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Proceedings of the World Tunnel Congress 2014 – Tunnels for a better Life. Foz do Iguaçu, Brazil. 
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1 INTRODUCTION 
TBM selection process is intertwined with 
multiple criteria and a variety of complex 
factors in addition to the engineering standards. 
The process for the selection of machine 
which is efficient and suitable for site conditions 
requires reasonable and reliable decisions. 
However, the current TBM selection process 
merely considers ground conditions, so it does 
not reflect the practical conditions on the 
feasible site, adjacent structures, obstacles and 
machine prices. 
In addition, the guide presented by ITA and 
its related entities consider only geological 
conditions and, it consists of the range type. So 
it needs the engineering judgement in the 
process of TBM selection (Figure 1&2). 
In recent years, researches related to the 
Tunneling and Underground Space Technology 
have been conducted, but they do not reflect 
construction conditions and environment due to 
complex selection criteria, subdivided machine 
and only consideration of ground conditions 
(Taheri, 2008, Abdolreza, 2012, Shahriar, 
2007). 
In this regard, this study propose a 
methodology possible to perform a more 
realistic TBM selection that reflects 
construction conditions and environment and 
includes geological, environment and cost 
conditions by applying the AHP (Analytic 
Hierarchy Process) technique. 
 
2 AHP TECHNIQUE 
The analytic hierarchy process AHP 
developed and presented by Saaty (1977) is a 
multi-criteria decision making method that can 
be utilized in the evaluation of alternatives by 
reflecting a variety of evaluation factors 
comprehensively. The AHP can be divided into 
four stages such as the establishment of 
hierarchical structure of alternatives and 
evaluation factors, calculation of the weights of 
evaluation factors by pair-wise comparison, 
estimation of evaluation factor scores of 
alternatives, and calculation of evaluation scores 
of alternatives in which weights are applied. 
In the AHP, measurement and data collection 
is done through a pair-wise comparison on the 
elements (Objective, Criteria, Sub-Criteria, 
Alternative) for each level of hierarchical 
structure. The pair-wise comparison is an 
operation to evaluate the relative importance of 
two elements from the viewpoint of the adjacent 
upper level elements, and the evaluation result 
is represented in the form of a matrix. The 
element value of this matrix (aij), represents the 
relative importance of element i on the element 
TBM selection methodology using the AHP (Analytic Hierarchy 
Process) Method 
Joon-Geun Oh 
Korea University, Seoul, Korea / Korea Railroad Research Institute, Uiwang, Korea. 
Myoung Sagong and Jun S. Lee 
Korea Railroad Research Institute, Uiwang, Korea. 
Sang-Hwan Kim 
Hoseo University, Asan, Korea. 
ABSTRACT: In this study, TBM selection methods to meet soil and site conditions were presented. 
Factors and excavation equipment affecting TBM selection by soil and environmental condition were 
selected and classified. Weights between equipment and influencing factors selected were calculated 
by applying the AHP method. The results of the analysis influence factors, Ground condition was a 
major factor in objective factors and strength was a major factor in the hard condition of criteria 
factors and water pressure was a major factor in the soft ground condition of criteria factors. In 
Environment condition, existence of adjacent structures was evaluated more important than existence 
of feasible site. Lastly, Adequacy was verified through the deduction of results that coincide with 
input equipment by applying derived weights to actual site conditions. 
Proceedings of the World Tunnel Congress 2014 – Tunnels for a better Life. Foz do Iguaçu, Brazil. 
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j. This matrix is assumed to satisfy reflectivity 
(aii=1) and reciprocity (aij=1/aji). Therefore, in 
case the number of elements is n, a pair-wise 
comparison of n(n-1)/2 is needed. For example, 
15th(652) times of the pair-wise comparison is 
required on 6 evaluation factors in the 
hierarchical structure. Meanwhile, the pair-wise 
comparison made by expert judgment may not 
be fully consistent. 
Saaty(1977) presented a method to use 
normalized eigenvectors corresponding Ŵ to the 
maximum eigenvaues λ max of the pair-wise 
comparison matrix Ȃ as weights of elements. 
The maximum eigenvalue of the pair-wise 
comparison matrix with complete consistency is 
equal to the number of elements n to be 
compared. 



