10120130405014

International Journal of Management (IJM), ISSN 0976 – MANAGEMENT (IJM)
INTERNATIONAL JOURNAL OF 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 5, September - October (2013)

ISSN 0976-6502 (Print)
ISSN 0976-6510 (Online)
Volume 4, Issue 5, September - October (2013), pp. 109-126
© IAEME: www.iaeme.com/ijm.asp
Journal Impact Factor (2013): 6.9071 (Calculated by GISI)
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IJM
©IAEME

READY MIXED CONCRETE SELECTION FOR INFRASTRUCTURE
DEVELOPMENT THROUGH ANALYTIC HIERARCHY PROCESS (AHP) IN
THE NEW MILLENNIUM
Ashish H. Makwana1, Prof. Jayeshkumar Pitroda2
1

Student of final year M.E. C. E. & M., B.V.M. Engineering College, Vallabh Vidyanagar
2
Assistant Professor and Research Scholar, Civil Engineering Department,
B.V.M. Engineering College, Vallabh Vidyanagar– Gujarat – India.

ABSTRACT
The Analytic Hierarchy Process (AHP) is a well-known multi-criteria decision making
method that has been applied to solve problems in diverse areas. This method was developed by Dr.
Thomas L. Saaty in 1970s as a tool to help with solving technical and managerial problems. During
the past decade, the construction industry in India witnessed remarkable growth, in which the readymixed concrete (RMC) industry can claim to be a proud partner. Historically speaking, India missed
the benefits of RMC technology for decades. It was only in the early nineties that the industry was
born, but really commenced from the second half of the nineties. During the past few years, housing
and infrastructure have remained the major expansion area. Faster speed and improved quality of
concrete have been the two major demands of these sectors. Ready-mixed concrete was the right
solution for this and it was heartening to see that the RMC industry responded positively to these
demands. The result was the rapid growth of the RMC industry. The industry, which was initially
confined to metropolitan cities, later spread to the two-tier and three-tier cities, vindicating the fact
that RMC was a right solution for different markets. The growth of the RMC industry brought in its
wake certain challenges, chief amongst which was about the quality of concrete supplied by RMC
plants.
KEYWORDS: Analytic Hierarchy Process (AHP), Construction Industry, Ready Mixed Concrete
(RMC), quality, growth, plants.

109

International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),

INTRODUCTION
Ready-Mixed Concrete (IS: 4926-2003) as “Concrete mixed in a stationary mixer in a central
batching and mixing plant or in a truck mixer and supplied in the fresh condition to the purchaser
either at the site or into the purchaser’s vehicles.” [10]
Ready mixed concrete (RMC) is a specialized material in which cement, aggregate, and other
ingredients are weigh batched at a plant in a central or truck mixer before delivery to the construction
site in a condition ready for placing by the customer. RMC is manufactured at a place away from the
construction site, the two locations being linked by a transport operation. IS: 4926-2003 defines
ready mixed concrete as 'Concrete mixed in a stationary mixer in a central batching and mixing plant
or in a truck mixer and supplied in a fresh condition to the purchaser either at site or into purchaser's
vehicle. [4]
The short 'life' of fresh concrete, with only 2-3 hours before it must be placed, results in ready
mixed concrete being a very much local delivery service, with rarely more than 30-60 minutes
journey to the construction site. The need for supply of ready mixed concrete to fit in with the
customer's construction program means that RMC has to be both a product and a delivery service.
This means that the ready mixed supplier is in two separate businesses — firstly, processing
materials and secondly, transporting product with a very short life. [4]
When researchers refer to the customer, researchers are speaking in effect of two customers.
As far as the product is concerned, concrete must satisfy not only the person who is using it, i.e., the
builder or contractor, but also the authority responsible for defining the properties. However, the
ready mix supplier has only one contract and that is with the builder or contractor and relies on the
latter to define exactly the requirements of die specifying authority (engineer). [4]
The basic product in ready mix concrete is fresh concrete, which is placed on site by the
customer. It is distinct from hardened, precast concrete units. The introduction of ready mixed
concrete has gradually replaced the operation in which the contractor made his own concrete on site.
When ready mix concrete was first introduced, engineers and contractors with considerable expertise
in concrete production and quality control were suspicious of the quality of this new product, whose
manufacture was no longer under their control. Ready mix concrete suppliers need to have stringent
quality control for their product and its delivery, so that customer's apprehensions regarding the
quality of concrete supplied by them are taken care. It will take a while before the customer places
his confidence and trust in the product and services offered by the supplier. [4]
Experience shows that the specifying authority or engineer will be satisfied with ready mixed
concrete if,
(1) The supply complies with the specification for fresh and hardened concrete; (2) He is
assured of continuity of suppliers from experienced and reliable ready mix concrete companies. [4]
In turn, the contractor or builder will be satisfied if,
(1) The deliveries are always on time and concrete is supplied at the required rate, (2) The
workability is correct and appropriate for the placing method used, (3) The quantities are correct, (4)
On those occasions when concrete proves to be defective, the supplier bears his fair share of the cost
of removal and replacement of the defective material, (5) The total cost of concrete, including
supply, handling, and placing, is economic. From this, it is seen that the specifying authority
(engineer) is concerned primarily with the quality of the product, whereas the user, i.e., builder or
contractor, is mainly concerned with the service and its cost, i.e., value for his money. [4]

