COST PREDICITION

DEPARTMENT OF CIVIL ENGINEERING, 2018 1
18ETCE001001
A
SEMINAR REPORT ON
COST PREDICTION
A COMPARITIVESTUDY ON DIFFERENT COST ESTIMATION TECHNIQUES IN CONSTRUCTION
PROJECTS
SEMINAR REPORT SUBMITTED BY
NAME: ABHIJNA DARSHINI K N
REGISTRATION NO: 18ETCE001001
NAME OF THE MENTOR: Dr. SREENIVAS PADALA
CIVIL ENGINEERING
FACULTYOF ENGINEERING& TECHNOLOGY
RAMAIAH UNIVERSITY OF APPLIED SCIENCES,
PEENYA CAMPUS
CLASS OF 2018

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FACULTYOF ENGINEERING& TECHNOLOGY
CERTIFICATE
This is to certify that the seminar titled “COST PREDICTION” is a bonafide work carried
out in the Department of Civil Engineering by Kumari ABHIJNA DARSHINI K N, bearing
register number 18ETCE001001 partial fulfilment of the requirementsfor the award of
B.Tech. degree in Civil Engineering of M S Ramaiah University of Applied Sciences.
MENTOR:
Dr. SREENIVAS PADALA
Assistant ProfessorofCE Dept.
Dr. NAYANA PATIL Dr. GOVIND R KADAMBI
Head of Department (CE) Pro Vice-chancellor& DEAN-FET

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DECLARATION
DECLARATION SHEET
STUDENT NAME: ABHIJNADARSHINIKN
REGISTRATION NO. 18ETCE001001
COURSE: B.Tech. BATCH: 2018
MODULE TITLE: SEMINAR
MODULE CODE: 19CEP406
MODULE DATE: TO:
MENTOR: Dr. SREENIVAS PADALA
DECLARATION:
The seminar report submitted here with is a result of my owninvestigation
and that I have conformedto the guideline against plagiarismas laidout in
the student handbook. All sectionof the text andresults, whichhave been
obtainedfromother source, are fully referenced. I understandthat cheating
and plagiarismconstitute a breachof university regulations andwill be dealt
with accordingly
SIGNATURE OF
STUDENT:
DATE:
SUBMISSION DATE
STAMP:
SIGNATURE OF THE
MODULE LEADER
AND DATE:
SIGNATURE OF
REVIEWER AND
DATE:

18ETCE001001
DECLARATION
COST PREDICTION
A COMPARITIVESTUDY ON DIFFERENT COST ESTIMATION TECHNIQUES IN CONSTRUCTION
PROJECTS
The internship report submitted in partial fulfilment of academic requirements for the
award of B.Tech. degree in the department of Civil Engineering and Technology of M S
Ramaiah UniversityApplied Sciences. The report here is a result of myown investigations and
that I have conformed to the guidelines against plagiarism as laid out in the student
handbook.All section of the text& result, which have beenobtained from other sources,are
fully referenced. I understand that cheating & plagiarism constitute a breach of university
regulation & will be dealt with accordingly and hence this seminar report has been passed
through plagiarism check and the report has beensubmitted to the supervisor.
NAME: REGISTRATION NO: SIGNATURE:
ABHIJNA DARSHINI K N 18ETCE001001

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ACKNOWLEDGEMENT
I give my deepestgratitude and appreciation to all those who gave me the possibility
to complete this report on my internship program as a part of the curriculum for the degree
of Bachelor of Technology in Civil engineering.
I would like to acknowledge with much appreciation the crucial role of the mentors and
guidance of Dr. SREENIVASPADALA & Mr. ALURI PURNA CHANDRA SATYANARAYAN (Project
Head, DBL) for their kind guidance during the research and analysis. Their consistent
support and advice have helpedus a lot to complete this seminar successfully.
My profound thanks go to our most respectedDEAN of The Faculty of Engineering and
Technology, Dr. RAJASHEKARA SWAMY for being such a support throughout the journey of
academics and for his valuable time and suggestion investedon us.
Also, many thanks to our belovedHead of the Civil Engineering Department, Dr. NAYANA
PATIL for providing such a required facilities and guidance.
Finally, my certain thanks to my family Mr. NARAYANA SHETTY, Mrs. MANGALA NARAYAN
SHETTY & Mrs. AMRUTHA VARSHINIR. ALVA for their love and thrust and for their unfailing
constant support me throughout my life.
DATE- Jan 2022
BY- ABHIJNA DARSHINI K N (18ETCE001001)

