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Devikamalam , et al., International Journal of Advanced Engineering Technology
Int J Adv Engg Tech/IV/III/July-Sept.,2013/113
RESOURCE SCHEDULING OF CONSTRUCTION PROJECTS
Address for Correspondence1PG student,
2Assistant Professor, Division of Structural Engineering, Anna University, Chennai, India
ABSTRACT Resource allocation and leveling are among the top challenges in project management, due to the complexity of projects. The
main objective of this project is to optimize the schedule of construction project activities in order to minimize the total cost
with resource constraints using Genetic Algorithm (GA optimization technique).
approach to Resource Constraint Project Scheduling Problems (RCPSP) in construction industry. The GA procedure
searches for an optimum set of tasks and priorities that produce shorter project duration and better
using GeneHunter software. Major advantage of the procedure is its simple applicability within commercial project
management software systems to improve the
KEYWORDS: Genetic Algorithm, Resource Scheduling, Resource constraint Scheduling, Construction management
1.0 INTRODUCTION
Resource management is one of the most important
aspects of construction project management in today’s
economy because the construction industry is
resource-intensive and the costs of
resources have steadily risen over the last several
decades.
Every project schedule has its own precedence
constraints, which means that each activity can be
processed when all its predecessors are finished. In
general, the purpose of project scheduler is to
minimize the completion time or make span, subject
to precedence constraints.
This paper brings out the drawback in existing
scheduling software and the method of using GA for
resource constraint scheduling by means of a case
study. Traditional scheduling methods use scheduling
rules (heuristics) specific to the project model or
constraint formulation, this method uses a direct
representation of schedules and a search algorithm
that operates with no knowledge of the problem space.
The representation enforces precedence constraints,
and the objective function measures both resource
constraint violations and overall performance.
advantages of using GA based optimization technique
for resource based scheduling has been proved by
several researchers (Weng-Tat Chan,
Cengiz 2002, Ahmed 2008).
Compared with traditional heuristic and mathematical
models, the GA scheduler has several advantages as it
can consider the objectives of time/cost trade
resource-constrained allocation, and resource levelling
simultaneously; it is difficult for traditional models to
have such a function. It has more flexibility to solve
scheduling problems of different types, because no
heuristic rules are necessary.
1.2 Genetic Algorithm
Genetic Algorithms (GA) are inspired by
theory about evolution. The GA is a global search
procedure that searches from one population of
solutions to another, focusing on the area of the best
solution. It models with a set of solutions (represented
by chromosomes) called initial population,
computation is performed through the creation of an
initial population of individuals and modifying the
characteristics of a population of solutions
(individuals) over a large number of generations
followed by the evolution, a satisfactory solution is
found. This process is designed to produce successive
populations that mean the solutions from one
population are taken and used to form a new
International Journal of Advanced Engineering Technology E
113-119
Research
RESOURCE SCHEDULING OF CONSTRUCTION PROJECTS
USING GENETIC ALGORITHM Devikamalam. J
1, Jane Helena. H
2
Address for Correspondence Assistant Professor, Division of Structural Engineering, Anna University, Chennai, India
Resource allocation and leveling are among the top challenges in project management, due to the complexity of projects. The
optimize the schedule of construction project activities in order to minimize the total cost
th resource constraints using Genetic Algorithm (GA optimization technique). This work describes a genetic algorithm
approach to Resource Constraint Project Scheduling Problems (RCPSP) in construction industry. The GA procedure
of tasks and priorities that produce shorter project duration and better-leveled
using GeneHunter software. Major advantage of the procedure is its simple applicability within commercial project
management software systems to improve the performance.
