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Energy Procedia 112 (2017) 19 – 26

Sustainable Solutions for Energy and Environment, EENVIRO 2016, 26-28 October 2016,
Bucharest, Romania

Choosing the optimal technology to rehabilitate the pipes in water

distribution systems using the AHP method
Ioan Aşchileana, Gheorghe Badeab, Ioan Giurcab,*, George Sebastian Naghiuc,
Florin George Iloaiec
a
SC ACI Cluj SA, Avenue Dorobanţilor, no. 70, Cluj-Napoca, 400609, Romania
b
Technical University of Cluj-Napoca, Faculty of Building Services Engineering, Boulevard December 21, no. 128-130, Cluj-Napoca, 400604,
Romania cS. C. Klever System S.R.L., str. 1 Decembrie, no. 30 A, Bistriţa, 420080, Romania

Abstract

In this paper the authors intend to solve in a scientific way, using the AHP method, a problem faced by companies in the field of
water supply in towns, and choosing the technology of pipe rehabilitation in water distribution systems. This paper presents a
case study on the selection of the technology for the rehabilitation of domestic water distribution network in Cluj-Napoca. We
used the AHP method in order to rank the priorities regarding the rehabilitation of water distribution network. Based on the study
performed, we recommend using the Slipline method for the rehabilitation of water distribution pipes in Cluj-Napoca, Romania.
The method presented in this paper may be used for the feasibility studies elaborated for water distribution networks. Also, the
AHP method may be correspondingly used for the selection of other types of installations for buildings.
© 2017
© 2017TheTheAuthors.
Authors. Published
Published by Elsevier
by Elsevier Ltd. Ltd.
This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility ofthe organizing committee of the international conference on Sustainable Solutions for Energy
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
and Environment
Peer-review 2016.
under responsibility of the organizing committee of the international conference on Sustainable Solutions for Energy
and Environment 2016
Keywords:selection of optimal technology; multi-criteria analysis – MCA; AHP method; rehabilitation of pipes; trenchless technologies.

* Corresponding author. Tel.: +40-0723 371 760; fax: +40-0258 841 127.
E-mail address:giurca_ioan@yahoo.com

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of the international conference on Sustainable Solutions for Energy and Environment 2016
doi:10.1016/j.egypro.2017.03.1109
20 Ioan Aşchilean et al. / Energy Procedia 112 (2017) 19 – 26

1. Introduction

a) Context. In 2007, according to a report released by the National Statistics Institute, in Romania drinking water
losses represented 40.9 % [1].
According to Romanian Standard 1343-1:2006 [2], acceptable water losses in the existent distribution networks
must not exceed 35 %, and in case of rehabilitated pipes, water losses must not exceed 22 %, while in case of
networks newer than 5 years water losses must not exceed 15 % [1].
In this context, in Romania, one should make a priority out of replacing water networks, due to the fact that their
normal lifespan was exceeded, and on the other hand water losses greatly exceed the accepted limits specified in
Romanian Standard 1343-1:2006 [2].
In order to select the technologies for the rehabilitation of the water supply systems one may use multi-criteria
analysis. Out of the various multi-criteria methods available, in this paper we propose the use of AHP method.
b) Present state of research in the world. Among the most relevant papers published internationally on the
rehabilitation of pipelines of water supply systems of settlements, are the following [3,4,5,6].
The Analytic Hierarchy Process (AHP) was developed by Thomas L. Saaty (1977, 1980, 1982, 1988, 1995), as a
method of analyzing decisions by structuring the decision’s components [7,8].
The applicability of this method was successfully proved in decisional problems pertaining to the technical and
economical field, namely for: the selection of the supplying variants, the selection of the investment projects, the
selection of certain types of equipments that are to be bought through the modernization project, the division of the
financial resources based on certain budgets, and so on [9].
c) Present state of research in Romania. Although in Romania there were studies related to the rehabilitation of
water distribution networks, until now, in the scholarly literature, there is no synthetic approach [1].
The AHP method proved to be one of the most applicable methods of multi-criteria analysis (MCA) and it is
mentioned in most of the MCA manuals and guides [10].
d) Purpose of the paper. In this paper the authors intend to solve in a scientific way, using the AHP method, a
problem faced by companies in the field of water supply in towns, and choosing the technology of pipe rehabilitation
in water distribution systems.
e) Contributions of the paper. The method presented in this paper may be used for the feasibility studies
elaborated for water distribution networks.

