=Paper=
{{Paper
|id=Vol-3619/AISD_Paper_3
|storemode=property
|title=A Study of Fuzzy Methodology of Tunnel Boring Machine in the Project of Lucknow Metro Rail Corporation
|pdfUrl=https://ceur-ws.org/Vol-3619/AISD_Paper_3.pdf
|volume=Vol-3619
|authors=Ubaid Asif Farooqui,Umar Khalid Farooqui,Chinta Mani Tiwari,Mohammad Husain Husain
|dblpUrl=https://dblp.org/rec/conf/aisd/FarooquiFGTH23
}}
==A Study of Fuzzy Methodology of Tunnel Boring Machine in the Project of Lucknow Metro Rail Corporation==
https://ceur-ws.org/Vol-3619/AISD_Paper_3.pdf
A STUDY OF FUZZY METHODOLOGY OF TUNNEL BORING
MACHINE IN THE PROJECT OF LUCKNOW METRO RAIL
CORPORATION
Ubaid Asif Farooqui1, Umar Khalid Farooqui2, C.M. Tiwari1, Mohammad Husain3
1Maharishi University of Information Technology, Lucknow, India
2 Amity University Lucknow Campus, Lucknow, India
3 University of Madinah, Al Madinah Al Munawarah, Saudi Arabia
Abstract
This work is about study on the fuzzy operation methodology of tunnel boring machines and finding
the optimum solution for the best approach of tunneling. Adaptive network-based fuzzy inference
system used in a mathematical model of TBM, neural networking with fuzzy logic and the fuzzy c-
means clustering technique is used for soft soil and hard rock condition both with regression model
developed by latest MATLAB technique to reduce error. UCS, BI, DPW, and Alpha degree constitute
four geological technical variables that are utilized to develop regression equations for Rate of
Penetration (ROP) to prevent unnecessary effort and save time and money in project.
Keywords
TBM, ANFIS, NN, CLUSTERING METHOD, FUZZY LOGIC, UCS, BI, DPW and ROP.1
1. INTRODUCTION
LUCKNOW METRO RAIL CORPORATION was faced with the challenge of connecting the metro
through densely populated city areas. Some citizens opposed trains crossing bridges close to
their homes, and for security reasons, some government institutions refused to allow trains to
pass close to sensitive locations like the secretariat.
LMRC had the option to build an underground segment utilizing the existing technology of
tunneling because it was decided to make a particular piece of the Lucknow Metro
subterranean.
1. Through Tunnel Boring Machine
2. Using Cut and Cover Method
3. NATM (New Austrian Tunneling Method)
The TBM method was an obvious choice for the majority of subterranean tunnels because to its
efficacy and track record in metro areas of other cities. The nearby Underground stations for the
UP line and DOWN line are connected by twin separate Bored tunnels.
AISD 2023: First International Workshop on Artificial Intelligence: Empowering Sustainable Development September
4-5, 2023, co-located with International Conference on Artificial Intelligence: Towards Sustainable Intelligence,
(AI4S-2023), Pune, India
uasif2u@gmail.com (U. Asif Farooqui); ukfarooqui@gmail.com (U. Khalid Farooqui); cmtiwari.12@gmail.com(C.M.
Tiwari); mohd.husain90@gmail.com (M. Husain)
http://dblp.org/pid/95/7965.html (Mohammad Husain)
Β© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Figure 1: Tunnel Boring Machine. [29]
Currently, engineers must navigate a metro line through an underground tube while the city is
home to some extremely historic structures.
The use of TUNNEL BORING MACHINE (TBM) was a smart move, however operating TBM is a
challenging chore. For this task to be monitored, data must be collected, analyzed, predicted,
and controlled. Estimating the tunnel-boring equipment's (TBM) efficiency of penetration is one
of the difficult jobs that must be completed in order to bore a tunnel. The hazards associated
with the high capital costs typical of boring operations may be decreased by estimating the
machine penetration rate. The key to successful tunnel boring is the ability to predict the tunnel
boring machine's effectiveness. Among the essential metrics utilized for performance prediction
of these machines, The precise energy consumption of wheel cutters, which is the volume of
power needed to obtain a unit volume of stone and soil.