Genetic Algorithm, Resource Scheduling, Resource constraint Scheduling, Construction management
Resource management is one of the most important
aspects of construction project management in today’s
economy because the construction industry is
intensive and the costs of construction
resources have steadily risen over the last several
Every project schedule has its own precedence
constraints, which means that each activity can be
processed when all its predecessors are finished. In
scheduler is to
minimize the completion time or make span, subject
This paper brings out the drawback in existing
scheduling software and the method of using GA for
resource constraint scheduling by means of a case
onal scheduling methods use scheduling
rules (heuristics) specific to the project model or
constraint formulation, this method uses a direct
representation of schedules and a search algorithm
that operates with no knowledge of the problem space.
entation enforces precedence constraints,
and the objective function measures both resource
constraint violations and overall performance. The
advantages of using GA based optimization technique
for resource based scheduling has been proved by
Tat Chan, et al., 1996,
Compared with traditional heuristic and mathematical
models, the GA scheduler has several advantages as it
can consider the objectives of time/cost trade-off,
and resource levelling
simultaneously; it is difficult for traditional models to
have such a function. It has more flexibility to solve
scheduling problems of different types, because no
are inspired by Darwin's
theory about evolution. The GA is a global search
procedure that searches from one population of
solutions to another, focusing on the area of the best
solution. It models with a set of solutions (represented
) called initial population,
computation is performed through the creation of an
initial population of individuals and modifying the
characteristics of a population of solutions
(individuals) over a large number of generations
satisfactory solution is
found. This process is designed to produce successive
populations that mean the solutions from one
population are taken and used to form a new
population. This is motivated by a hope, that the new
population will be better than the old one and so on
through generations.
1.2.1 Fitness Function
The fitness function is the function to be optimized.
For standard optimization algorithms, this is known as
the objective function.
1.2.2 Individuals
An individual is any point to which the
function can be applied. The value of the fitness of an
individual is its score; an individual is a single
solution. A chromosome is a set of parameters which
define a proposed solution to the problem that the
genetic algorithm is trying to solve. The chromosome
is often represented as a simple string.
1.2.3 Populations and Generations
A population is an array of individuals. At each
iteration, the genetic algorithm performs a series of
computations on the current population called parents
to produce a new population called children. Each
successive population is called a new generation.
Typically, the algorithm is more likely to select
parents that have better fitness values.
1.2.4 Encoding
A chromosome is subdivided into genes. A gene is the
GA representation of a single factor for a control
factor. The process of representing the solution in the
form of a string that conveys the necessary
information is called Encoding. Each gene controls a
particular characteristic of the individual; similarly,
each bit in the string represents a characteristic of the
solution. Encoding Methods are Binary Encoding,
Permutation Encoding (Real number encoding), and
Value Encoding.
For eg, the GA chromosome is represented as follows
2.0 BASIC OUTLINE OF GENETIC
Figure 1 shows the various steps involved in Genetic
Algorithm process.
Fig. 1 Flowchart showing
the Genetic Algorithm
E-ISSN 0976-3945
search Paper
RESOURCE SCHEDULING OF CONSTRUCTION PROJECTS
Assistant Professor, Division of Structural Engineering, Anna University, Chennai, India
Resource allocation and leveling are among the top challenges in project management, due to the complexity of projects. The
optimize the schedule of construction project activities in order to minimize the total cost
This work describes a genetic algorithm
approach to Resource Constraint Project Scheduling Problems (RCPSP) in construction industry. The GA procedure
leveled resource profiles
using GeneHunter software. Major advantage of the procedure is its simple applicability within commercial project
Genetic Algorithm, Resource Scheduling, Resource constraint Scheduling, Construction management
population. This is motivated by a hope, that the new
old one and so on
The fitness function is the function to be optimized.
For standard optimization algorithms, this is known as
An individual is any point to which the fitness
function can be applied. The value of the fitness of an
individual is its score; an individual is a single
solution. A chromosome is a set of parameters which
define a proposed solution to the problem that the
The chromosome
is often represented as a simple string.
A population is an array of individuals. At each
iteration, the genetic algorithm performs a series of
computations on the current population called parents
e a new population called children. Each
successive population is called a new generation.
Typically, the algorithm is more likely to select
parents that have better fitness values.