110


Figure 1: Modern Ready Mixed Concrete Plant
(Source: JAGAJI Construction Janta Circle, Opp. Elecon Company, Vallabh Vidyanagar – Anand –
Gujarat)

Figure 2: Modern Ready Mixed Concrete Plant
(Source: RMC India pvt. Ltd. Vadodara, Gujarat)
LITERATURE REVIEW
Ready mixed concrete was first patented in Germany in 1903, but means of transporting was
not sufficiently developed by then to enable the concept to be utilized commercially. The first
111


commercial delivery of ready mixed concrete was made in Baltimore, USA in 1913 and the first
revolving-drum-type transit mixer, of a much smaller capacity than those available today, was born
in 1926. In 1920s and 1930s, ready mixed concrete was introduced in some European countries. [4]
Some early plants were of very small capacities. In 1931, a ready mixed concrete plant set up at what
is now Heathrow airport, London, had 1.52m capacity central mixer, supplying six 1.33 m3 capacity
agitators with an output of 30.58 m3/h. Aggregates were stored in four compartments, each of 76.45
m3 capacity. Cement was handled manually in bags. Till the beginning of World War II, there were
only six firms producing ready mixed concrete in UK. After the War, there was a boost to the ready
mixed concrete industry in whole of Europe. In mid 1990s, there were as many as 1100 RMC plants
in the UK, consuming about 45% of cement produced in the country. [4]
European Ready Mixed Concrete Organization (EMRO) was formed in Europe in 1967. In
1997, some 5850 companies having a large turnover were represented by it. Cement consumption in
RMC plants ranged from 33% to 62% of total cement sales. [4]
In USA, till 1933, only 5% of cement produced was utilized through RMC. ASTM published
first specification for ready mixed concrete in 1934. The RMC industry in USA progressed steadily.
During 1950-4975, RMC industry consumption of total OPC in the USA increased form (l/3) rd to
(2/3) rd and by 1990 to 72.4%. There were 5000 RMC companies in that country by 1978. [4]
In Japan, the first RMC plant was set up in 1949. Initially, dump trucks were used to haul concrete of
low consistency for road construction. In early 1950s mixing type trucks were introduced. Since then
there has been a phenomenal growth of the industry in that country. By the end of 1970s there were
4462 RMC plants in Japan. By 1992 Japan was the largest producer of RMC, producing 181.96
million tons of concrete. In many countries, including some developing countries such as Taiwan,
Malaysia, Indonesia, as well as certain countries in the Gulf region, RMC industry is well developed
today. [4]
Ready mixed concrete plants arrived in India in early 1950s, but their use was restricted to
only major construction projects such as dams. Later RMC was also used for other projects such as
construction of long-span bridges, industrial complexes, etc. These were, however, captive plants
which formed an integral part of the construction projects. It was during 1970s when the Indian
construction industry spread its tentacles overseas, particularly in the Gulf region, that an awareness
of ready mixed concrete was created among Indian engineers, contractors, and builders. Indian
contractors in their works abroad started using RMC plants of 15 to 60 m3/h, and some of these
plants were brought to India in 1980s. Currently there are many ready mix plants operating in
different parts of India, especially in metropolitan cities and towns. [4]
NEED OF READY MIXED CONCRETE SELECTION USING ANALYTIC HIERARCHY
PROCESS
The conventional Ready Mixed Concrete selection approach may sometime towards
improper Ready Mixed Concrete selection which brings partial failure of the project. Present Ready
Mixed Concrete selection process of construction companies in Central Gujarat Region of India was
studied in the beginning of this Research work. Present approach lacks scientific methodology and
does not consider multi-criteria in decision making. There is a need of scientific methodology for
Ready Mixed Concrete selection approach. Such approach will provide the best selection of Ready
Mixed Concrete considering all aspects of the process.
Hence, the need of this Research work based upon various utility measures like quality control, cost,
delivery, quantity at which owners or plant manager have to concentrate for enhancing profit as well
as maintaining standard by Analytic Hierarchy Process (AHP) which will help the decision maker to
understand the problem systematically.
112


