Science Journal of Civil Engineering and Architecture
ISSN:2276-6332
Published By
Science Journal Publication
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© Author(s) 2014. CC Attribution 3.0 License.
Volume 2014, Article ID sjcea-165, 8 Pages, 2014. doi: 10.7237/sjcea/165
Research Article
USING NEURAL NETWORKS FOR THE PREDICTION OF PERMEABILITY IN
ROCK FOR DERIK EMBANKMENT EARTH DAM
Houtan Jebelli ¹, Melika sharifironizi², Vandad Mazarei³ and Esmail Aflaki⁴
¹Department of Construction Engineering, University of Nebraska-Lincoln ,NE,US
E-mail:jebelli.houtan@huskers.unl.edu
²Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, US.
E-mail:Msharifi@nd.edu
³Department of Civil and Environmental Engineering, Oregon State University, OR, US
Email:Vandad@aut.ac.ir
⁴Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran
E-mail: eaflaki@aut.ac.ir
Accepted 27�� February, 2014
ABSTRACT
In this study, an artificial neural network has been modeled that
is able to predict the permeability of the jointed rocks of the Derik
embankment earth dam in the north west of Iran. In jointed rocks
the nonlinear relationship exists between the absorption rate and
pressure so water pressure test frequently used for evaluating the
permeability of jointed rock masses. Different pressures were used
during the water pressure test that may result changes in the
behavior of rock masses. An artificial neural network is a
biologically inspired computing method that is capable of
predicting the permeability values of rock masses with high
accuracy. The neural networks have shown high potential in
interpreting the raw data obtained from Leugon test and predicting
the final permeability of a testing section in a borehole. The result
showed that, the network that contains five hidden neurons has
the best results among other networks and the neural networks
are capable of predicting permeability values of jointed rock with
high accuracy. This method can be used to analyze the permeability
of jointed rocks of dams and grouting zones.
KEYWORDS:Permeability, Artificial Neural, Network,
jointed rocks, water pressure test
INTRODUCTION
One of the most important parameters that used for the
design of large structures like dams and water reservoirs is
the permeability (Ewert, 1985). Knowledge of Permeability
has got a paramount importance as one of the petrophysical
parameters of a rock that cannot be over emphasized;
however, the estimation of formation of permeability
without actual laboratory measurement of cores or
interruption in production is one of the basic problems
(Osmanet al., 2001) In natural rocks, permeability depends
on several orders of magnitude. It is usually obtained from
direct measurements on core samples or from empirical
models that relates permeability to parameters calculated
from well logs like porosity and water saturation. The first
method is very expensive and in the second method, the
coefficients of the equations must be defined for every well
and the equation obtained for one formation does not
perform well in the other fields (Salanet al., 1995).
One of the widely accepted methods is the water pressure
test that was introduced by Lugeon (Lugeon, 1933).It is a
pump-in test where the volume of water is taken in a section
Corresponding Author: Houtan Jebelli
Department of ConstructionEngineering, University of Nebraska-Lincoln ,NE,US
E-mail:jebelli.houtan@huskers.unl.edu
of a borehole and measured during given time intervals
(Hashemi, 2006).
The use of artificial neural network eliminates some of the
problems associated with costs and generalization of
developed models for the prediction of permeability
distribution. The artificial neural network was designed and
trained to acceptably predict the permeability without
overall reliable results. (Edison, 2000)
An alternative approach to the parametric modeling
approach is the application of artificial neural networks
(ANNs). In recent year, ANNs have emerged as powerful
tools for modeling complex systems (Jamial ahmadi,
Javadpour, 2000)
Parametric methods like statistical regression require the
assumption and satisfaction of multi-normal behavior and
linearity. Therefore, neural network as a non-linear and
non-parametric tool is becoming increasingly popular in
well log analysis. The neural network is a computer model
that attempts to mimic simple biological learning processes
and simulate specific functions of human nervous system.