18ETCE001001
ABSTRACT
This seminar report is on a broad spectrum that contains chapters which reveals my
experience in preparing the seminar report. The content of all chapters is summarized and is
constructed from all the practical basis of my analysis.
In the opening chapter, I give details on the background of PWD and its functionality and
includes the intentions of the project undertakenfollowed by the company, DBL that has
taken the contract.
The second chapter is the most huntedchapter which explains my overall internship
familiarity in one month. The chapter reveals the record of the overall work that I have been
executing.It gives a highlight to the main works of the construction industry.
The following chapters lead to the Understandingof the benefitsof the internship on
differentaspects and areas. The internship has had plus in terms of improving skills and
differentabilities. The advantages and the gains of the internship are put in short.
The report ends with the conclusion and photo gallery as to what I have gained and
understoodfrom this internship programme.

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CONTENT
Certificate……………………………………………………………………….……………………………………………….….2
Declaration…………………………………………………………………………….…………………………………………...3
Acknowledgement……………………………………………………………………………………………………………….4
Abstract……………………………………………………………………………………………………………………………….6
Contents………………………………………………………………………………………………………………………………7
CHAPTER 1- Challenges facedin India Industry………….…………………………………………………………9
1.1 Challenges Faced in Construction
CHAPTER 2- Cost Estimation…………………………………….…………………………………………………………13
2.1 Cost and its importance
2.2 Project cost estimation techniques
2.3 Types of cost estimates in construction
2.4 Future of cost estimation
2.5 Estimating software for construction
CHAPTER 3- Cost Prediction………………….……………………………………………………………………………27
3.1 Definitions & objectives
3.2 Cost analysis approaching methodsin differentareas of construction
3.3 Methodsin construction cost estimates
3.4 Case Study- Cost prediction model for institutional buildings in Nigeria
CHAPTER 4- Conclusion…………………………………………….……………………………………………………….35
References…………………………………………………………………………………………………………………………37

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FIGURE CONTENTS
1.0 Inputs to the Residential Construction with the effectof Covid 19……………………………….9
1.1 Framework of Environmental Protection…………………………………………………………….………..10
1.2 Skilled Labour availability in various fields of construction……………………………………….…..12
2.1 Comparative Study from the projectto project……………………………………….……………………11
2.2 Probability Density Function of a Parametric Estimations…………….…………………….…………15
2.3 Structural Representationof Bottom-up Estimation.……………………….………………….……….18
2.4 Graphical Representationof Triangular Distribution…………………………………………………….20
2.5 Graphical Representationof BETA Distribution…………………………………………………………….22
3.1 Schematic Representation of Cost Analysis…………………………………………………………………..23
3.2 Approach Type Distribution………………………………………………………………………………………….26
3.3 Distribution of articles of approach………………………………………………………………………………26
3.4 Approach applications applied in DifferentAreas………………………………………………………...28
3.5 Feedformal neural network………………………………………………………………………………………….30

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Chapter 1
CHALLENGESIN INDIANINDUSTRIES
1.1 CHALLENGESFACED IN CONSTRUCTION
The constructionindustry occupies the 2nd most important place in the Indian
economic and industrialsector. Thecontribution of thesectorto the country’s GDPis
almost 9%, employingover 51 million people with the recent lockdown and several
restrictions imposeddue to theCOVID-19pandemic, theIndian construction industry has
been impacted to a great extent. Analysts andresearchfirms are predicting steady
growthin the year2021 and beyond, yet multiple challenges arehoveringin the
constructionsectorin India.
⚫ INFLUXIN MATERIAL COST
The cost of raw materials is on the rise. The main reasonbeing a
shortagein the supply of materials tothe sectordue to a disruptive supply chain. Besides,
to stabilizetheeconomy, reforms arebeing introduced by Centralas well as the state
governments. Additional on taxes isbeing introduced, spurring up thecost of the raw
materials. Theresult is high expenses leading to a high-value evaluation of thereal estate
constructions. Consequently, buyers areless attractedtoinvest in the construction
sector. Themitigationpath can be, adopting automated and modern technologies to
increase customer satisfaction.
Fig 1.0 Inputs tothe Residential Construction withthe effect ofCOVID-19