ADVANTAGES OF READY MIXED CONCRETE
Advantages of Ready Mixed Concrete are well recognized. Some of these are given below:
Uniform and assured quality of concrete: Since RMC is factory produced, the raw material
and production process quality is better than conventional site mixed concrete.
Durability of concrete: RMC can ensure correct W/C ratio to be maintained. Hence the
durability of RMC is consistent and better.
Faster construction speed: In site mixed concrete, the contractor needs to mobilize labour for
mixing as well as placing. In RMC, fresh concrete is supplied in a place able condition and can
directly be placed by pumping. Hence a faster construction speed can be achieved.
Elimination of storage needs at the construction site: In case of site mixed concrete; all raw
materials such as aggregates, sand, and cement have to be stored at the site. In urban situations
and when the work is progressing close to the highways, there is a problem of storage of raw
materials affecting smooth flow of traffic. In case of RMC, this problem is completely avoided
as the storage of materials takes place at the central plant.
Easier admixture addition: In RMC admixtures can be added in a controlled manner because
of the use of sophisticated computer-controlled methods of releasing exact quantities needed.
This is not possible in normal concreting.
Documentation of mix designs: The contractor purchases fresh concrete from the supplier of
RMC, who is responsible not only for documentation but also for maintaining the records.
Reduction in wastage of material: In RMC materials are stored in bulk and used in bulk.
Hence wastage that occurs in loose handling of cement, etc. is completely avoided.
RMC is eco-friendly: The production of RMC is done in an environmentally assessed and
licensed central plant. Hence, dust and noise pollution which is inevitable in concrete is avoided.
[4]

DISADVANTAGES OF READY MIXED CONCRETE
Disadvantages of RMC are well recognized. Some of these are given below:
Need huge initial investment.
Not affordable for small projects (small quantity of concrete).
Needs effective transportation system from R.M.C. to site.
Traffic jam or failure of the vehicle creates a problem if the proper dose of retarder is not given.
Labors should be ready on site to cast the concrete in position to vibrate it and compact it.
Double handling, this results in additional cost and losses in weight, requirement of go downs
for storage of cement and large area at site for storage of raw materials.
Aggregates get mixed and impurities creep in because of wind, weather and mishandling at site.
Improper mixing at site, as there is ineffective control and intangible cost associated with
unorganized preparation at site are other drawbacks of RMC.
There are always possibilities of manipulation; manual error and mischief as concreting are done
at the mercy of gangs, who manipulate the concrete mixes and water cement ratio. [2]
OF THE STUDY
This paper has an objective to develop criteria framework which contributes to Bricks
selection. Secondly, it suggests a case study based Analytic Hierarchy Process (AHP) for Bricks
selection.

113


A CASE STUDY BASED CRITERIA FRAMEWORK FOR BRICKS SELECTION
Bricks selection depends upon many factors. Literature study and interview with construction
professionals were carried out to prepare the hierarchical framework for bricks selection. Criteria
which contribute towards bricks selection are divided in four major groups such as: Clay bricks,
Human hair bricks, Fly ash (FAL-G) bricks, Sugarcane bassage ash bricks. These criteria are further
subdivided into sub criteria. A final framework for Brick selection criteria is given in Figure 3.
ABRAVIATION
CL – Clay Bricks
CS - Cost
TM - Time
QL - Quality
QN – Quantity

HHB - Human Hair Bricks
CS - Cost
TM - Time
QL - Quality
QN – Quantity

FAB - Fly Ash (FAL –G) Bricks
CS - Cost
TM - Time
QL - Quality
QN – Quantity

SBAB - Sugarcane Bassage Ash
Bricks
CS - Cost
TM - Time
QL - Quality
QN - Quantity

Figure 3: Framework for bricks selection (a case study) – Indian context
The purpose of this research paper is to develop a ranking of criteria which are responsible
for bricks selection (a case study). According to the Analytical Hierarchy Process (AHP),
development of the Criteria Framework (Figure 3) in Indian context is having total 4 numbers of subcriteria’s which are identified for each type of bricks typically which are Quality, Quantity, Delivery
and Cost which affect the bricks selection problem. Main Criteria for bricks selection are: Fly ash
(FAL-G) bricks, Sugarcane bassage ash bricks, Human hair bricks, Clay bricks.
114