A chromosome is subdivided into genes. A gene is the
resentation of a single factor for a control
factor. The process of representing the solution in the
form of a string that conveys the necessary
information is called Encoding. Each gene controls a
particular characteristic of the individual; similarly,
ch bit in the string represents a characteristic of the
Binary Encoding,
Permutation Encoding (Real number encoding), and
For eg, the GA chromosome is represented as follows
BASIC OUTLINE OF GENETIC ALGORITHMS Figure 1 shows the various steps involved in Genetic
Fig. 1 Flowchart showing
the Genetic Algorithm
Process
Devikamalam , et al., International Journal of Advanced Engineering Technology E-ISSN 0976-3945
Int J Adv Engg Tech/IV/III/July-Sept.,2013/113-119
2.1 Importance of GA
Genetic algorithm technique provides best solutions in
comparison with the other classic approach. Because
traditional techniques like Microsoft project and
Primavera scheduling software do not consider
resource constraints, actual cost and duration may
vary from the estimated one. An effective GA
representation and meaningful fitness evaluation are
the keys to the success in GA applications.
The method of solving resource constraint problem
using the software GeneHunter which uses GA
optimization technique is presented in this paper.
GeneHunter is a powerful software solution for
optimization problems which utilizes a state-of-the-art
genetic algorithm methodology. GeneHunter includes
an Excel Add-In which allows the user to run an
optimization problem from Microsoft Excel, as well as
a Dynamic Link Library of genetic algorithm
functions that may be called from programming
languages such as Microsoft Visual Basic or C.
3.0 PROBLEM FORMULATION
In order to solve a project scheduling problem
involving resource constraint, activities involved in a
real-time construction project has been considered.
The resource constraint in the form of number of
skilled and unskilled labourers available per day is
taken.
3.1.2 GeneHunter's Excel Interface
Creating a problem solving model in GeneHunter
requires that the relevant data is entered into a
Microsoft Excel spreadsheet and specify problem
solving parameters.
GeneHunter actually solves the problem by allowing
the less fit individuals in the population to die, and
selectively breeding the fit individuals. The process is
called selection, as in selection of the fittest. Two
individuals are taken and mated (crossover), the
offspring of the mated pair will receive some of the
characteristics of the mother and some of the father.In
nature, offspring often have some slight abnormalities,
called mutations. Usually, these mutations are
disabling and inhibit the ability of the offspring to
survive, but once in a while, they improve the fitness
of the individual. GeneHunter occasionally causes
mutations to occur. As GeneHunter mates fit
individuals and mutates some, the population
undergoes a generation change.
The population will then consist of offspring plus a
few of the older individuals which GeneHunter allows
to survive to the next generation. These are the most
fit in the population, and we will want to keep them
breeding. These most fit individuals are called elite
individuals. After dozens or even hundreds of
"generations," a population eventually emerges
wherein the individuals will solve the problem very
well. In fact, the fit individual will be an optimum or
close to optimum solution.
3.1.3 GeneHunter Dialog Screen
Fig.2 GeneHunter dialog Screen
The GeneHunter Dialog screen shown in Figure 2 to
identify the cells in the spreadsheet involved in
solving the problem. The list of constraints that should
be met by the solution can also be listed.
3.1.4 Fitness Function Cell
The Fitness Function box tells GeneHunter the
location of the cell which contains the formula that
measures GeneHunter's success in finding a solution
to the problem. The formula may be created using any
of the Excel functions that are available from the
Insert menu, such as average, sum, percentage, etc.,
Use of Excel macros or Visual Basic functions to
create a formula that allows solving very complex
problems. A neural net may even be used to model the
process if an appropriate mathematical formula is not
available.
3.1.5 Chromosomes
Chromosomes are the variables whose values are
adjusted in order to solve the problem. Their value is
related in some way to the fitness function.
GeneHunter uses two types of chromosomes to solve
the problems.
Continuous Chromosomes are used when the
adjustable cell can take on a value that may be within
a continuous range, such as the value 1.5 with the
range 0 to 2. Continuous chromosomes may also be
integers if the search space is to be restricted.
Enumerated Chromosomes are used when the
problem involves finding an optimal combination of
tasks, resources, duties, etc.
3.1.6 Constraints
The constraint portion of the GeneHunter dialog box
allows doing the following:
Limit the range of values that GeneHunter will search
for a solution, thus limiting the time taken to find an
optimal solution. This is called hard constraint.
Add restrictions or sub-goals to the original fitness
function. This is called a soft constraint. Solutions are
attempted to be found that meet the soft constraints, as
well as optimize the fitness function.
4.0 CASE STUDY
A real time project named “Aqualily” at Mahindra
world city with resources is chosen. Details of the
project are shown in Appendix A.