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⚫ SAFETY OF EMPLOYEES
Safety and securityarenow a growing concern worldwide. Employees and
the constructionsitesneedto be Covid free, preventing any spread of infections.
Adequate safetymeasures must be adopted within thebuilding premises. Deploying a
huge number of employees or construction workers maylead to unprecedented infection
spreads, harming theprogress of the projects andunnecessarydelays. On the flip side, a
lesser number of employees impacts the deadlines and timelines severely.
⚫ REDUCED INVESTMENT INTHE REAL STATE SECTOR
The impact of thepandemic has severelycrippled the Indian economy. There
have been severaljob losses and a reduction in wages. Consumers ofthe realestatesector
arehesitant to invest in construction projects. Thenumber of buyers has reduced
considerably. Besides the need for commercial buildings has lesseneddue to the “work from
home” policies enforced by most of thecompanies and firms. With thesituations in the
future being bleak and uncertain,thereis no clarityon how long the recessionwill continue.
⚫ ENVIRONMENT PRESERVATION
A mandatoryaspect of any construction project is to ensure the
preservationof thesurroundingenvironment. In India, it is tough to maintain soil erosion
and degradation. Thereasonbeing mainly floods, droughts, soil alkalinity, aridity, and
salinity. Besides airand water pollution levelsarestill not within controllable limits in the
country. Urbanizationin major cities has decreased thesoil quality, impacting the
environment. The builders and real estateowners areforcedto adopt innovative
measures andinvest more in reducing the negative effect on the environment.
Fig 1.1 Frameworkfor EnvironmentalProtection

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⚫ PROVISIONOF ELECTRCITY
With more and more real estatefirms coming up in India, the necessityfor
the continuous provision of electricity is increasing at a rapid pace. Property owners and
builders sometimes struggle in obtaining clearance faster from the authorities. They are
forced many a time to create captive power units within the apartment complexes. Such
provisions may turn out to beexpensive and involvement of skilled laborers. Besides, it
increases thecompletion timeof the projects.
⚫ EFFECT OF NATURAL DISASTERS
Naturaldisasters andhazards areunpredictableand uncertain in the
climaticconditions of India.Hencesiteselectionis an important factorin construction
projects. Even if sites arein proximity of rawmaterials thefact that they are locatedon a
flood prone or earthquakeprone areas, reduces thechances of being shortlistedfor
construction. Moreover, buildings need to beplanned, designed, and constructedin sucha
manner enabling tolerance of naturaldisasters.
JOURNAL TITLE FREQUENC
Y
PERCENTAGE
Journal Of Computing in Civil Engineering 7 9%
Journal Of StructuralEngineering 4 5%
Journal Of ConstructionEngineering and
management
3 4%
Journal Of EarthquakeEngineering and
Engineering Vibrations
2 2%
Sustainable TransportationSystems 3 4%
⚫ SKILLED MANPOWER
The availability of skilled manpower at different stages ofconstruction is
sometimes a concern. Especiallyduring pandemic situations, thecrisis rises leading to delay in the

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proposed deadline of the projects. Besides lackof training provided to the constructionworkers
further hampers the timeline and quality of the constructions. Focusedattentionis needed to speed.
Fig 1.2 Skilled Labour Availability In Various Fields Of Construction

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Chapter2
COST ESTIMATION
2.1 COST AND IT’S IMPORTANCE
Costs arethetotalfunds needed to monetarily cover and complete a
business transactionor work project. COST MANAGEMENT is theprocess that involves
planning, controlling, and otherwisehandling the budget of a business – cost management
helps the business predict unavoidable expenses with as much accuracyas possible. Project
management costs involve allcosts that cover the tasks relatedtoproject management, i.e.,
everything involving initiating, planning, executing, controlling, and finishing a specific
project.
Cost management is the process that involves planning, controlling, and otherwise handling
thebudget of a business – cost management helps the business predict unavoidable
expenses withas much accuracyas possible.
Project management costs involve allcosts that cover thetasks relatedtoproject management,
i.e., everything involving initiating, planning, executing, controlling, and finishing a specific project
finally, project cost management is a process that involves the estimation and allocation of project
budget and subsequent costs, as wellas project cost control.
• Direct costs –Direct costs arethosedirectly involved with, and necessaryto
complete saidproject.
1. Professionals working on the project –Company employees or outsourced
contractorsandfreelancers
2. Equipment – The tools and machines the employees, contractors, orfreelancers
use tofinish the project
3. Materials –Physical materials (that arenot tools or machines)needed to
finish theproject
4. Project management tasks –All tasks meant to facilitateproject completion
before agiven time, and according to specific requirements

