approach for the correlation between Cerchar Abrasivity Index and Uniaxial Compressive Strength of rocks

Giovanni

Todaro

Cardu

2023

Geomechanics and Tunnelling

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In any underground excavation project, choosing the best excavation technique and careful preliminary assessments of wear costs and production rates are crucial aspects to consider. The production rate and costs of tools, as well as the frequency of tool replacement, deeply affect the duration and cost‐effectiveness of the whole project. Among the rock‐specific characteristics affecting tool wear, Cerchar abrasivity index (CAI) and uniaxial compressive strength (UCS) play an important role. In this article, the relationship between these two parameters was examined by regression analyses for 237 different rocks with the aim to define a preliminary and expected wear potential of three groups of rocks: sedimentary, metamorphic and igneous rocks. The analysis of the results showed that 86 % of the igneous rocks analysed have a CAI > 2, which is a medium or high level of abrasiveness. In sedimentary rocks, over 70 % of the samples are characterised by low abrasiveness behaviour (CAI < 2). Results obtained for these rocks can represent an instrument for predicting the abrasiveness potential of a rock when the UCS is known associating it with an expected range value of CAI. Metamorphic rocks, on the other hand, cannot be classified in specific ranges of UCS and CAI. For this rock group, therefore, a univocal tool for forecasting could not have been obtained, but analyses of rock type and quartz content show that the mineralogical content cannot be considered the only parameter for a wear analysis.

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A gene expression programming-based method for real-time wear estimation of disc cutter on TBM cutterhead

Tao

Qin

et al. 2022

Neural Comput & Applic

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Effect of geomechanical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunnelling

Cited by 62 publications

References 14 publications

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

A statistical approach for the correlation between Cerchar Abrasivity Index and Uniaxial Compressive Strength of rocks

A gene expression programming-based method for real-time wear estimation of disc cutter on TBM cutterhead

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