2. LITRATURE SURVEY
Neuro-fuzzy techniques were utilized by Grima and Bruines to model TBM performance. To
predict the TBM performance, Benardos and Kaliampakos used artificial neural networks. A
neural network-based approach for predicting TBM performance was introduced by Zhao and
Gong. Acaroglu and Ozdemir developed a fuzzy logic model to forecast the precise amount of
energy needed to anticipate TBM performance. In a subsequent attempt, Yagiz proposed two
nonlinear prediction tools for the estimate of TBM performance artificial neural networks and
nonlinear multiple regression. Two key components of the TBM performance, including the
penetration rate and utilization factor, were examined by Torabi and Shirazi using A
computational social arts analytical program and a synthetic neural network. Yagiz and Karahan
used the particle swarm optimization (PSO) method to predict the rate of hard-rock TBM
breakthrough.
The performance of TBMs can be anticipated using an adaptive neuroscience fuzzy inference
system built around Fuzzy C-Means Grouping Methodology (ANFIS-FCM), a unique data analysis
technique. In this model, the rate of penetration served as the output parameter, As input
variables, the intact rock brittleness (BI), alpha angle, planes of weakness, and uniaxial in nature
compressible strength (UCS) were all utilized. The ANFIS-estimating FCM's capabilities are
demonstrated using field data from open-access publications. Yet, as the primary function of
cutters is to shatter rock, significant advances in cutter design and metallurgy have been made.
Disc cutters, which are frequently used by TBMs and may cut an assortment of sandstone
different types with various qualities. Hard rock TBMs have utilized single disc cutters with
replacement disc rings for a long time because they have shown themselves to be effective and
dependable. When tip wear developed, the V-shape (V-profile) of early disc cutters produced a
sharp decline in efficiency. Constant cross-section (CCS) profiles began to take the place of V-
shape ring profiles in the late 1970s in order to preserve cutting efficiency after the tip wore
out. Mini disc cutters, often used after 30 cm (12 in.) in diameter, are disc cutters with a
diameter of between 49 cm (19 in.) and 8 cm (3 in.). The maximum cutting forces that discs can
withstand depend on the cutters' bearing capabilities, which decrease as disc diameter
increases. Little discs, however, penetrate more deeply than larger discs when applied with the
same force (Friant and Ozdemir, 1994).
A project's schedule and cost are significantly impacted by the ability to produce precise
performance estimations when applying TBM technology. Several case studies demonstrate
how performance estimate errors can lead to project delays and expense overruns. The
penetration and progress rates as well as machine utilization serve as key performance
indicators for TBMs. One of the main functions of a disc cutter is to cut rock, and particular
efficiency, which is the amount of power needed by wheel cutters to do something, is used to
Identify the performance parameters for the machine. With a lower SE, the same amount of
material can be produced while excavating the rock much more effectively and with comparably
less expensive equipment. One of the reliable artificial intelligence methods, It has been shown
that the fuzzy c-means grouping algorithm based adaptive neuro fuzzy inference system
(ANFIS-FCM) is very good at identifying associations with input and output features.
3. METHODOLOGY
3.1 Adaptive network-based fuzzy inference system used in a mathematical
model of TBM:
Without using exact quantitative analysis, the qualitative components of human understanding
and logic can be replicated by a fuzzy inference system. Information-processing software called
neural networks (NNs) is modeled after research, observation and analysis on functions of the
animal and human brain. A collection of interrelated components of processing that resemble
neurons make up NN. The instruction process provides a set of input data into the NNs and
verifies the desired output. It has been demonstrated that combining NNs with fuzzy logic (FL)
can reasonably simulate the psychological procedure for making decisions for skilled. Typical
NNs merely evolve the development process' values for weighting, therefore a neuro-fuzzy
decision-making system combines the learning capabilities of NNs with the FL's inference
process. A network structure made up of multiple nodes connected by directional links is an
adaptable neural network. Each node is defined by a node function that can have either fixed or
variable arguments. As soon as the fuzzy inference system (FIS) is in place, NN approaches can
be used to discover the rules' underlying assumptions and resulting parameters, which are
unknown, while lowering the error measure typically defined for each system variable. The
system is referred to as adaptive because of this optimization process. Five levels make up the
ANFIS architecture, and the model is briefly described below.
Level 1: The membership grades of a linguistic label are generated by each node R in this layer.
For example, the π π‘β node's node feature could be:
1
ππ1 = ππ΄π (P) = π₯βππ 2 π (1)
1 +[( ) ] π
ππ
here P is the each node's data, Ar is the linguistic term (listed in ascending order) attached to it,
and ππ, Kr , dr is the parameter set that alters the MF's forms . The "Premises factors" are the
name given to the variables in this level.