ANALYTIC HIERARCHY PROCESS
Analytic Hierarchy Process has been a tool at the hands of decision makers and researchers;
and it is the most widely used multiple criteria decision making tools [19]. The AHP method is
developed by Thomas L. Saaty in 1980 [16]. AHP is very popular and widely applicable in various
fields due to its simplicity, ease of use and flexibility [17]. AHP is a reliable tool to facilitate
systematic and logical decision making processes and determine the significance of a set of criteria
and sub-criteria. AHP method is very suitable for complex social issue in which intangible and
tangible factors cannot be separated [11]. AHP helps in reducing bias in decision-making and it can
minimize common pitfalls of team decision-making process, such as lack of focus, planning,
participation or ownership, which ultimately are costly distractions that can prevent teams from
making the right choice [5, 6, and 7].
The AHP is based on the experience gained by its developer, T. L. Saaty, while directing
research projects in the US Arms Control and Disarmament Agency. It was developed as a reaction
to the finding that there is a miserable lack of common, easily understood and easy-to-implement
methodology to enable the taking of complex decisions. Since then, the simplicity and power of the
AHP has led to its widespread use across multiple domains in every part of the world. The AHP has
found use in business, government, social studies, R&D, defence and other domains involving
decisions in which choice, prioritization or forecasting is needed. [12]
Owing to its simplicity and ease of use, the AHP has found ready acceptance by busy
managers and decision-makers. It helps structure the decision-maker’s thoughts and can help in
organizing the problem in a manner that is simple to follow and analyze. Broad areas in which the
AHP has been applied include alternative selection, resource allocation, forecasting, business process
re-engineering, quality function deployment, balanced scorecard, benchmarking, public policy
decisions, healthcare, and many more. Basically the AHP helps in structuring the complexity,
measurement and synthesis of rankings. These features make it suitable for a wide variety of
applications. The AHP has proved a theoretically sound and market tested and accepted
methodology. Its almost universal adoption as a new paradigm for decision-making coupled with its
ease of implementation and understanding constitute its success. More than that, it has proved to be a
methodology capable of producing results that agree with perceptions and expectations. [12]
The importance of the AHP, its variants, and the use of pairwise comparisons in decision
making is best illustrated in the more than 1,000 references cited in [Saaty, 1994]. A number of
special issues in refereed journals have been devoted to the AHP and the use of pairwise
comparisons in decision making. These issues are: Socio-Economic Planning Sciences [Vol. 10,
No.6, 1986]; Mathematical Modeling [Vol. 9, No. 3-5, 1987]; European Journal of Operational
Research [Vol. 48, No.1, 1990]; and Mathematical and Computer Modeling [Vol. 17, No. 4/5, 1993].
Also, four international symposia (called ISAHP) have been dedicated on the same topic so far and
one such event is now scheduled every two years. [12]
STEP BY STEP PROCEDURE OF ANALYTIC HIERARCHY PROCESS
The procedure for using the AHP can be summarized as:
Define the problem and determine the kind of knowledge sought.
Structure the decision hierarchy from the top with the goal of the decision, then the objectives
from a broad perspective, through the intermediate levels (criteria on which subsequent elements
depend) to the lowest level (which usually is a set of the alternatives).
Construct a set of pairwise comparison matrices. Each element in an upper level is used to
compare the elements in the level immediately below with respect to it.
115


Use the priorities obtained from the comparisons to weigh the priorities in the level immediately
below. Do this for every element. Then for each element in the level below add its weighed
values and obtain its overall or global priority. Continue this process of weighing and adding
until the final priorities of the alternatives in the bottom most level are obtained. [15]
To make comparisons, Researchers need a scale of numbers that indicates how many times more
important or dominant one element is over another element with respect to the criterion or
property with respect to which they are compared. Table No. 1 exhibits the scale.
Table No. 1: Fundamental Scale of Absolute Numbers
INTENSITY OF
IMPORTANCE

DEFINATION

1

Equal Importance

2

Weak or slight

3

Moderate importance

4

Moderate plus

5

Strong importance

6

Strong plus

7

Very strong or
Demonstrated importance

8

Very, very strong

9

Extreme importance

RESIPROCALS OF
ABOVE (1-9)

If activity i has one of the above nonzero numbers assigned to it when
compared with activity j, then j has the
reciprocal value when compared with i

A reasonable assumption

If the activities are very close

May be difficult to assign the best value
but when compared with other
contrasting activities the size of the small
numbers would not be too noticeable, yet
they can still indicate the relative
importance of the activities.

1.1–1.9

EXPLATION
Two activities contribute equally to the
objective
Experience and judgement slightly
favour one activity over another
Experience and judgement strongly
favour one activity over another
An activity is favoured very strongly over
another; its dominance demonstrated in
practice
The evidence favouring one activity over
another is of the highest possible order of
affirmation

(Source: Saaty, T.L. (2008) ‘Decision making with the analytic hierarchy process’, Int. J. Services
Sciences, Vol.1, No.1, pp.83–98) [15]
APPLICATION OF ANALYTIC HIERARCHY PROCESS
It is widely used for decision making. AHP technique is widely applied to various fields as given
below:
Choice - The selection of one alternative from a given set of alternatives, usually where there
are multiple decision criteria involved.
Ranking - Putting a set of alternatives in order from most to least desirable.
Prioritization - Determining the relative merit of members of a set of alternatives, as opposed to
selecting a single one or merely ranking them.
Resource allocation - Apportioning resources among a set of alternatives.