4.1 Objective function: The objective function is to
find the best schedule that gives minimum total
project duration (T),
Minimize (T)
where,
T depends on start date (Si) of activity and its duration
(Di), i.e,
T=Maximum(Si+Di) subjected to resource constraint
4.1.1 Resource limit:
The following table shows the range of resource
limits. Requirements of resources per day and duration
in shown in Appendix B. This is input in GeneHunter,
Table 1 Resource Limit
Devikamalam , et al., International Journal of Advanced Engineering Technology
Int J Adv Engg Tech/IV/III/July-Sept.,2013/113
5.0 RESULTS
5.1 Microsoft Project Solution:
Implementation of this problem in MSP with
unlimited resources gives total project duration as
430days. Resource Profile for a resource is shown in
the Figure 3,
Fig: 3 Resource Profile
After carrying out levelling operation, the total
duration of the project is 504 days.
5.2 Genehunter solution
Problem is executed by using many generations. The
converging result obtained was T = 760 days by
applying resource constraints.
Best fitness is obtained in 200 generations.
Fig. 4 GeneHunter solution
5.3 Comparison between two solutions
Intended goal of achieving the best schedule with
resource constraints gives optimized duration of the
project is T = 760 days.
Solution obtained from MSP is practically not
possible for tracking process and for execution. Hence
optimized duration is 760 days. Table: 2 Comparison of Results
Table 3 shows the Resource Limit and Actual usage
International Journal of Advanced Engineering Technology E
113-119
problem in MSP with
unlimited resources gives total project duration as
430days. Resource Profile for a resource is shown in
After carrying out levelling operation, the total
Problem is executed by using many generations. The
converging result obtained was T = 760 days by
Best fitness is obtained in 200 generations.
Fig. 4 GeneHunter solution
between two solutions
Intended goal of achieving the best schedule with
resource constraints gives optimized duration of the
Solution obtained from MSP is practically not
possible for tracking process and for execution. Hence
of Results
Table 3 shows the Resource Limit and Actual usage
Table: 3 Comparison Actual usage of Resources and
Cost
Input can be real values or variables in GenrHunter
whereas it is only variables in MSP. Both GeneHunter
and MSP undertake time and Predecessor constraint,
but resource constraint can be incorporated in
GeneHunter alone.
6.0 CONCLUSION
An implementation of the GA developed model for
resource-constrained project scheduling has resulted
in optimized output with reduced cost. A real time
project solved using this optimization software shows
that best converging result can be obtained.
REFERENCES 1. Ahmed B. Senouci and Neil N. Eldin
Genetic Algorithms in Resource Scheduling of
Construction Projects", Journal of Construction Engineering and Management , Vol. 130, No. 6, pp. 869
877.
2. Cengiz Toklu Y.C (2002), "Application of GA to Construction Scheduling with or without Resource
Constraints", Canadian Journal of Civil Engineering,
Vol.29, No. 3, pp 421-429. 3. Jin-Lee Kim and Ralph D. Ellis Jr.
“Permutation-Based Elitist Genetic Algorithm for
Optimization of Large-Sized ResourceProject Scheduling”, Journal of Construction
Engineering and Management, Vol. 134, No.11, pp. 904
913. 4. Sou-Sen Leu, An-Ting Chen, and
(1999), “Fuzzy Optimal Model for Resource
Constrained Construction Scheduling”, Computing in Civil Engineering, Vol. 13, No. 3, pp.
207-216.
5. Tarek Hegazy and Moustafa KassabOptimization Using Combined Simulation and Genetic
Algorithms”, Journal of Construction Engineering and
Management, Vol. 129, No. 6, pp. 6986. Tarek Hegazy, (1999), "Optimization of Resource
Allocation and Leveling Using Genetic Algorithms", Journal of Construction Engineering and Management
Vol. 125, No. 3, pp. 167-175.
7. Weng-Tat Chan, David K. H. ChuaKannan (1996), "Construction Resource Scheduling wi
Genetic Algorithms", Journal of Construction
Engineering and Management, Vol. 122, No. 2, pp. 125132.