Level 2: The "firing intensity" of every rule in this level is determined by compounding at each
node:
ππ2 = ππ = ππ΄π (P).ππ΅π (Q) r = 1,2 (2)
Level 3: The ππ‘β component of this level defines the proportion of the ππ‘β rule's blazing
strength to the sum of all rules' blazing strengths:
π
ππ3 = Μ
Μ
Μ
ππ = β2 π π r = 1,2 (3)
π=1 π
This level of output will be referred to as "normalized firing" strengths for simplicity.
Level 4: All nodes r at this level are nodes functions:
ππ4 = Μ
Μ
Μ
ππ ππ = Μ
Μ
Μ
ππ (πΌπ π + π½π π + πΎπ ) (4)
Where, Μ
Μ
Μ
ππ is level 3's output. The term "consequent parameters" will be used to describe the
parameters at this level.
Level 5: The "comprehensive outcome" of this level is calculated by a single circular component
with the label "R," which adds together all of the signals coming in.
βπ π
ππ5 = Comprehensive Outcome = β Μ
Μ
Μ
ππ πΊπ = β ππ π (5)
π
In this work, the antecedent MFs are also identified using FCM.
3.2 The procedure of fuzzy c-means grouping
The FCM is a Bezdek-invented data grouping technique where each data point is a member of a
group to the extent specified by the classification of membership of a cluster. FCM is used to
distribute a set of n vector ππ, where r = 1, 2,..., n, into C fuzzy groups.. In each group, a clump
center is found that minimizes the cost function of the dissimilarity measure. Thus, a quick
description of the FCM algorithm's steps follows. The cluster's initial nodes are ππ, r = 1, 2,..., C.
From the n points, π1 is randomly selected, followed by π2 and so on until πn . The following
equation is then used to build the composition of the matrix U:
1
πππ = 2β (6)
πππ π€β1
π
βπ=1( )
πππ
The formula provides the Euclidian distance that separates the ππ‘β group and the ππ‘β value
is given by the formula πππ = βππ β ππβ and w is the fuzziness index. The cost function is then
calculated using the equation shown below. If it falls below a specific threshold, the process is
stopped.
L(U,π1,β¦β¦.π2 ) = βππ=1 π½π = βππ=1. βπ‘π=1 πππ
π€ 2
πππ (7)
A fresh c fuzzy group with r = 1,2 focuses on ππin the final step. To figure out C, apply the
equation below.
βπ‘ π€π₯
πππ π
ππ = βπ=1
π‘ (8)
ππ€
π=1 ππ
3.3 Using the most recent version of MATLAB, 4 Regression Models for ROP are
concluded:
Technically, because their empirical correlation coefficients to ROP are more than 0.6, qualities
of boulders demonstrate the most precise factors for forecasting the ROP will be the UCS and
the BI. The DPW and alpha angle are also important measures to assess ROP because they have
correlation factors to ROP that are higher than 0.5. As a result, the following part will develop
regression models for ROP using four (4) rock technical attributes, UCS, BI, DPW, and Alpha.
Model 1: Linear regression model (LRM)
Model 1: Linear regression model (LRM)
(1) (1) (1) (1) (1)
ROP (i) = π½0 +π½1 Β·UCS+ π½2 Β·BI+ π½3 Β·DPW + π½4 Β·Alpha
Model 2: Linear regression model with log(Alpha)
(2) (2) (2) (2) (2)
ROP (ii) = π½0 +π½1 Β·UCS+ π½2 Β·BI+ π½3 Β·DPW + π½4 Β·log(Alpha)
Model 3 : Nonlinear regression model with exponential Alpha (NLRM1)
(3) (3) (3) (3) (3) (3)
ROP (iii) = π½0 +π½1 Β·UCS+ π½2 Β·BI+ π½3 Β·DPW + π½4 Β·(Alpha)π½5
Model 4: Nonlinear regression model with both exponential DPW & Alpha (NLRM2)
(4) (4) (4) (4) (4) (4) (4)
ROP (iv) = π½0 +π½1 Β·UCS+ π½2 Β·BI+ π½3 Β· DPWπ½4 + π½5 Β·(Alpha)π½6
3.4 Methodology for Data Processing
To increase the effectiveness of networks in recognizing the links between inputs and outputs,
inputs and output data should be normalized prior to the training process. Moreover, scaling the
data to reduce the biassing of the networks and improving prediction accuracy are both greatly
aided by normalization. The time-consuming nature of training can be cut down through data
normalization. Modeling applications with input data of various scales can greatly benefit from
it. Many normalization methods, such as Z-Score normalization, Min-Max normalization,
sigmoid normalization, statistical column normalization, etc, are commonly used to scale up the
data, Nonetheless the Min-Max normalization method was employed for the purposes of this
study. This was made possible by Min-Max normalization's ability to preserve each feature's
variation following normalization. Moreover, this normalization technique can maintain all of
the data's relationships. Below is how the Min-Max normalization equation is expressed:
π¦βπ¦πππ
ππ = π¦ (9)
πππ₯ βπ¦πππ
where Y is the dataset's original value, ππ is the mapped value, and π¦πππ₯ (π¦πππ ) stands for the
dataset's highest (lowest) possible raw input values.