116


Benchmarking - Comparing the processes in one’s own organization with those of other bestof-breed organizations.
Quality management - Dealing with the multidimensional aspects of quality and quality
improvement.
Conflict resolution - Settling disputes between parties with apparently incompatible goals or
positions. [1]
ADVANTAGES OF ANALYTIC HIERARCHY PROCESS
It illustrates how possible changes in priority at the upper levels have an effect on the priority
of criteria at lower levels.
The method is able to rank criteria according to the needs of the buyer which also leads to more
precise decisions concerning supplier selection.
It provides the buyer with an overview of criteria, their function at the lower levels and goals
at the higher levels.
PROPOSED READY MIXED CONCRETE SELECTION PROCESS
Ready Mixed Concrete selection is a multi-criteria decision making problem and hence AHP
fits to it. It is suggested to use AHP technique for Ready Mixed Concrete selection. So, a survey
questionnaire can be prepared based on AHP technique. It will require the experts to compare
various criteria and sub-criteria on 1 to 9 scales. While doing this comparison they have to use their
past knowledge and information of criteria as well as available Ready Mixed Concrete Plants.
Figure 4 - Explains proposed AHP based Ready Mixed Concrete selection process.
WEIGHTS ALLOCATION
With the help of AHP approach, by doing pair wise comparisons from all respondents,
weights for all sub-criteria’s are calculated. Eigen vector method (EM) is used to derive local
weights for each sub-criterion. The preference weights given by each respondent is aggregated by
Geometric mean method (GMM), as GMM is more consistent with the meanings of both judgments
& priorities in AHP [9]. When the GMM is used as the prioritization procedure, the group
inconsistency is at least as good as the worst individual inconsistency for aggregation approaches [9].
In AHP, two different approaches can be adopted for group decision making: the aggregation of
individual judgments (AIJ) and the aggregation of individual priorities (AIP) [14]. In this research,
AIP method is used; as each respondent is acting in his or her rights and not working together as
team member. In addition, group member are considered to be of equal importance.
Priorities from individual expert are synthesized into a single priority through geometric
mean in order to get an overall estimate of the priorities for each criterion in every level of hierarchy.
The geometric mean for synthesizing individual priorities is expressed in Eq. (1) and (2).

=
… (1)
=

… (2)

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Here,
G = Geometric mean of individual priorities,
a = Priority weight given by expert
n = Number of experts
The Global weight of each sub-criteria is calculated as per Eq. (3) [13]

… (3)
Where:
i = 1, 2, 3…….n = main criteria, sub-criteria at each level
WM, i = Local Weight of Main criteria, W S, i = Local Weight of Sub-criteria
At every level

=1

=1

…..
(4)

According to the AHP the best alternative (in the maximization case) is indicated by the following
relationship [8]

..…(5)

Figure 4: Proposed AHP based Ready Mixed Concrete selection process

118


A CASE STUDY BASED ON BRICKS SELECTION USING ANALYTIC HIERARCHY
PROCESS (AHP)
i.

Pairwise Comparison Matrices for the main criteria and its analysis
Table No. 2: Pairwise Comparison Matrices for the Main Criteria
1.00
1.00

Fly ash (FAL - G)
bricks
0.25
0.25

Sugarcane bassage
ash bricks
0.20
0.33

4.00

4.00

1.00

3.00

5.00

3.00

0.33

1.00

11.00

9.00

1.83

4.53

Criteria

Clay bricks

Human hair bricks

Clay bricks
Human hair bricks
Fly ash (FAL - G)
bricks
Sugarcane bassage ash
bricks
TOTAL

1.00
1.00

Now, Normalised matrices is found by dividing each component of matrices by appropriate column
sum.
Table No. 3: Normalised Matrices for Main Criteria
Criteria
Clay bricks (CB)
Human hair bricks
(HHB)
Fly ash (FAL - G)
bricks (FAB)
bricks (SBAB)
TOTAL

Clay bricks
(CB)

Human hair
bricks (HHB)

Fly ash (FAL G) bricks (FAB)

0.09

0.11

0.14

Sugarcane
bassage ash
bricks
(SBAB)
0.04

0.09

0.11

0.14

0.07

0.10

0.36

0.44

0.55

0.66

0.50

0.45

0.33

0.18

0.22

0.30

1.00

1.00

1.00

1.00

1.00

Row
average
0.10

Therefore, local weights of the criteria’s are as follows.
LWCB = 0.10, LWHHB = 0.10, LWFAB = 0.50, LWSBAB = 0.30,
Now, check the consistency of the result.
Lemna max. = sum of [Wi * sum of each column]
Lemna max. = 4.25, and n = 4
Now, find Consistency index (CI) = {Lemna max - n} / (n - 1)
CI = 0.08 and now, Consistency Ratio (CR) = CI / RI
Where, RI (Random Index) = 0.90 (for n = 4),
CR = 0.09 < 0.1 hence OK. (According to T. Satty – the founder of the AHP method)
ii.