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5. Engineering tasks –All research, designwork, and installationof equipment
made to finish the project
6. Transportation–Customrates, bringing thefinished product to retailers, etc.
• Indirect costs – Indirect costs for a project are costs which do not directly lead to
project completion but arestill vitalfor the company or individual working on said
project. As such,theyarea part of individual project costs.
1. Operating overhead expenses-Office rent, utilities, insurance, generaloffice
equipment,and materials
2. Target annualsalary-The clean profit the company or individual wants to
make, inaddition to the money needed to cover overhead andother
expenses.
WORK BREAKDOWNSTRUCTURE:
A work breakdown structure, orWBS, is a project management tool
that takes a step-by-stepapproachto complete largeprojects withseveral moving
pieces. By breaking down the project into smaller components, a WBS can integrate
scope, cost and deliverables into a singletool. a deliverable-oriented hierarchical
decompositionof the work to be executed by the project teamto accomplish the
project objectives and create therequired deliverables. It organizes anddefines the
totalscope of the project. Eachdescending level represents anincreasinglydetailed
definition of theproject work. The WBS is decomposed into work packages. The
deliverable orientation of the hierarchy includes both internaland external
deliverables.
2.2 PROJECTCOST ESTIMATIONTECHNIQUES
1. Analogous Estimating:
Through analogous estimating, a project manager calculates theexpected costs of
a project basedupon the known costs associatedwitha similarproject that was completed in the
past. This method of estimationrelies upon a combination of historicaldata and expert judgment

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of theproject manager. Analogous estimating is an estimationtechnique is also referredto as top-
down estimating. It involves leveraging the estimators’ experienceorhistoricaldata from previous
projects by adopting observed cost, durationor resourceneeds to a current project or portions of
a project.
Analogous estimating does not require data manipulation or statistical adjustments. Notwo
projects are thesame, analogous estimating does have its limitations. As such, it is often
leveragedin the earliest stages ofproject planning, when a rough estimatecansuffice. Analogous
estimating canalsobe usedwhen thereis relativelylittle information about the current project
available. This technique is useful if you need to produce estimates without having plenty of
information available. This may be the caseduring project selectionor initiation phases when
overseeing a bunch of projects at the portfolio level or in theearlystages ofa project. Estimations
can relateto a whole project or parts of a project, such as work packages oractivities.
Fig 2.1 Comparative StudyfromProjectsTo Projects
(source: PMI Practice Standard for Project Estimating)
Merits:
• Analogous estimating typically does not require a lot of resources or time.
• This type of estimating canbe performed withvery limited available data.
• It is thereforeideal in the project initiation phaseand for activities for which
not muchinformation and historicaldata are available.

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• Theseestimates canbeideal for high-level assessments andstrategic
considerations, astheaccuracyis often sufficient for working on the ‘big picture’.
It can then be used in programmanagement or for early stakeholder
communications, for instance.
Demerits:
• Estimates tendtobe rough, and theyare often not very accurate.
• The underlying assumptionis that historical data or experience of the estimators wouldbe
applicable to the current project.
• In practice, top-down estimates can sometimes be driven by political
considerations or even pressure rather than based on the project-specific
characteristics ortheexpertiseofthe subject matter experts.
• The high level and thepotential inaccuracyof analogous estimates put certain limitations
on their use for decision-making or project planning and controlling.
EXAMPLES
An IT vendor is askedby a prospective customer to estimatetheimplementation cost of off-
the-shelf software. Thevendor has done similar types of jobs a couple of times before and
storedthe key indicators of past projects in a dedicateddatabase. Thedatabaseshows the
following data for a long list of comparableprojects:
HISTORICAL PROJECT
DATA
COST (In Rs.1000crores) Duration(in days)
A 100 40
B 200 70
C 80 50
D 120 60
One point estimate: Someexpert judgment and concludes that the characteristicsofthe
current project like D
Range of estimate: Here, they consider the “C” as the outliner in terms of scope and cost
(narrower than the current scope)