Mean square error (MSE) and coefficient of determination (R2) are two more established
metrics used to evaluate the effectiveness of the networks in addition to normalization. The
following equation is used to compute MSE:
1 2
MSE = π‘ βπ‘π=1(βπ β βΜπ ) (10)
where βπ and βΜπ are the real and anticipated standard of the qth observation, accordingly,
and t is the quantity of samples utilized to train or test the network. It is common practice to
demonstrate the difference between measured and estimated network values using the MSE
criterion. You can also compute R2, or the coefficient of determination, as follows:
2
βπ‘π=1(βπ ββ
Μπ )
π
2 = 1β π‘ 2 (11)
βπ=1 βπ2 β(βπ‘π=1β
Μπ2 βπ‘ )
R2 is frequently used to illustrate the model's initial level of uncertainty. The ideal network
model, which is highly improbable to be created, has MSE=0 and R2 =1.
4. Results
A specialist who is familiar with the system that needs to be simulated specifies the number of
rules in a regular fuzzy inference system. However, no expert is available in the ANFIS
simulation; as a result, the total number of member functions (MFs) provided for every input
parameter is chosen intuitively, i.e., by charting the data sets and visually inspecting them, or
just by trial and error.
Figure 2: Fuzzy logic surface. [30]
Figure 3: Fuzzy logic rules viewer [30]
Using the MATLAB environment, the ANFIS-FCM model was used to this study's prediction
model to calculate the TBM penetration rate from the available data. In order to estimate TBM
performance, the ANFIS-FCM model's fuzzy architecture is depicted in Fig 4. The current study
used a dataset with 153 data points, 122 of which (or 80%) were used to build the model while
the other 31 data points were used to assess the model's performance. Table 1 provides more
information on the parameters of the ANFIS-FCM model.
Figure 4: An ANFIS-FCM Strategy Structure
Table 1
Modeling components for the ANFIS-FCM
Parameter Description
Type of participation duty Gaussian
participants perform produce Linear
Density of vertices 57
numerous linear factors 25
various nonlinear parameters 40
Numerous parameters in total 65
Total pairs of training data 122
combinations of test data in 31
numerical
quantity of fuzzy rules 5
5. Conclusion and Discussion
The forecast and evaluation of its utilization factor are two of the most crucial elements
influencing how well TBM performs. In actuality, TBM tunneling projects are more productive
when the utilization factor is accurately identified and calculated. Since the goal of the current
study is to anticipate the utilization approach in sandy soil tunneling (metropolitan
environment), correct locating and calculating the utilization proportion helps to avoid
potential machine damage as well as excessive project costs and time. However, there are
always unknown and unforeseen events in underground operations, requiring the use of smart
data analysis techniques.
In this study, the hard rock TBM penetration rate was estimated using the ANFIS-FCM method.
The UCS, BI, DPW, and alpha angle of intact and bulk rocks, among others, have been discovered
to significantly affect TBM penetration. Consequently, the model was created using relevant
properties. It can be concluded that ANFIS-FCM is a dependable method of system modeling
that forecasts penetration rate with a very respectable level of resilience and accuracy.
With π
2= 0.6765 and MSE= 0.0257. This study highlights the potential of the ANFIS-FCM
technique as a powerful tool for simulating several tunnel engineering-related problems.
When the outcomes of the various generated models were compared, the created model, which
was based on the regression model and ANN-FCM, was discovered to have a superior capacity
for training multiple linear regression and multi-layer perceptron neural networks. Last but not
least, given the importance of figuring out the utilization factor in tunneling project procedures.
It is advised to research and compare this coefficient in soft lands with other prediction
techniques like the support vector machine, genetic programming, and regression tree
technique, as well as compare it with the findings of this study.
Acknowledgements:
Thanks to Lucknow metro rail corporation technical team, civil and electrical department and
special thanks to the TBM control unit team, Amity University Lucknow campus and Maharishi
University of Information Technology Lucknow, for help in this research work.
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