Pairwise Comparison Matrices for the Criteria-Clay bricks (CB)
Table No. 4: Pairwise Comparison Matrices for the Criteria-Clay bricks
Criteria

Quality Control (QC)
Quantity (QN)
Delivery (DL)
Cost (CS)
TOTAL

Quality Control
(QC)
1.00
1.00
1.00
4.00
7.00

Quantity (QN)

Delivery (DL)

1.00
1.00
1.00
1.00
4.00

1.00
1.00
1.00
3.00
6.00

119

Cost

(CS)
0.25
1.00
0.33
1.00
2.58


Table No. 5: Normalised Matrices for the Criteria-Clay bricks
Criteria


Quantity (QN)
Delivery (DL)
Cost (CS)
TOTAL

0.14
0.14
0.14
0.57
1.00

Delivery
(DL)
0.17
0.17
0.17
0.50
1.00

Quantity (QN)
0.25
0.25
0.25
0.25
1.00

Cost (CS)

Row average

0.10
0.39
0.13
0.39
1.00

0.16
0.24
0.17
0.43
1.00

LWQC = 0.16, LWQN = 0.24, LWDL = 0.17, LWCS = 0.43,
Lemna max = 4.23, and n = 4
CI = 0.08
Now, Consistency Ratio (CR) = CI / RI
iii.

Pairwise Comparison Matrices for the Criteria- Human Hair Bricks (HHB)
Table No. 6: Pairwise comparison matrices for the Criteria-Human hair bricks
Criteria
Quality Control
(QC)
Quantity (QN)
Delivery (DL)
Cost (CS)
TOTAL


Quantity (QN)

Delivery (DL)

Cost (CS)

1.00

1.00

1.00

1.00

1.00
1.00
1.00
4.00

1.00
1.00
0.50
3.50

2.00
1.00
1.00
5.00

2.00
1.00
1.00
5.00

Table No. 7: Normalised Matrices for the Criteria-Human Hair Bricks
Criteria
Quality Control
(QC)
Quantity (QN)
Delivery (DL)
Cost (CS)
TOTAL

Quality Control
(QC)

Quantity
(QN)

Delivery
(DL)

Cost (CS)

Row
average

0.25

0.29

0.20

0.20

0.23

0.25
0.25
0.25
1.00

0.29
0.29
0.14
1.00

0.40
0.20
0.20
1.00

0.40
0.20
0.20
1.00

0.33
0.23
0.20
1.00

CI = 0.02
120


iv.

Pairwise Comparison Matrices for the Criteria- Fly ash (FAL-G) Bricks (FAB)
Table No. 8: Pairwise Comparison matrices for the Criteria - Fly Ash (FAL-G) Bricks (FAB)


Quality Control
(QC)
1.00

Quantity (QN)

1.00

1.00

2.00

1.00

Delivery (DL)

1.00

0.50

1.00

2.00

Cost (CS)

1.00

1.00

0.50

1.00

TOTAL

4.00

3.50

4.50

5.00

Criteria

Quantity (QN)

Delivery (DL)

Cost (CS)

1.00

1.00

1.00

Now, Normalised matrices are found by dividing each component of matrices by appropriate column
sum.
Table No. 9: Normalised Matrices for the Criteria-Fly Ash (FAL-G) Bricks [FAB]
Criteria

Quality Control
(QC)

Quantity (QN)

Delivery
(DL)

Cost (CS)

Row average


0.25

0.29

0.22

0.20

0.24

Quantity (QN)

0.25

0.29

0.44

0.20

0.30

Delivery (DL)

0.25

0.14

0.22

0.40

0.25

Cost (CS)

0.25

0.29

0.11

0.20

0.21

TOTAL

1.00

1.00

1.00

1.00

1.00

CI = 0.06
v.

Pairwise Comparison Matrices for the Criteria - Sugarcane Bassage Ash Bricks (SBAB)
Table No. 10: Pairwise Comparison matrices for the Criteria – Sugarcane Bassage Ash Bricks
(SBAB)
Criteria

Quality Control
(QC)

Quantity (QN)

Delivery (DL)


1.00

1.00

2.00

1.00

Quantity (QN)

1.00

1.00

1.00

1.00

Delivery (DL)

0.50

1.00

1.00

1.00

Cost (CS)

1.00

1.00

1.00

1.00

TOTAL

3.50

4.00

5.00

4.00

121

Cost

(CS)


Table No. 11: Normalised matrices for the Criteria – Sugarcane Bassage Ash Bricks (SBAB)
Criteria
Quantity (QN)
Delivery (DL)
Cost (CS)
TOTAL

Quality Control
(QC)
0.29
0.29
0.14
0.29
1.00

Quantity
(QN)
0.25
0.25
0.25
0.25
1.00

Delivery
(DL)
0.40
0.20
0.20
0.20
1.00

Cost
(CS)
0.25
0.25
0.25
0.25
1.00

Row
average
0.30
0.25
0.21
0.25
1.00

CI = 0.02
vi.

Overall Global Weight Of The Criteria Of The Case Study
Table No. 12: Overall Global Weight of the Criteria
SR.
NO.

DESCRIPTION

SUB CRITERIAS

R1

R2

R3

R4

GMM

Clay bricks

5.