E-ISSN 0976-3945
Table: 3 Comparison Actual usage of Resources and
Input can be real values or variables in GenrHunter
SP. Both GeneHunter
and MSP undertake time and Predecessor constraint,
resource constraint can be incorporated in
An implementation of the GA developed model for
constrained project scheduling has resulted
optimized output with reduced cost. A real time
project solved using this optimization software shows
that best converging result can be obtained.
Neil N. Eldin, (2008), " Use of
Genetic Algorithms in Resource Scheduling of
Journal of Construction , Vol. 130, No. 6, pp. 869-
Toklu Y.C (2002), "Application of GA to Construction Scheduling with or without Resource
Constraints", Canadian Journal of Civil Engineering,
Lee Kim and Ralph D. Ellis Jr. (2008),
Based Elitist Genetic Algorithm for
Sized Resource-Constrained Journal of Construction
, Vol. 134, No.11, pp. 904-
and Chung-Huei Yang
Fuzzy Optimal Model for Resource-
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Moustafa Kassab (2003), “Resource Optimization Using Combined Simulation and Genetic
Journal of Construction Engineering and
Vol. 129, No. 6, pp. 698-705. Optimization of Resource
Allocation and Leveling Using Genetic Algorithms", Journal of Construction Engineering and Management,
David K. H. Chua, and Govindan (1996), "Construction Resource Scheduling with
Journal of Construction
, Vol. 122, No. 2, pp. 125-
Devikamalam , et al., International Journal of Advanced Engineering Technology
Int J Adv Engg Tech/IV/III/July-Sept.,2013/113
A. PROJECT DETAILS:
S.No Description
1 Name of the
2 Location of the project
3 Client
4 Consultant
5 Architect
6 PMC
7 Quantity surveyor
8 Contract value in Crores
9 Type of Contract
10 Type of structure
11 Type of Building
12 Building Configuration
13
Scope of work
14 Duration of the project
15 Contractual start
16 Contractual finish
(Milestones if any)
17 Working hours
18 Mobilisation advance
19 CAR policy
20 Defect liability period
21 Penalty / Liquidated Damages
22 Material supply by client
23 Water & power
24 Retention &
retention
25 Taxes
A. LAYOUT OF THE PROJECT :
International Journal of Advanced Engineering Technology E
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APPENDIX
Description Particular
Name of the project "AQUALILY "Mahindra World
City
Location of the project Natham Post, Chengalpet, Tamilnadu.
Mahindra Residential Developer
Limited.
M/s. Ecalibre Engineering Consultant
Pvt Ltd.
M/s. Edifice Consultants Pvt Ltd.
M/s. CB Richard Ellis.
Quantity surveyor M/s. KPK Quantity Surveyor.
Contract value in Crores Rs, 44.01
Type of Contract Item rate contract
Type of structure Structural and Identified Finishes works
Type of Building Residential Building
Configuration Block D1-Block D8
Scope of work Structural and Identified Finishes works
Basement + Ground Floor + 7 Floors
Number of Blocks - 8
Number of units - 178
Duration of the project 18 Months
Contractual start Immediately after award of LOI
Contractual finish
(Milestones if any)
Nil
Working hours 24hours
Mobilisation advance 10% of contract value at the time of acceptance of work order against B.G
CAR policy Car Policy will be obtained by
Contractor
Defect liability period The defects liability period will be 12 Months from the date of issue of virtual
completion
Penalty / Liquidated Damages Contractor shall be liable for liquidated damages of 1% per week of delay and
Max 5% on the final Contract Value
Material supply by client Cement ,Reinforcement steel & Structural steel
Water & power Supplied but chargeable
Release of 5% will be deducted from the running
bill.
The contract value is inclusive of all taxes.