2. Materials and methods

2.1. Materials

In order to perform the rehabilitation of the water distribution networks, one may use the classic methods with
trenches or the trenchless technologies.
Some of the trenchless technologies widely used for the rehabilitation of water distribution systems are:
Compact-Pipe, Sliplining, Subline, CIPP, GFK-Liner, Swagelining, Rolldown, Short Liner, Berstlining, Pilot Pipe
and Microtunneling.
The technologies currently used for the rehabilitation of water distribution systems are the ones presented in
Table 1.

Table 1. Matrix of alternatives [1].

Alternative’s symbol Alternative name Alternative’s symbol Alternative name
A1 Compact Pipe A6 GFK Liner
A2 Slipline A7 Berstlining
A3 Subline A8 Pilot Pipe
A4 Swagelining A9 Microtunneling
A5 CIPP (Cured in place pipe) A10 Open cut
 Ioan Aşchilean et al. / Energy Procedia 112 (2017) 19 – 26 21

Up to 50 years ago, most of the pipes were placed in tranches while nowadays most of the works are performed
using the trenchless technologies.
The water distribution systems require rehabilitation due to both damages and poor water quality [1].

2.2. Methods

In order to select the technologies for the rehabilitation of the pipes from the water distribution systems one shall
use the AHP method. In our opinion, using the AHP method involves 11 steps, as following:
Step 1: Problem identification. In this step we shall identify the practical issue that has to be solved.
Step 2: Establishing the decision-making criteria. Here we shall identify the criteria (objectives) that shall be used
for the selection of the alternatives, while the data shall be written in the decision criteria matrix C = [Cj]. Where j =
1...m, represents the number of criteria [11].
Step 3: Establishing the decision-making alternatives. In this stage, the set of alternatives that can be applied shall
be identified, while the data shall be written in the alternatives matrix A = [Ai]. Where i = 1...n, represents the
number of alternatives [11].
Step 4: Determining of relative weight of criteria by comparing the criteria in pairs.
In this step we shall determine the relative weight of the criteria c = [cij], and their importance in taking the
decision [12], respectively. In order to determine the relative weight of the criteria, we shall perform a pairwise
comparison.
The pair comparisons are made by the decision makers who assess the pairs subjectively (initially based on verbal
assessments, such as “equally important”, “slightly more important”, “absolutely more important”, and so on, and
then by assigning values on a scale from 1 to 9, which represents the importance degree of one attribute towards
another attribute). If the comparison between two criteria is reversed, then the importance value equals the reverse of
the direct comparison value [9].
We used the Thomas L. Saaty scale for this purpose. For further details, please see Table 2.

Table 2. Fundamental scale of Thomas L. Saaty [13].

Values/Rates Description Values/Rates Description
1 Equally preferred or it does not matter 6 Strongly preferred towards obviously
(equal importance) preferred
2 Equally preferred, but with certain 7 Obviously preferred
moderate differentiation tendencies
3 Moderately preferred 8 Obviously preferred towards
extremely preferred
4 Preferred towards strongly preferred 9 Extremely preferred
5 Strongly preferred

Then we fill in the data into a square matrix with “m” elements, where “m” is the number of decisional criteria.
The table shall contain the values resulted from the comparison between the criteria. Then, by performing the
calculations for the ratios 1/2…1/9, the data shall be filled in a new matrix of pairwise comparisons between criteria.
Also, this matrix shall contain the total on every column, which is calculated based on the following formula:

m
Sj ¦ cji
j 1 (1)

Step 5: Normalizing the comparisons between criteria. The normalized values ’’nij’’ are obtained by dividing the
value obtained as a result of comparison with the total value of their column [9], calculation based on the following
formula:
22 Ioan Aşchilean et al. / Energy Procedia 112 (2017) 19 – 26

cij (2)
nij
Sj

Then, the pairwise comparison between criteria is transformed in weights, these weights being calculated as an
average of the normalized values on each row, based on the Formula (3), as follows:

m (3)
¦ nij
j i
kj
m

where: kj represent the importance coefficients (weights) of the decision criteria.