Sugarcane
bassage ash
bricks

0.5038

0.4193

0.4675

0.4742

0.4576

0.5028

0.3485

0.3257

0.3543

0.3695

0.1641

0.3944

0.4476

0.2470

0.3045

0.2367

0.2389

0.1565

0.2887

0.2385

Delivery

0.1721

0.1972

0.1000

0.1756

0.1699

0.4271

0.1694

0.2673

0.2887

0.2870

0.2464

0.1614

0.2200

0.3000

0.2382

Quantity

0.2964

0.3035

0.3400

0.3000

0.3214

Delivery

0.2464

0.2480

0.2339

0.1964

0.2421

0.2107

0.2872

0.2000

0.1000

0.1984

0.2395

0.2470

0.2964

0.2875

0.2687

Quantity

0.2950

0.2887

0.2464

0.2375

0.2680

Delivery

0.2538

0.2887

0.2000

0.2375

0.2451

Cost

Fly ash (FAL - G)
bricks

Fly ash (FAL - G) bricks
bricks

Quality
4.

0.0932

Cost

Human hair
bricks

0.0798

0.1000

Quality
3.

0.0600

0.1000

Cost

Clay bricks

0.0809

0.1000

Quantity
2.

Main Criteria

0.1253

0.1030

Quality

1.

0.0956

Human hair bricks

0.2117

0.1756

0.2464

0.2375

0.2182

Quality

0.2964

0.2950

0.2396

0.2417

0.2727

Quantity

0.2464

0.2000

0.4063

0.1917

0.2549

Delivery

0.2107

0.2395

0.1771

0.2417

0.2215

Cost

0.2464

0.2538

0.1771

0.3250

0.2509

122


vii.

LOCAL AND GLOBAL WEIGHT OF THE CRITERIA
Table No. 13: Composite priority weights for ‘Main Criteria – Sub Criteria of Bricks in Indian
context
SN.

CRITERIA

LOCAL
WEIGHTS

SUB CRITERIA

LOCAL
WEIGHTS

GLOBAL
WEIGHTS

RANK

Quality
1.

Clay bricks

0.0243

10

0.2385

0.0190

14

Delivery

0.0798

0.3045

Quantity

0.1699

0.0136

16

Cost

11

0.0222

13

Quantity

0.3214

0.0299

9

Delivery

0.0932

0.0229

0.2382
0.2421

0.0225

12

Cost

Human hair
bricks

2.

0.2870

Quality

0.1984

0.0185

15

Quality

Sugarcane
bassage ash
bricks

4.

0.0999

0.2727

0.1008

4

0.2549

0.0942

6

Delivery

0.3695

0.2182

2
3
5

Quantity

0.4576

1

0.1226
0.1122

0.2215

0.0819

8

Cost

Fly ash bricks

0.1230

0.2680
0.2451

Quality

3.

0.2687

Quantity
Delivery
Cost

0.2509

0.0927

7

TOTAL

viii.

1.0000

Bricks Manufacturers Overall Ranking
Table No. 14: Summarizes of priority weights of each alternative of Bricks selection