LAYOUT OF THE PROJECT :
Fig : 5 Layout of “Aqualily” Project
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Int J Adv Engg Tech/IV/III/July-Sept.,2013/113-119
B. RESOURCE REQUIREMENT PER DAY
Activities Duration
Days
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19
/d /d /d /d /d /d /d /d /d /d LS LS LS LS /d /d /d /d /d
Letter of Indent 1 1 1
Handing Over 15 1 1
Initial
Mobilization 1 1 1
Receipt of
Setting out Drg 5 1 1
Setting out and Surveying 1 5 1
Basement
Works 150
Dewatering Works 1 1 12 1 1 2 4 1 1 2 5 4
Zone A&B (Basement)
Sub Structure works
Excavation 40 1 2 4 1 1 1 2 5 4
Sandfilling 41 1 1 2 4 1500 1 1 2 5 4
P.C.C 42 1 2 30 1200 10000 1 1 2 5 4
Waterproofing 40 1 4 12 2 2 950 20000 1 2 5 4
Raft Slab 40 1 10 60 8 8 2 2500 200000 18000 1 1 2 5 4
Retaining wall
& Column works 40 1 10 60 8 8 2 3200 480000 24000 1 1 2 5 4
Backfilling 45 1 40 1 1 2 5 4
Basement slab 1 10 30 8 8 2 3500 200000 25000 1 1 2 5 4
Zone C&D (Basement)
Sub Structure works
Excavation 40 1 2 4 1 0 1 1 2 5 4
Sandfilling 41 1 1 2 4 1500 1 1 2 5 4
P.C.C 42 1 2 30 1200 10000 1 1 2 5 4
Waterproofing
works 40 1 4 12 2 2 950 20000 1 1 2 5 4
Raft Slab 40 1 10 60 8 8 2 2500 200000 18000 1 1 2 5 4
Retaining wall
& Column
works 40 1 10 60 8 8 2 3200 480000 24000 1 1 2 5 4
Backfilling 45 1 40 1 1 2 5 4
Basement slab 1 10 30 8 8 2 3500 280000 25000 1 1 2 5 4
Basement Finishing Works
Zone A& B
Brick Work 100 1 16 20 3800 6750 20000 1 1 2 5 4
Plastering 80 1 18 20 2 4200 23000 1 1 2 5 4
Painting 1 20 20 8 1 1 2 5 4
Zone C&D
Brick Work 100 1 16 20 3800 6750 20000 1 1 2 5 4
Plastering 80 1 18 20 2 4200 23000 1 1 2 5 4
Painting
1 20 20 8 1 1 2 5 4
Int J Adv Engg Tech/IV/III/July-Sept.,2013/113-119
Super Structure
D4 Block
Structure
G.F Slab 16 1 8 32 8 8 2 2 4500 360000 25000 1 1 2 5 4
F.F slab 16 1 8 32 8 8 2 2 4500 360000 25000 1 1 2 5 4
Second Floor
Slab 16 1 8 32 8 8 2 2 4500 360000 25000 1 1 2 5 4
Third Floor Slab 16 1 8 32 8 8 2 2 4500 360000 25000 1 1 2 5 4
Fourth Floor slab 16 1 8 32 8 8 2 2 4500 360000 25000 1 1 2 5 4
Fifth Floor Slab 16 1 8 32 8 8 2 2 4500 360000 25000 1 1 2 5 4
Terrace Slab 12 1 8 32 8 8 2 2 4500 360000 25000 1 1 2 5 4
LMR 1 15 40 10 8 2 2 4800 384000 26000 1 1 2 5 4
Finishing Works
Ground Floor 45 1 12 30 3 2 3 2 5 3800 360 30000 1 1 2 5 4
First Floor 45 1 12 30 3 2 3 2 5 3800 360 30000 1 1 2 5 4
Second Floor
Slab 45 1 12 30 3 2 3 2 5 3800 360 30000 1 1 2 5 4
Third Floor 45 1 12 30 3 2 3 2 5 3800 360 30000 1 1 2 5 4
Fourth Floor 45 1 12 30 3 2 3 2 5 3800 360 30000 1 1 2 5 4
Fifth Floor 45 1 12 30 3 2 3 2 5 3800 360 30000 1 1 2 5 4
Sixth Floor 45 1 12 30 3 2 3 2 5 3800 360 30000 1 1 2 5 4
Header Room 55 1 15 30 3 2 3 2 5 3800 360 30000 1 1 2 5 4
Structural
