Re search Paper RESOURCE SCHEDULING OF … IV/IJAET VOL IV ISSUE III... · Int J Adv Engg...

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


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


of tasks and priorities that produce shorter project duration and better-leveled


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


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


leveled resource profiles


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



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


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-

Constrained Construction Scheduling”, Journal of , Vol. 13, No. 3, pp.

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-



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 :


113-119

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

E-ISSN 0976-3945


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


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


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

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