Considering that we use normalized values, the following condition must be observed:

m (4)
¦ kj 1
j 1

Step 6: Determining the consistency factor of the decision criteria matrix. In order to determine the consistency
factor of the matrixes, we shall perform the following steps [9]:
a) Determining the vector of priorities - λmax. The vector of priorities is calculated as an average of multiplication
between the matrix of relative weights of decision criteria and the average weight of decision criteria, based on the
Formula (5), as follows:

m (c ˜ k ) j (5)
O max ¦
j 1 m ˜ kj

where: (c · k)j represent the elements of the matrix vector determined as a result of multiplying the “c” matrix
with “k” vector [9].
b) Establishing the average stochastic uniformity coefficient. The average stochastic uniformity coefficient,
marked ’’R’’, is determined depending on the rank of the analyzed matrix, marked ’’m’’, based on the following
table 3 [9]:

Table 3. Values of the average stochastic coefficient depending on the rank of the matrix [14].
(Order of matrix) 1 2 3 4 5 6 7 8 9 10
R 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49

c) Determining the uniformity coefficient. The uniformity coefficient “CI” is calculated based on the Formula (6),
as follows:

O max  m (6)
CI
m 1

d) Determining the consistency factor of the matrixes. The consistency factor of matrixes “CR” is calculated
based on the Formula (7) and Formula (8), as follows:

CR = CI, if m = 1 or 2; (7)
 Ioan Aşchilean et al. / Energy Procedia 112 (2017) 19 – 26 23

CI
CR
R , if m> 2; (8)

When determining the consistency relation, one takes into account the following rule: if CR< 0.10, than the
matrix is considered to be consistent, namely the vector of the weights is well determined.
Step 7: Determining the relative weight of the alternatives based on criteria. The procedure of comparing the
alternatives is identical with the one related to criteria, and the results are recorded in a square matrix with “m”
elements, where “m” is the number of alternatives. The number of matrixes is equal to the number of criteria [9].
Step 8: Normalizing the comparisons between the alternatives in relation with each decisional criterion.
Practically, step 9 supposes the transformation into weights of the comparisons between alternatives, in relation
with each criterion. The normalized values are obtained by dividing the value obtained out of the comparison to the
total of the column to which it belongs [9].
Step 9: Filling in the performance matrix, where the performance of the alternatives shall be identified for each
criterion, and the data shall be written in the performance matrix P = [Pij] [11].
Step 10: Determining the total value for the priority of each alternative. In this step we shall multiply the weight
of each alternative related to each criterion with the weight of each criterion and then we calculate their sum [9]:

m (9)
Pi ¦ pij ˜ kj
j 1

where: Pi represent the total value for the priority of each alternative; pij – the weight of each alternative related
to each criterion.
Considering that we use normalized values, the following condition must be observed:

n (10)
¦ pij 1
i 1

Step 11: Making the decision. The optimum alternative is the one for which the sum of the multiplications
between the weight of each alternative and the weight of each criterion has the highest value:

m (11)
Aopt max ¦ pij ˜ kj
j 1

3. Case study, results and discussions

3.1. Case study

In order to exemplify this, we shall present a case study concerning the rehabilitation of a drinking water
distribution network from Cluj-Napoca Municipality, and as a method of determining the priorities concerning the
rehabilitation of the water distribution network we shall use the AHP method.
Considering the important water losses from the water distribution system of Cluj-Napoca Municipality, one
must set a rehabilitation and modernization plan, and when elaborating such a plan one must take into account the
lack of homogeneity of the system [1].
Step 1: Problem identification. The purpose of this paper is to select the optimum technology for the rehabilitation
of the pipes from the domestic water supply system in Cluj-Napoca, Romania.
Step 2: Determining the decision criteria. Identifying the decision criteria C = [C1, C2, ..., Cm], based on which
we shall determine the performance of alternatives [12]. For the case study analyzed in this paper we shall use seven
decision criteria, as presented in Table 4.
24 Ioan Aşchilean et al. / Energy Procedia 112 (2017) 19 – 26

Table 4. The set of decision criteria.

No. Criterion Name of criteria Type Description
1 C1 Diameter of the pipe maximized It is advisable to select that alternative that can be used
for the entire range of pipes used in water distribution
networks.
2 C2 Length of the pipe maximized It is advisable to select that alternative that can be used
for the longest possible pipelines.
3 C3 Period of time required for minimized It is preferable the installation to be as quick as possible.
installation
4 C4 Lifespan ratio between the maximized The lifespan of the rehabilitated pipe must be higher
rehabilitated pipe and the than the lifespan of the replaced pipe.
not rehabilitated pipe
5 C5 Pressure losses minimized The pressure losses should be as low as possible.
6 C6 Price minimized The price for replacing the pipes should be as low as
possible.
7 C7 Installation conditions minimized The alternative should not set special installation
conditions.

Step 3: Determining the alternatives. In this step we shall determine the decision alternatives A = [A1, A2, ..., An]
[12].
In order to establish the optimal technology of pipe rehabilitation in water distribution systems, initially the
existing technologies on the market must be analyzed and the ones compatible with the specific demands of the
project must be established, followed by their ranking.
After a market analysis, in this study, detailed information about only 10 rehabilitation technologies, among the
most representative ones, were chosen and obtained, specified as follows table no. 1.