m
1i i
i
max W
WA
m
1λ 

, (1) 
Thus, (λmax - n) can be used as an indicator that 
represents the level of consistency of the pair-
wise comparison. Saaty(1980) suggested the 
consistency ratio(CR) proposed them as 
measures used to determine the level of 
consistency. 
1n
nλ
CI max
 , (2) 
)
RI
1
)(
1n
nλ
(
RI
CI
CR max
 , (3) 
 (wherein, RI is a value obtained from a random 
pair-wise comparison matrix) 
Table 1. RI values for different values of n (Saaty, 1980) 
n 1 2 3 4 5 6 7 8 9 10
R
I
0.
00 
0.
00
0.
58
0.
09
1.1
2
1.2
4 
1.3
2 
1.4
1 
1.4
5
1.4
9
 
Here, RI refers to random inconsistency 
index, and its value varies depending on the size 
of the pair-wise comparison index. Table 1 
shows RI values when the size of the pair-wise 
comparison matrix ranges from 1 to 10. 
In general, the pair-wise comparison matrix 
in which the consistency ratio is less than or 
equal to 0.1 is consider to be have no problems 
in consistency. 
 
3 SELECTION OF INFLUENCING 
FACTORS 
3.1 Selection of machine for comparison 
TBM can be classified in various ways 
according to such main criteria as 1) the 
presence of the shield, 2) supporting system, 3) 
reaction force, and the type of machine varies. 
In case of a comparison of mechanical 
excavation machine as alternatives, the number 
of the pair-wise comparisons used in this study 
increases exponentially, so five kinds of 
machine are selected as machine with which to 
compare based on the two machine such as 
general machine which is commonly used and 
machine that can be easily recognized by 
surveyees as shown in Table 2. 
 
Table 2. Machine for comparison 
Shield Supporting system Reaction Force Machine Type 
1 Non-Shield None Gripper Open TBM 
2 Shield Face without Support Gripper + Segment Double Shield TBM 
3 Shield Face without Support Gripper or Segment Single Shield TBM 
4 Shield 
Face with earth pressure 
balance support 
Segment EPB M 
5 Shield Face with fluid support Segment Slurry M 
Proceedings of the World Tunnel Congress 2014 – Tunnels for a better Life. Foz do Iguaçu, Brazil. 
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3.2 Selection of influencing factors 
Factors affecting the TBM selection are 
divided into hard/soft ground conditions in the 
case of ITA Guidelines. In hard rock condition, 
tensile strength, RQD, joint spacing and 
uniaxial compressive strength of the rock are 
considered to be major factors (Figure 1). 
The German tunnel association (Deutscher 
Ausschuss für unterirdisches Bauen, DAUB) 
divided influencing factors into hard rock and 
soft ground conditions as in the case of ITA 
criteria. The soil condition includes particle size 
distribution, permeability coefficient, adhesion, 
water pressure and swelling as influencing 
factors, and it is characterized by abrasivity test 
of the soil, like abrasiveness LCPC index ABR 
(g/t), Breakability LCPC Index BR (%). The 
rock condition has its characteristics in that it 
includes compressive/tensile strength, RQD, 
water consumption per 10m of RMR tunnel, 
swelling, water pressure and CAI abrasivity test 
as influencing factors. The scope of the each 
factoris shown in the Figure 2. 
In a situation where influencing factors that 
only considers ground conditions are presented, 
this study attempts to identify factors affecting 
the selection of machine primarily by adding 
them to ground conditions to be expanded to 
include environmental and price conditions. 
Secondarily, classify and select influencing 
factors for each condition under the first 
classification on the basis of expert consultation 
and standards and guidelines presented in each 
institution. 
The range of influence of each influencing 
factors selected by third classification is also 
selected by splitting into two based on the 
expert consultation as well as guidelines and 
standards which are currently presented in each 
institution. The classification of the influencing 
factors is shown in Table 3. Figure 3 shows 
evaluation factors, which are influencing factors 
classified in Table 3 with machine for 
comparison selected in Table 2 as alternative 
and hierarchization between influencing factors 
and machine. 
Table 3. Classification of influencing factors on TBM selection 
Objective Criteria Sub-Criteria 
Geology 
Hard 
Rock 
Rock Compressive Strength 
300Mpa ~ 50Mpa
50Mpa ~ 5Mpa
RQD 
100% ~ 50% 
50% ~ 10% 
Fissure Spacing 
>2.0m ~ 0.6m 
0.6m ~ 0.06m 
Fault zone 
Existence 
None 
Water in flow per 10m tunnel 
≥25ℓ 
< 25ℓ 
Soft 
Ground 
Cohesion 
≥30kPa 
30 kPa ~5 kPa 
Grain size distribution 
(<0.06mm) 
≥30% 
< 30% 
Supporting Pressure 
≥2bar 
< 2bar 
Environment 
Feasible Site 
Existence 
None 
Adjacent structures 
≥2.5D 
<2.5D 
Cost - 
Proceedings of the World Tunnel Congress 2014 – Tunnels for a better Life. Foz do Iguaçu, Brazil. 
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Figure 1. Ranges of application for tunneling machines (ITA, 2000) 
 