The neural network can be used as a nonlinear regression
method to develop transformation between the selected
well logs and core analysis data. ( Jong-Se Lim , 2005)
Artificial neural networks are a large class of parallel
processing architectures, which can mimic complex and
non-linear relationships through the application of many
non-linear processing units called neurons. The relationship
can be 'learned' by a neural network through adequate
training from the experimental data (Lin et al.,
2008).Artificial neural network provides a parameterized,
non-linear mapping between inputs and output. . It has the
inherent capability to deal with fuzzy, non-linear, whose
functional relations are not clear (Mandal et al., 2007).
Geology of the dam site
Derik dam is located in the north west of Iran at the latitude
44̊.38N and the longitude 38˚.10E. It has been constructed
on the Derik River, one of the Ashnak river branches. A data
bank containing a large number of data points was compiled
over a wide range of reservoir depth, porosity and
Science Journal of Civil Engineering and Architecture( ISSN:2276-6332)
page 2
permeability. The basin formed in surface deposit mainly of
sediments of the Cretaceous Derik formations and is
typically clay-marl and shale as is shown in Fig 1. More
recent geological deposits are alluvium sediments in the
valley bottom (Geological survey of Iran, 1985).
A geological section of the formations along the axis of the
Derik river dam is shown in Fig 1.
Results of several site investigations show four faults on the
left side of the embankment dam (from 0+000 km to 0+800
km) that identified as F1 to F4 (Table1). Plot of poles of
planes of joints and four faults in hard rocks is given in Fig
2.
,
Table 1:Faults in the left side of the dam
F1
F2
F3
F4
Dip
80
75
78
70
Dip Direction
230
230
225
30
Fig. 1: Geological section showing the geological
How to Cite this Article: Houtan Jebelli, Melika sharifironizi, Vandad Mazarei and Esmail Aflaki, "Using Neural Networks for the prediction of Permeability
in Rock for Derik Embankment Earth Dam" Science Journal of Civil Engineering and Architecture, Volume 2014, Article ID sjcea-165, 8 Pages, 2014. doi:
10.7237/sjcea/165
page 3
Science Journal of Civil Engineering and Architecture( ISSN:2276-6332)
Fig. 2: Plot of poles of joints and faults in hard rock mass
DESCRIPTION OF DATA
Then, all the data were entered into an Excel application
program for calculations before the analysis began.
Lugeon tests were performed to obtain the Lugeon value,
which gives the permeability of that specific borehole
section. The tablelists the data analyzed for 51 boreholes.
All of the data were collected from the inspection chart of
the grout-curtain construction of the Derik dam in 2010.
Table 2, 3 and 4 shows the number of boreholes in each zone
of the Derik dam. The location of the boreholes and the
Lugeon changes referring to depth in left zone and the main
zone shown in Fig.3 and Fig.4.
Table 2. Number of boreholes in left zone
Zone
Left Zone
Bore hole
number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Lu=2.5
1
2
0
2
0
0
0
0
1
2
1
3
4
3
3
1
2
3
0
Lu=12.5
1
1
2
2
1
1
4
5
3
2
1
3
1
0
3
2
2
3
0
Lu=35
Lu=75
0
1
1
1
2
1
0
1
0
1
3
0
0
0
0
0
1
2
0
1
0
1
0
1
2
1
1
1
0
1
0
0
1
1
2
5
2
2
Lu>100
1
0
1
0
1
1
1
0
1
1
0
0
0
2
0
2
3
1
2
Total
number of
Lugeon Test
4
4
5
5
5
5
6
7
6
6
6
6
5
6
7
7
13
11
4
How to Cite this Article: Houtan Jebelli, Melika sharifironizi, Vandad Mazarei and Esmail Aflaki, "Using Neural Networks for the prediction of Permeability
in Rock for Derik Embankment Earth Dam" Science Journal of Civil Engineering and Architecture, Volume 2014, Article ID sjcea-165, 8 Pages, 2014. doi:
10.7237/sjcea/165
Science Journal of Civil Engineering and Architecture( ISSN:2276-6332)
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
4
3
3
4
6
4
5
5
3
2
7
3
4
5
2
4
3
2
4
6
4
5
0
3
5
1
1
1
1
7
Page 4
0
3
0
0
1
0
2
2
1
2
0
0
0
0
0
3
1
1
2
0
1
1
0
1
1
1
4
2
1
1
3
3
2
1
0
1
0
3
1
1
1
3
1
0
1
14
13
8
11
13
10
13
10
9
11
10
11
8
7
11
Table 3:Number of boreholes in main zone
Zone
Bore Hole
Number
Lu=2.5
Lu=12.5
Lu=35
Lu=75
Lu>100
Main Zone
1
2
3
4
5
6
7
8
9
10
11
12
13
14
2
1
0
4
4
5
3
3
3
2
3
3
6
4
3
1
1
5
1
0
0
0
2
4
4
2
1
5
0
0
2
0
0
0
0
0
0
1
1
2
2
0
3
2
1
0
0
0
0
0
0
1
1
1
1
2
2
2
0
0
0
0
0
0
0
0
0
1
1
2
Total
Number of
Lugeon tests
10
6
4
9
5
5
3
3
5
8
9
9
11
13
Table 4:Number of boreholes in right zone
Zone
Right Zone
Bore Hole
Number
Lu=2.5
Lu=12.5
Lu=35
Lu=75
Lu>100
Total
Number of
Lugeon tests
1
8
1
1
2
2
14
2
3
2
0
0
0
5
3
2
4
1
1
0
8
How to Cite this Article: Houtan Jebelli, Melika sharifironizi, Vandad Mazarei and Esmail Aflaki, "Using Neural Networks for the prediction of Permeability
in Rock for Derik Embankment Earth Dam" Science Journal of Civil Engineering and Architecture, Volume 2014, Article ID sjcea-165, 8 Pages, 2014. doi:
10.7237/sjcea/165
page 5
Science Journal of Civil Engineering and Architecture( ISSN:2276-6332)
Fig. 3: Lugeon changes referring to depth in left zone
Fig. 4: Lugeon changes referring to depth in main zone
How to Cite this Article: Houtan Jebelli, Melika sharifironizi, Vandad Mazarei and Esmail Aflaki, "Using Neural Networks for the prediction of Permeability
in Rock for Derik Embankment Earth Dam" Science Journal of Civil Engineering and Architecture, Volume 2014, Article ID sjcea-165, 8 Pages, 2014. doi:
10.7237/sjcea/165
Science Journal of Civil Engineering and Architecture( ISSN:2276-6332)
NEURAL NETWORK APPROACH
An artificial neural network is a parallel-distributed
information processing system. It stores the samples with
distributed coding, thus forming a trainable nonlinear
system. Given the inputs and the desired output, it is also
self-adapt to the environment so that responding to different
inputs rationally (Jianget al., 2008). The aim is training the
network to achieve a balance between the ability to respond
correctly to the input patterns (Fausett, 1994).
Page 6
A simple network has a feed-forward structure where
signals flow of inputs through any hidden units and
eventually reaches the output units (Figure 5). To train
neural networks as a learning algorithm is used for
specifying the initial set of weights and indicates how
weights should be adapted to improve performance by
minimizing errors. By sequentially applying a set of input
while adjusting network weights, the network weights
gradually converge to values that are able to produce desired
sets of outputs (Tarassenko, 1998).
Fig.5. The structure of a simple Neural Network
DESIGN OF THE NEURAL NETWORKS
In this study, NeuroSolutions version 6.0 was used for design
purposes (NeuroDimentions, 2002). Two models were
considered in order to design Neural Networks that
predicting the final permeability of rock and the flow regime.
In the first model, the values of absorption rate and water
pressure data are the inputs. Therefore, the network needs
10 inputs (two Q and P in each five testing stages).
In the second model, the network uses Lugeon values for
calculation for each stage of the test as input, which means
that the network requires five inputs. One Lugeon value
calculated for each five tests and the Lugeon value for the
test sections in the boreholes is the output of the test.