3.2. Results and discussions

Step 4: Determining of relative weight of criteria by comparing the criteria in pairs.

In Step 4 we shall determine the relative weight of the seven decision criteria as compared to the next upper
hierarchy rank, namely the goal of the study.
In the table 5 we presented the values of the comparisons between criteria, using the fundamental scale of
Thomas L. Saaty (see table 2).
On the matrix’ diagonal one assigns the value 1, because by comparing a criterion with itself one obtains the
same comparison, namely the value 1 [15].
In order to fill in the entire matrix, one must note the following: if the criterion C2 is third times more preferred
than the criterion C3, then the criterion C3 is 1/3 times less preferred than criterion C2. Thus, if the criterion C2
receives the mark 3, then the criterion C3 shall have the mark 1/3.

Table 5. Values of the comparisons between criteria.

C1 C2 C3 C4 C5 C6 C7 C1 C2 C3 C4 C5 C6 C7
C1 1 1/3 1 1/3 1/5 1/5 1/3 C5 5 3 5 3 1 1 3
C2 3 1 3 1 1/3 1/3 1 C6 5 3 5 3 1 1 3
C3 1 1/3 1 1/3 1/5 1/5 1/3 C7 3 1 3 1 1/3 1/3 1
C4 3 1 3 1 1/3 1/3 1

Then we shall complete the matrix of pairwise comparisons of criteria based on data taken from the Table 5, and
the totals corresponding to each column are calculated based on Formula (1).
 Ioan Aşchilean et al. / Energy Procedia 112 (2017) 19 – 26 25

Step 5: Normalizing the comparisons between criteria. Then, the pairwise comparison between criteria is
transformed in weights based on the Formula (2) and Formula (3), and the final result is presented in Table 6. As one
can see in Table 6, the sum of columns equals to 1, hence the condition required by Formula (4) is observed.

Table 6. Normalized for decision criteria matrix.

C1 C2 C3 C4 C5 C6 C7 Total Medium value
C1 0.05 0.03 0.05 0.03 0.06 0.06 0.03 0.32 0.045
C2 0.14 0.10 0.14 0.10 0.10 0.10 0.10 0.79 0.113
C3 0.05 0.03 0.05 0.03 0.06 0.06 0.03 0.32 0.045
C4 0.14 0.10 0.14 0.10 0.10 0.10 0.10 0.79 0.113
C5 0.24 0.31 0.24 0.31 0.29 0.29 0.31 2.00 0.285
C6 0.24 0.31 0.24 0.31 0.29 0.29 0.31 2.00 0.285
C7 0.14 0.10 0.14 0.10 0.10 0.10 0.10 0.79 0.113
Total 1.00 1.00 1.00 1.00 1.00 1.00 1.00 7.00 1.00

Step 6: Determining the consistency factor of the decision criteria matrix. We have seven decision criteria in this
case study, then according to Table 3, if m = 7 then R = 1.32.
We shall further perform the calculations based on Formula (5)–(8), and the results obtained are, as follows: λmax
= 7.16; CI = 0.014; RI = 1.410; CR = 0.011. Considering that the calculated value of CR is lower than 0.10, then the
decision criteria matrix is consistent, namely the weights vector is clearly defined.
Step 7: Determining the relative weight of the alternatives based on criteria is performed in the same manner as in
Step 4. Due to the space restrictions, we shall go to the next step.
Step 8: Normalizing the comparisons between alternatives according to each decision criterion is performed in the
same manner as in Step 5. Due to the space restrictions, we shall go to the next step.
Step 9: Completing the performance matrix. Step 9 consists of determining the performance of the ten alternatives
in connection with the seven decision criteria.
Step 10: Determining the global priority value of each alternative. The values of each alternative’s global priority
are calculated based on Formula (9), and the results obtained from the calculations performed are presented in Table
7 and in Figure 1.

Tabel 7. Global priority value of the alternatives. A10

A9
Alternative’s Alternative name Total score Place
A8
symbol
A7
Alternative

A1 Compact pipe 0.1339 2

A6
A2 Slipline 0.1527 1
A5
A3 Subline 0.1134 3
A4
A4 Swagelining 0.0733 10 A3
A5 Cured in place pipe (CIPP) 0.0736 9 A2
A6 GFK Liner 0.0819 8 A1
A7 Berstlining 0.0872 6 0,00 0,05 0,10 0,15 0,20
A8 Pilot Pipe 0.0972 5 Total score
A9 Microtunneling 0.1007 4
A10 Open cut 0.0860 7 Fig. 1. Global priority value of the alternatives.