Figure 2. Area of application and selection criteria, SM-V5 (DAUB, 2000) 
 
Proceedings of the World Tunnel Congress 2014 – Tunnels for a better Life. Foz do Iguaçu, Brazil. 
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4 DERIVATION OF WEIGHTS 
Weights are derived by applying the AHP 
technique. For AHP analysis, a total of 217 pair-
wise comparison questions, including criteria 
factors (3 questions), sub-criteria factors (14 
questions) and detailed sub-evaluation element 
factors -alternatives (200 questions) are 
prepared. Distribution of marks for an inter-
comparison between the two factors of A and B 
is as follows: A is very important (9 points), A 
is fairly important (7 points), A is important (5 
points), A is somewhat important (3 points), 
Equally important (1 point), B is somewhat 
important (1/3 points), B is important (1/5 
points), B is fairly important (1/7 points), B is 
very important (1/9 points). 
The written questionnaires are evaluated by 
several experts for analysis. Expert assessment 
results, or results of pair-wise comparison are 
summarized in a matrix. 
As a square matrix, the matrix takes a spacial 
form of reciprocal matrix in which all diagonal 
factor lines are 1. 
A total of 24 matrices derived is as follows. 
 · Objective - 3x3 · Criteria - Hard rock: 5x5 
Soft ground: 3x3 
Environment: 2x2 · Sub-Criteria - 5x5, 20ea 
 
These matrices are normalized based on each 
column, and weights by each factor are 
estimated by the average on the column of the 
matrices composed of normalized values. 
In the case of this research project in which a 
number of experts are included in decision-
making process, methods to represent pair-wise 
comparison matrices of each expert as one 
matrix are required. In this connection, a 
method of generalization using the geometric 
mean (Saaty 1980), a method of generalization 
using the weighted mean (Ramanathan and 
Ganesh, 1994), and a method of generalization 
by means of goal programming are presented 
(Bryson, 1999, Yehm et al, 2001, Mikhailov, 
2004). 
In this study, the final weights are derived 
through generalization by means of a method of 
geometric mean that obtain elements of the 
generalized pair-wise comparison matrix using 
the geometric mean of individual pair-wise 
comparison matrices. (See tables 4 and 5) 
In the objective, ground condition (0.58/1) 
was derived as the most important factor. In the 
criteria for each condition, uniaxial compressive 
strength (0.30/1), water head (0.59/1), and 
influence on surrounding structures (0.71/1) 
turned out to be the most important factors. 
The generalization list of factors and 
alternative in the sub-criteria, or weights 
between machines is shown in Table 6. 
 