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2. Parametric Estimating
In parametricestimating, historicaldata andstatisticalmodeling are
used to assigna dollar valueto certainproject costs. This approach determines the
underlying unit cost for a particular component of a project and then sales that unit cost as
appropriate. It is much more accurate thananalogous estimating but requires more initial
data to accuratelyassess costsis a common technique to estimatecosts indifferent levels of
granularity, theform of its implementation varies greatly.
Parametricestimating is oftenused in construction. Other examples might include
estimating thecost per unit to print and bind a book or to build anelectronic device. The
determination of an estimateis basedon a statistical(orassumed)correlationbetween a
parameter and a cost ortimevalue. This observed correlationis then scaledto the size of the
current project. Some projects build complex statisticalmodels andperform a
comprehensive regressionanalysisforvarious parameters.
They might alsodevelop algorithms and assigna significant number of resources for
deploying and (back)testing suchmodels. This is an approach applicable to large projects or
so-called ‘mega projects’ whereeven smallshortcomings in the accuracyof estimatescould
causea materialimpact. Smaller projects, on the other end of the range, canuseparametric
estimationby developing functions or simply applying the ‘ruleof three’ if there is evidence
or a reasonableassumptionthat observedparameters andvalues correlate. This may also
involve some expert judgment whether assumedregressions are reasonableandapplicable
to theproject or activity.
According toPMI’s Practice Standard, thereare 2 types of results:
• Deterministicestimates: Thedeterministicresult typeof theparametric
estimationis asinglenumber for cost or timeneeded, calculatedbased on
parametric scaling.
• Probabilistic estimates: Therangeofestimates basedon the probability of
different costandduration amounts.

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Fig 2.2 Probability DensityFunction of a Parametric Estimation
The optimistic and pessimisticcost andduration estimates canbe determined by
defining a target probability and/or a multiplier to theirstandarddeviations. Depending on
the form of theprobability density curve, these3 points can then be transformedinto a so-
called final estimate
Merits:
• The parametricestimationtechnique canbe very accuratewhenit comes to
estimating cost andtime.
• It is therefore easier toget stakeholders’support and approval of budgets
determinedthis way.
• Once the model is established, it can be reusedfor other similarproject and the
qualityof data becomes better with every additional project.
• Manual adjustments tothe calculatedresults toaccount for differences
between historicand the current project can help address weaknesses ofa
model or underlyingdata, e.g., ifqualitative and environmental factors arenot
fully fed into themodel.

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Demerits:
• Parametricestimating canbetime-consuming and costly. Obtaining the historic
data andbuilding a model requires some efforts and resources.
• The required availability of historicdata and the expected scalabilityare
furtherconstraints forthe useof this technique.
• It can often only be used for some parts of a project while others need to be
estimatedwithdifferent techniques.
• Relying on the data may not be appropriate if certainfactors differ betweenthe
currentand previous projects. Aspects suchas theexperience of thepersonnel,
the progress on thelearning curve, environmental factors and other criteria may
not be fully reflected in a model. Thus, the reliability of calculatedestimates may
be affected.
• The quality of thehistoric data may alsobe an area of concern in somecases. The
saying‘garbagein, garbageout’ applies to parametricestimating inthe sameway it
is truefor any other use of data.
• Parametricestimating has theinherent riskof providing a false senseof accuracyif
models areinaccurateor data from other projects prove not to apply to the
current project.
EXAMPLE:
ESTIMATING IMPLEMENTATION COST OFIT SYSTEM: A softwarevendor is askedto
estimatetheimplementation cost of its solution. The implementationconsists of 4 parts –
installation, customizing, theestablishment ofinterfaces toother systems andtesting.
PART PARAMETER HISTORIC
AVGCOST
PER
PARAMETER
HISTORIC
AVGTIME
PER
PARAMETER
PARAMETER
VALUEIN
CURRENT
PROJECT
ESTIMATED
COST
ESTIMATED
TIME
Installation Fix 25000 10 days fix 25000 10days
Customizing Diff.Pro lines
the client
produce
12000 5days 15 product
line
180000 75days
Establishment
of interfaces
No. Of
interfaces
with other
systems
20000 5 days 5 system 100000 25days