Step 11: Making the final decision. Analyzing the Table 7 and Figure 1, one may notice that the alternative no. 2
has the highest global priority score, while the alternative no. 1 ranks second and the alternative no. 3 ranks third.
26 Ioan Aşchilean et al. / Energy Procedia 112 (2017) 19 – 26

4. Conclusions

Based on this study, we recommend that the rehabilitation of the Cluj-Napoca water distribution networks should
be performed by implementing the alternative no. 2, by applying the Slipline method respectively.
Obviously, in the assessment process, one may take into account as many versions and as many criteria as he
desires, and thus the selection of the pipes from the water distribution systems will be more precise, but at the same
time one shall have to make more calculations.
The necessary calculations are quite complex. In practice, these calculations should be performed using a
software program, such as Expert Choice [10].The AHP method presented above may be analogously used in order
to choose other types of construction installations too.

References

[1]Aşchilean I. Reabilitarea şi modernizarea sistemelor de alimentare cu apă a localităţilor urbane (Rehabilitation and modernization of water
supply in urban). Cluj-Napoca: Editura Risoprint; 2014.
[2]Romania. SR 1343-1:2006 Alimentări cu apă. Determinarea cantităţilor de apă potabilă pentru localităţi urbane şi rurale (Water supply.
Determining the quantities of drinking water for towns and villages). Bucureşti: ASRO; 2006.
[3] Shamir U, Howard C D D. An analytic approach to scheduling pipe replacement. Journal AWWA 1979; 5:248-58.
[4] KleinerY, Adams B J, Rogers J S. Water distribution network renewal planning. ASCE Journal of Computing in Civil Engineering 2001;
15/1:15-26.
[5] Park S W, Loganathan G V. Methodology for economically optimal replacement of pipes in water distribution systems: 1. Theory. KSCE
Journal of Civil Engineering 2002; 6/4:539–43.
[6] Giustolisi O, Laucelli D, Savic D A. Development of rehabilitation plans for water mains replacement considering risk and cost-benefit
assessment. Civil Engineering and Environmental Systems 2006; 23/3:175-90.
[7] Bana C A, Vansnick J-C. A critical analysis of the eigenvalue method used to derive priorities in AHP. European Journal of Operational
Research 2008; 187:1422–8.
[8] Turcksina L, Bernardinia A, Macharisa C. A combined AHP-PROMETHEE approach for selecting the most appropriate policy scenario to
stimulate a clean vehicle fleet. Procedia Social and Behavioral Sciences 2011; 20: 954–65.
[9]Dobrea R. Eficienta modernizării sistemelor tehnico-economice (Efficiency of modernization of technical and economical systems) [PhD
thesis]. Bucureşti: Academia de Studii Economice; 2006. p. 232-235.
[10]Roman M. Analiza multi-criterială. Manual (Handbook - Multi-criteria Analysis). Bucureşti: Academia de Studii Economice; 2012. p. 18-21.
[11] Naghiu G S, Giurca I, Aşchilean I, Badea G. Multicriterial analysis on selecting solar radiation concentration ration for photovoltaic panels
using Electre-Boldur method. Proceedings of the 9th International Conference Interdisciplinarity in Engineering, INTER-ENG 2015; 2015
October 8-9, Tirgu-Mures, Romania. Amsterdam: Procedia Technology; 2016;22:773-780.
[12] Prejmerean V. Sisteme pentru fundamentarea deciziilor (Systems for Substantiating Decisions) [internet]. Cluj-Napoca: Universitatea
Babeş Bolyai; 2012. Cursul 5 Teoria Deciziilor (Course 5 Decisions). p. 13. [cited 2015 June 17]. Available from:
http://www.cs.ubbcluj.ro/~per/Dss/Dss_5.pdf. p. 13.
[13] Saaty T L. The Analytic Hierarchy Process. Planning, Priority Setting, Resource Allocation. New York: McGraw-Hill; 1980. p. 283.
[14] Winston W L. Operations Reseach. Applications and Algorithms. Third Edition. Belmont, CA: International Thomsom Publishing
Company; 1994. p. 1400.
[15] Constantin S L, Constantin B V. Metodologie de alegere între reţelele GPRS, UMTS şi WLAN prin metoda decizională AHP
(Methodology of selection between the GPRS, UMTS and WLAN networks by means of AHP method). Telecomunicaţiii 2010; 1:
http://www.agir.ro/buletine/859.pdf.