Bricks Selection Criteria

Global
weights

Local
weights

Global
weights

Local
weights

Global
weights

Local
weights

Global
weights

Brick Manufacturer
4
Local
Global
weights
weights

Brick Manufacturer 1



Quality
Clay
bricks

0.0243

0.1641

0.0040

0.3944

0.0096

0.4476

0.0109

0.2470

0.0060

Quantity

0.0190

0.2367

0.0045

0.2389

0.0045

0.1565

0.0030

0.2887

0.0055

Delivery

0.0136

0.1721

0.0023

0.1972

0.0027

0.1000

0.0014

0.1756

0.0024

Cost

0.1694

0.0039

0.2673

0.0061

0.2887

0.0066

0.0055

0.1614

0.0036

0.2200

0.0049

0.3000

0.0067

Quantity

0.0299

0.2964

0.0089

0.3035

0.0091

0.3400

0.0102

0.3000

0.0090

Delivery

0.0225

0.2464

0.0056

0.2480

0.0056

0.2339

0.0053

0.1964

0.0044

0.0185

0.2107

0.0039

0.2872

0.0053

0.2000

0.0037

0.1000

0.0018

0.1230

0.2395

0.0294

0.2470

0.0304

0.2964

0.0365

0.2875

0.0354

Quantity

0.1226

0.2950

0.0362

0.2887

0.0354

0.2464

0.0302

0.2375

0.0291

Delivery

0.1122

0.2538

0.0285

0.2887

0.0324

0.2000

0.0224

0.2375

0.0266

Cost
Sugarca
ne
bassage
ash
bricks

0.0098

0.2464

Quality
Fly ash
(FAL G) bricks

0.4271

0.0222

Cost

Human
hair
bricks

0.0229

Quality

0.0999

0.2117

0.0211

0.1756

0.0175

0.2464

0.0246

0.2375

0.0237

Quality

0.1008

0.2964

0.0299

0.2950

0.0297

0.2396

0.0241

0.2417

0.0244

Quantity

0.0942

0.2464

0.0232

0.2000

0.0188

0.4063

0.0383

0.1917

0.0180

Delivery

0.0819

0.2107

0.0172

0.2395

0.0196

0.1771

0.0145

0.2417

0.0198

Cost

0.0927

0.2464

0.0228

0.2538

0.0235

0.1771

0.0164

0.3250

0.0301

Total scores
Rank

1.0000

0.2528

0.2516

1st

3rd

123

0.2524
2nd

0.2495
4th


CONCLUSIONS
From this research work, following conclusion are drawn
The main contribution of the work was the identification of the important criteria for the bricks
selection (above case study). Then a multi-criteria decision model for evaluating and selecting a
bricks manufacturer was developed. The model for bricks manufacturer evaluation and selection
was developed using the AHP method. The AHP model is assessing decision-makers to identify
and evaluate the bricks manufacturer selection.
Finally, the developed model is tested on four bricks manufacturer selection problems. The
results show the models are able to assist decision-makers to examine the strengths and
weaknesses of bricks manufacturer selection by comparing them with appropriate main criteria
and sub-criteria.
The developed model has not been implemented yet. It is just tested on four bricks manufacturer
selection problems as mentioned, but the outcome implies that the quality of fly ash (FAL-G)
bricks criterion has the majority weight among other criteria.
A case study of bricks selection based on AHP approach can be applied to four types of selected
bricks which are made of industrial waste such as Fly ash (FAL-G) bricks, Sugarcane Bassage
ash bricks, Human hair bricks, Clay bricks.
Present Approach of bricks selection in construction projects has certain shortcomings and it is
required to improve by application of scientific technique. Present approach does not consider
multiple objectives, Present approach does not collect sufficient data to evaluate bricks selection.
Therefore, Analytical Hierarchy Process (AHP) was suggested and applied due to its
applicability to the shortcomings.
According to the Analytical Hierarchy Process (AHP), development of the Criteria Framework
in Indian context is prepared for a case study of bricks selection. Total 4 nos. of sub-criteria’s
are identified for each type of bricks typically which are Quality, Quantity, Delivery and Cost
which affect the bricks selection problem. Main Criteria for bricks selection are: Fly ash (FALG) bricks, Sugarcane bassage ash bricks, Human hair bricks, Clay bricks.
For above mentioned case study of brick selection, 4 different bricks manufacturers were
evaluated through AHP based approach. There is found that Bricks Manufacturer No. 1 is best,
Customer can be placed order for fly ash bricks because of top three criteria of fly ash bricks are
Quality, Quantity, Delivery which weights are highest in descending order and affects the bricks
selection and cost of fly ash (FAL-G) bricks is on 5th rank therefore there can be an
improvement in the decision for fly ash bricks selection for profit maximization and cost
optimization.
By using Analytic Hierarchy Process (AHP) complete ranking with scores can be applied on
selected criteria.
With the help of Analytic Hierarchy Process (AHP) further research work can be carried out on
Ready Mixed Concrete selection as per case study.
The proposed methodology can also be applied to any other selection problem involving
multiple and conflicting criteria.
ACKNOWLEDGEMENT
The Authors thankfully acknowledge to Dr. C. L. Patel, Chairman, Charutar Vidya Mandal,
and Er. V. M. Patel, Hon. Jt. Secretary, Charutar Vidya Mandal, Dr. F. S. Umrigar, Principal, B.V.M.
Engineering College, Prof. J. J. Bhavsar, Associate professor and coordinator PG (Construction
Engineering & Management), Civil Engineering Department, B.V.M Engineering College, Er.
Yatinbhai Desai, Jay Maharaj Construction, Vallabh Vidyanagar, Gujarat, India for their motivations
and infrastructural support to carry out this research.
124

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125


AUTHOR’S BIOGRAPHY

Ashish Harendrabhai Makwana was born in 1988 in Jamnagar District,
Gujarat. He received his Bachelor of Engineering degree in Civil Engineering
from the Charotar Institute of Science and technology in Changa, Gujarat
Technological University in 2012. At present he is Final year student of Master`s
Degree in Construction Engineering and Management from Birla Vishwakarma
Mahavidyalaya, Gujarat Technological University. He has papers published in
international journals.

Prof. Jayeshkumar R. Pitroda was born in 1977 in Vadodara City. He
received his Bachelor of Engineering degree in Civil Engineering from the Birla
Vishvakarma Mahavidyalaya, Sardar Patel University in 2000. In 2009 he
received his Master's Degree in Construction Engineering and Management from
Birla Vishvakarma Mahavidyalaya, Sardar Patel University. He joined Birla
Vishvakarma Mahavidyalaya Engineering College as a faculty where he is
Assistant Professor of Civil Engineering Department with a total experience of
12 years in the field of Research, Designing and education. He is guiding M.E.
(Construction Engineering & Management) Thesis work in the field of Civil/ Construction
Engineering. He has published papers in National Conferences and International Journals.

126

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