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Testing Costof
customizing
+ costof
interfaces
300 0.0089 days Sumof
customizing
and
interface
cost=
280000
84000 25days
SUM 389000 135days
3. Bottom-UpEstimating
In bottom-up estimating, a larger project is broken down into several
smaller components. The project manager then estimates costs specificallyforeach of these
smaller work packages. For example, if a project includes work that will be split between
multiple departments within anorganization, costs might be split out by department. Once
all costs have been estimated, they aretalliedinto a single larger cost estimate for the
project. Becausebottom-up estimating allows a project manager totake a more granular
look at individual tasks withina project, this technique allows for a very accurateestimation
process.
Fig 2.3 StructuralRepresentation ofBottom-UpEstimation
Merits:
• Bottom-up estimates canbevery accurate. This is becauseteam members are
estimating thepiece of work, they areresponsible for. As they typically havethe
most knowledge of their work package, theirestimates tendto be much more
accuratethantop-down estimates.
• Estimationerrors canbalance out across thecomponents of a project. If thetime

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or costof one work packagehas been underestimated, forinstance, this could be
offset by an overestimationof another work package. Such errors might therefore
not necessarilyimpact the budget baseline at the project level.
• Bottom-up estimating canbe used in conjunction with other estimation
techniques, e.g.,theactivitydurationcould be obtained through parametric or
analogous estimating.
Demerits
• The underlying assumption is that the project estimate consists of the sum of its
pieces.This mayignoreoverhead and integrationefforts that may occur in addition
to the work defined in activities. This holds for large and complex projects.
• The bottom-up estimationitselfrequires a lot more resources thanother
techniquessuchas analogous estimating (top-down estimation).
• The cost estimationis basedon theduration estimate. Bothrely on the
estimatedresourcerequirements. Thus, anestimationerrorthere would lead
to inaccuratetimeandcost estimates as well.
In practice, bottom-up estimates canbeprone to thebias or the interests ofthe
estimators. Whilethis applies to all types of estimates (tosomeextent), it may be less
manageablein bottom-up estimating. This is becausethese estimations areusuallydone
by many different estimators, i.e.,those responsible fora work package.
EXAMPLES:

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4. Three-Point Estimating
In three-point estimating, a project manager identifies three separateestimatesfor
the costsassociatedwitha project. The first point represents an“optimistic” estimate, where
work is done and funds spent most efficiently; the secondpoint represents the“pessimistic”
estimate, whereworkis done and funds spent in the least efficient manner; and the third
point representsthe“most likely” scenario, which typically falls somewhere in themiddle.
Three-point estimating relies on severalweightedformulas and originates from th
eProgram
Analysis and Review Technique (PERT).
Triangulardistribution: E = (o + m + p ) / 3
Fig 2.4 GraphicalRepresentationofTriangular Distribution

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Beta (or PERT): E = (o + 4m +p ) / 6
Fig 2.5 GraphicalRepresentationOfBETA Distribution
2.3 TYPES OF COST ESTIMATES IN CONSTRUCTION
Preliminary cost estimate(abstract cost estimateorapproximatecost estimateorbudget estimate):
It is generally preparedin the initial stages toknow the approximatecost of the
project. Thecompetent sanctioning authority can decide the financial position and policy for the
administrationsection. The approximatecost of eachimportant item of work is displayed
individually to know the necessityand utility of eachitem of work which includes the cost oflands,
cost of roads, electrification, water supply costs, cost ofeach building, etc. Here, it’s prepared
concerning the cost of similar type projects in a practicalmanner.
1. Plinth area cost estimate:
Plinth area estimateis obtainedby multiplying plinth area of building with plinth area
rate. Open areas, courtyards, etc. arenot included in the plinth area. If thebuilding is
muti-storied, the plinth area estimateis prepared separatelyforeach floor level. Plinth
area estimateis obtained by multiplying plinth area of building with plinth area rate.
Open areas, courtyards, etc. arenot included in theplinth area.
2. Cube rate cost estimate
Cube ratecost estimateofa building is obtained by multiplying plinth area with the height of
building. Height of building should be consideredfrom floor level to the top of the roof level.
3. Approximate quantity method cost estimate
the totalwall length of the structureis measured, andthis length is multiplied by the
rateperrunning meter which gives the cost of thebuilding. The rateper running
meter is calculatedseparatelyfor thefoundation and superstructure.