 
Figure 3. Schematic representation of AHP for Selecting of TBM 
 
Proceedings of the World Tunnel Congress 2014 – Tunnels for a better Life. Foz do Iguaçu, Brazil. 
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Table 4. Total weight of the factors (parameters) in each step 
Objective Factors Geology Environment Cost Weight CR 
Geology 1.000 3.032 3.003 0.58 
0.096 Environment 0.330 1.000 2.675 0.28 
Cost 0.333 0.374 1.000 0.14 
Criteria Factors 
Hard Rock 
Rock Compressive 
Strength RQD 
Fissure 
Spacing
Fault 
 zone 
Water in flow per 
10m tunnel Weight CR 
Rock Compressive 
Strength 1.000 2.714 2.807 1.088 1.246 0.30 
0.030 
RQD 0.368 1.000 1.463 0.919 0.707 0.15 
Fissure Spacing 0.356 0.683 1.000 0.353 0.683 0.11 
Fault zone 0.919 1.088 2.831 1.000 0.623 0.21 
Water in flow per 10m 
tunnel 0.803 1.415 1.463 1.605 1.000 0.23 
Criteria Factors 
Soft Ground Cohesion 
Grain size 
distribution Supporting Pressure Weight CR 
Cohesion 1.000 0.776 0.327 0.18 
0.002 Grain size distribution 1.288 1.000 0.365 0.23 
Supporting Pressure 3.055 2.737 1.000 0.59 
Criteria Factors 
Environment Feasible Site Adjacent structures Weight CR 
Feasible Site 1.000 0.401 0.29 
0.000 
Adjacent structures 2.493 1.000 0.71 
Table 5. Alternative Factors in 1st Sub-Criteria 
1st - Rock Compressive 
Strength=300~50(MPa) Gripper D.S S.S EPB Slurry Weight CR 
Gripper 1.000 4.990 4.908 5.520 5.473 0.51 
0.081 
D.S 0.200 1.000 2.807 3.712 3.839 0.21 
S.S 0.204 0.356 1.000 3.240 3.410 0.15 
EPB 0.181 0.269 0.309 1.000 1.552 0.07 
Slurry 0.183 0.261 0.293 0.644 1.000 0.06 
Alternative Factors in 2nd to 20th Sub-Criteria 
 
Proceedings of the World Tunnel Congress 2014 – Tunnels for a better Life. Foz do Iguaçu, Brazil. 
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Table 6. Total Weight (Parameters-Machine type) 
 Machine 
type 
 
Parameters 
Gripper D.S S.S EPB Slurry 
Geology 
Hard 
Rock 
Rock Compressive 
Strength (MPa) 
300~50 0.0886 0.0371 0.0251 0.0120 0.0098 
50~5 0.0120 0.0321 0.0460 0.0458 0.0366 
RQD 
100~50 0.0414 0.0218 0.0134 0.0062 0.0050 
50~10 0.0052 0.0201 0.0234 0.0188 0.0201 
Fissure Spacing (m)
>2.0~0.6 0.0175 0.0201 0.0138 0.0061 0.0050 
0.6~0.06 0.0037 0.0170 0.0152 0.0144 0.0121 
fault zone 
Existence 0.0100 0.0318 0.0313 0.0255 0.0252 
None 0.0558 0.0306 0.0215 0.0096 0.0062 
Water in flow per 10m 
tunnel (l/min.) 
≥25 0.0082 0.0252 0.0212 0.0332 0.0454 
< 25 0.0346 0.0289 0.0308 0.0205 0.0184 
Soft 
Ground 
Cohesion (kPa) 
> 30 0.0100 0.0280 0.0218 0.0266 0.0203 
30~5 0.0041 0.0117 0.0117 0.0344 0.0449 
Grain size distribution 
(<0.06mm) 
≥30 0.0069 0.0220 0.0164 0.0385 0.0473 
< 30 0.0060 0.0143 0.0147 0.0440 0.0520 
Supporting Pressure 
(bar) 
≥2 0.0139 0.0218 0.0161 0.1450 0.1450 
< 2 0.0205 0.0425 0.0366 0.1170 0.1253 
Environment 
Feasible Site 
Existence 0.0081 0.0152 0.0125 0.0193 0.0237 
None 0.0219 0.0158 0.0161 0.0169 0.0081 
Adjacent structures 
≥2.5D 0.0104 0.0373 0.0389 0.0856 0.0913 
< 2.5D 0.0492 0.0418 0.0410 0.0360 0.0252 
Cost 0.050 0.045 0.040 0.035 0.03 
 