Currently there is no way to estimate the number of hidden
layers or hidden units, which is an optimal layer in these
layers, other than by experiments. However, one layer in the
network is capable to solve any problems (Goh, 1995 and
Shi et al., 1998). The number of inputs and outputs of the
neural networks is determined by considering the
characteristics of the application (Saemiet al., 2007). In this
study, one hidden layer used for designing the network for
both models, but the number of hidden neurons changed to
find the most appropriate network topology. The backpropagation learning algorithm was used in this study in
order to train the neural networks (Rumelhart et al., 1986).
This algorithm executes the patterns into steps. In the first
step a forward flow of signals produce from the input layer
to the output layer.
Then from the difference between the calculated and desired
output, the error of each output neuron was computed. In
the second step, the weights in the hidden layers and output
layer re-adjusted in order to reduce the difference between
the exit output and the desired output (Rumelhart et al.,
1986). The network performance may be evaluated
quantitatively in terms of the coefficient of correlation (R2),
mean summed square of errors (MSSE), and the error rate.
The error rate was defined by (1), (2) and (3). (Yehet al.,
2003)
1
2
3
Where T and O are targeted and network output values,
respectively; Np is the number of input patterns; Nout is the
How to Cite this Article: Houtan Jebelli, Melika sharifironizi, Vandad Mazarei and Esmail Aflaki, "Using Neural Networks for the prediction of Permeability
in Rock for Derik Embankment Earth Dam" Science Journal of Civil Engineering and Architecture, Volume 2014, Article ID sjcea-165, 8 Pages, 2014. doi:
10.7237/sjcea/165
page 7
Science Journal of Civil Engineering and Architecture( ISSN:2276-6332)
number of output neurons, for a network with one output
neuron, as in this study.
Result of the networks
cross validation. Also the data were randomized after
normalizing the data to prevent the network being
influenced by similar or redundant data. The results of the
neural networks presented in Table 1.
Obtained data from 445 water pressure tests (Leugon) were
considered in this study. 356 leugon's results were used for
training the network, 44 sets for cross validation and 47 for
testing the network. In order to omit the over fitting in the
network and check the progress of the algorithm we used
In order to analyze and compare the performances of the
networks the mean absolute error (MAE) and the correlation
coefficient (r) were used and to distinguish the performance
of the network, the maximum value of r and the minimum
value of MAE should be considered.
Table 5: Results of different networks using Lugeon values as input
Number of hidden
neurons
20
15
10
7
5
3
Training
r
MAE
0.991
2.563
0.994
2.101
0.996
1.911
0.996
1.855
0.997
1.823
0.992
2.001
For this model, the network that contains five hidden
neurons has the best results among other networks. Using
3 hidden neurons is very simple and also using too many
hidden neurons is making the network complex and it causes
the network longer in time so the network show lower
performance.
CONCLUSION
There are some disadvantages during water pressure test
which cause bad effects on the results. For example, using
different pressures during the tests in the rock masses may
result changes of the behavior of the rock masses. In
addition, the relation between the absorption rate and the
water pressure in jointed rocks is non-linear that it can make
the interpretation of the flow type more difficult, but neural
networks have the ability to recognize the relative
importance of a particular parameter in a given pattern. By
using neural networks, determination of the permeability of
rock masses from lugeon values calculated for each testing
stage become more capable. Neural networks could be a
useful mean for predicting and analyzing the permeability
of rock masses of foundations for dams and grouting zones.
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How to Cite this Article: Houtan Jebelli, Melika sharifironizi, Vandad Mazarei and Esmail Aflaki, "Using Neural Networks for the prediction of Permeability
in Rock for Derik Embankment Earth Dam" Science Journal of Civil Engineering and Architecture, Volume 2014, Article ID sjcea-165, 8 Pages, 2014. doi:
10.7237/sjcea/165
Science Journal of Civil Engineering and Architecture( ISSN:2276-6332)
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How to Cite this Article: Houtan Jebelli, Melika sharifironizi, Vandad Mazarei and Esmail Aflaki, "Using Neural Networks for the prediction of Permeability
in Rock for Derik Embankment Earth Dam" Science Journal of Civil Engineering and Architecture, Volume 2014, Article ID sjcea-165, 8 Pages, 2014. doi:
10.7237/sjcea/165