Works 76 1 15 35 8 5 3200 1 1 2 5 4
External
plastering 60 1 20 35 3500 35000 1 1 2 5 4
External painting 1 22 35 8 1 1 2 5 4
D2,D1,D3,D6&D7,D5
Structure
G.F Slab 25 1 48 192 48 6 18 12 27000 2160000 150000 1 6 12 30 24
F.F slab 25 1 48 192 48 6 18 12 27000 2160000 150000 1 6 12 30 24
Second Floor Slab 25 1 48 192 48 6 18 12 27000 2160000 150000 1 6 12 30 24
Third Floor Slab 25 1 48 192 48 6 18 12 27000 2160000 150000 1 6 12 30 24
Fourth Floor
slab 25 1 48 192 48 6 18 12 27000 2160000 150000 1 6 12 30 24
Fifth Floor Slab 25 1 48 192 48 6 18 12 27000 2160000 150000 1 6 12 30 24
Terrace Slab 25 1 48 192 48 6 18 12 27000 2160000 150000 1 6 12 30 24
LMR 1 90 240 48 6 18 12 27000 2160000 150000 1 6 12 30 24
Finishing Works
Ground Floor 60 1 72 180 18 12 12 30 22800 2160 180000 1 6 12 30 24
First Floor 60 1 72 180 18 12 12 30 22800 2160 180000 1 6 12 30 24
Second Floor 60 1 72 180 18 12 12 30 22800 2160 180000 1 6 12 30 24
Third Floor 60 1 72 180 18 12 12 30 22800 2160 180000 1 6 12 30 24
Fourth Floor 60 1 72 180 18 12 12 30 22800 2160 180000 1 6 12 30
24
Fifth Floor 60 1 72 180 18 12 12 30 22800 2160 180000 1 6 12 30 24
Sixth Floor 20 1 72 180 18 12 12 30 22800 2160 180000 1 6 12 30 24
Header Room 75 1 90 180 18 12 12 30 22800 2160 180000 1 6 12 30 24
Structural Steel Works 90 1 90 210 48 6 19200 87500 1 6 12 30 24
External 85 1 120 210 12 12 21000 210000 1 6 12 30 24
Int J Adv Engg Tech/IV/III/July-Sept.,2013/113-119
plastering
External
painting 218 1 132 210 48 1 6 12 30 24
D8 Block
Structure
G.F Slab 18 1 4 25 8 1 3 2 4500 360000 25000 1 1 2 4 4
F.F slab 18 1 4 25 8 1 3 2 4500 360000 25000 1 1 2 4 4
Second Floor
Slab 18 1 4 25 8 1 3 2 4500 360000 25000 1 1 2 4 4
Third Floor 18 1 4 25 8 1 3 2 4500 360000 25000 1 1 2 4 4
Fourth Floor
slab 18 1 4 25 8 1 3 2 4500 360000 25000 1 1 2 4 4
Fifth Floor Slab 18 1 4 25 8 1 3 2 4500 360000 25000 1 1 2 4 4
Terrace Slab 15 1 4 25 8 1 3 2 4500 360000 25000 1 1 2 4 4
LMR 1 4 25 8 1 3 2 4800 360000 26000 1 1 2 4 4
Finishing Works
Ground Floor 45 1 15 30 3 2 2 5 3800 360 30000 1 1 2 4 4
First Floor 45 1 15 30 3 2 2 5 3800 360 30000 1 1 2 4 4
Seecond Floor Slab 45 1 15 30 3 2 2 5 3800 360 30000 1 1 2 4 4
Third Floor 45 1 15 30 3 2 2 5 3800 360 30000 1 1 2 4 4
Fourth Floor 45 1 15 30 3 2 2 5 3800 360 30000 1 1 2 4 4
Fifth Floor 45 1 15 30 3 2 2 5 3800 360 30000 1 1 2 4 4
Sixth Floor 25 1 15 30 3 2 2 5 3800 360 30000 1 1 2 4 4
Header Room 45 1 15 30 3 2 2 5 3800 360 30000 1 1 2 4 4
Structural Steel
Works 65 1 10 40 8 1 3200 87500 1 1 2 4 4
External plastering 60 1 15 45 3500 35000 1 1 2 4 4
External
painting 60 1 20 30 8 1 1 2 4 4
Total 85 1969 5427 16 819 196 339 290 400 4 627100 36540 25079000 4082000 85 174 350 856 700