5 FIELD APPLICATION AND 
VERIFICATION 
The 000 section of works for Seoul Metro 
which is currently under construction using EPB 
(EarthPressure Balance) machine takes a single 
parallel form and is composed of shield tunnel 
section (1,274m) and NATM section (86m), 
where weathered soil/rock, bedrock and 
composite ground coexist. In addition, this 
section requires the minimization of risk and 
subsidence of the ground since it crosses a total 
of 5 underground structures, including Olympic 
Convention Center and Mong-chon To-seong 
(cultural property) and passes through the 
bottom of the Mong-chon Lake. 
As a result of the evaluation using this 
preferred value based on the values of 
influencing factors of 000 section site, the 
preferred value of the machine according to 
each influencing factor is shown in Table 7, and 
the fact that it is the same machine as EPB 
(Earth Pressure Balance) used in the actual field 
is identified in the total. 
The results of using this preferred value 
based on the value of influencing factors of this 
site in TBM tunnel construction in the riverbed 
passage section of 000 construction confirmed 
that the machine selected according to each 
factor is the same as that used in the actual site 
as shown in Table 8. 
In addition, the reliability of this machine 
selection method was checked by applying the 
preferred value to power district and channel 
tunnel as shown in Table 9 below, and it was 
confirmed that the same machine as the one 
used in the field is derived. 
 
Proceedings of the World Tunnel Congress 2014 – Tunnels for a better Life. Foz do Iguaçu, Brazil. 
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Figure 4. Longitudinal section of the Seoul Metro Line No.9, Sector *** 
Table 5. Geological properties of Seoul Metro Line No.9 sector *** and result applies in the selection of TBM 
Parameters Value Selection of TBM 
Hard 
Rock 
Rock Compressive Strength 11.3~170.1 (MPa) Service Machine 
RQD under 50% 
Gripper D.S S.S EPB Slurry 
Fissure Spacing 0.6~0.06 (m) 
Fault zone ○ 
 Water in flow per 10m tunnel Max. 3.7ℓ/min. 
Soft 
Ground 
Cohesion 0~30 (kPa) Selecting Machine 
Grain size distribution 
(<0.06mm) 0% Gripper D.S S.S EPB Slurry 
Supporting Pressure 0.85~1.4 (bar) 
Environ-
ment 
Feasible Site X 
0.165 0.242 0.251 0.352 0.343 
Adjacent structures within 2.5D 
Table 7. Geological properties of Seoul Metro Line No.9 sector *** and result applies in the selection of TBM 
 
Figure. 5. Longitudinal section of the Seoul Metro Line No.5, Passing tunnel under the Han River 
Table 8. Geological properties of Seoul Metro Line No5, Passing tunnel under the Han River and result applies in the 
selection of TBM 
Parameters Value Selection of TBM 
Hard 
Rock 
Rock Compressive Strength 20~160 (MPa) Service Machine 
RQD above 50% Gripper D.S S.S EPB Slurry
Fissure Spacing (m) 0.6~0.06 (m) 
Fault zone ○ Selecting Machine 
Water in flow per 10m tunnel Max. 85ℓ/min. 
Gripper D.S S.S EPB Slurry
Environ- 
ment 
Feasible Site X 
Adjacent structures within 2.5D 0.085 0.183 0.191 0.232 0.232
 
Proceedings of the World Tunnel Congress 2014 – Tunnels for a better Life. Foz do Iguaçu, Brazil. 
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Table 9. Result applies in the selection of TBM in Other projects 
Project Service Machine Selecting Machine 
Seoul Metro Line No.9 Sector 921 EPB EPB 
Bundang Line Sector *** EPB EPB 
Dong-bok conveyance water Tunnel Gripper Gripper 
Electric power tunnel between Gwang-jin and 
Jungnang EPB Slurry 
Electric power tunnel at Banpo section Slurry Slurry 
 
 
6 CONCLUSION 
This study proposed the guideline for 
machine selection of the type of 'machine-factor' 
preferred value that considers environmental 
conditions and price conditions which were not 
taken into account in the previous studies 
though the AHP technique. The proposed 
guideline was verified through 5 projects, 
including the 000 section of works for Seoul 
Metro, and results similar to those of most 
machine used in the actual field were obtained. 
In this regard, it is expected that the selection 
of TBM that reflects domestic construction 
conditions and environment will be possible 
through the preferred value of factor-machine 
derived based on this study. 
ACKNOWLEDGEMENTS 
The authors gratefully acknowledge funding 
for this research provided by the Korea Agency 
for infrastructure Technology Advancement 
under the Ministry of Construction and 
Transportation of the Republic of Korea (Grant 
No. 10-E091, Innovation and optimization in 
TBM design, refurbishment and tunnel 
construction). 
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