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4. Detailedcost estimate
Detailedcost estimateis prepared when competent administrativeauthority approved the
preliminary estimates. This is very accuratetypeof estimate. Quantities ofitems of work are
measuredand the cost of eachitem of work is calculatedseparately. Therates of different itemsare
provided according to the current workable rates andtotalestimatedcost is calculated. 3to 5 % of
estimatedcost is added to this for contingencies as miscellaneous expenditure. IT must alsocontain:
• Report
• GeneralSpecifications
• DetailedSpecifications
• Drawings/plans – layout plans, elevation, sectionalviews, detaileddrawings etc.
• Designs andcalculations –In case of buildings design of foundations, beams, slabetc.
• Schedule of rates
5. Revisedcost estimate
Revisedcost estimateis a detailed estimate, andit is preparedwhen theoriginal
sanctionedestimatevalue is exceeded by 5% or more. The increasemay be due to
sudden increasein costof materials, cost of transportationetc. Thereasonbehind the
revision of estimateshouldbe mentioned on the last pageof revisedestimate.
6. Supplementary cost estimate
Supplementary cost estimateis a detailed estimate, andit is prepared freshlywhen there
is a requirement of additional works during theprogress of original work. The estimate
sheet shouldconsist of cost of original estimateas wellas the totalcost of work including
supplementarycost of work for which sanctionis required.
7. Annual repair cost estimate
The annual repair cost estimate is alsocalled as annual maintenance estimate which is
preparedto know the maintenance costs of the building which will keep the structure in
safecondition. Whitewashing, painting, minor repairs, etc. aretakeninto consideration
while preparing annualrepair estimatefor a building.

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2.4 FUTURE OF COST ESTIMATION
MODEL BASED ESTIMATION:
One of the biggest trends in pre-construction is the rising incorporation of BIM in
estimation. Using programs suchas Autodesk‘s Revit in tandemwith takeoff software such
as Tekla Structures or Vico Takeoff Manager, contractors can extract quantities directly
from models and plug them into estimating software such as WinEst, Sage 300
Construction, and Vico Cost Planner to develop early cost estimates for buildings much
faster. Not only does this help automatetheestimating process; it alsoallows contractors
to accommodatedesignchanges more easily.
EXAMPLE: Skanska USA, for instance, is now doing “parametricestimating,” inwhichit links
its models to a customized template that allows not only for real-time quantity extraction
but alsoreal-timecost-estimation. Vicosoftwareallows forintegrationbetween3-D models
and various estimating programs such as WinEst, Timberline, and MC2. However, the
database management required to get the models and programs talking to each other is
tedious and timeconsuming.
VIRTUAL REALITY
A report releasedby Goldman Sachs last year, “Profiles in Innovation: Virtual& Augmented
Reality,” predicted that the AR/VR market in the US could be as large as $182 billion by
2025,and a few construction companies are already seeing ways the technology might be
useful in the pre-construction stage. VR not only to show what its buildings could look like
but to perform what it calls “immersive estimating.”
COMPUTATIONAL AND GENERATIVE DESIGN
thegenerativedesignsoftwarehasn’tbeencombineddirectlywith anyestimating tools yet,
but it very well could be soon, and Petersonthinks it might have to be for cost estimation
to keep pace with the rest of the preconstructionprocess. “[Let’s say you have] a hundred
designsolutions in a week or two,” he says. “How do you provide feedback from a pricing
and scheduling perspective for all those in as quick a manner? Right now, a lot of it is
manual.”

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“The future of cost estimation” (2017)builtworlds)
2.5 ESTIMATING SOFTWARES FOR CONSTRUCTION
The list of software used for the cost estimation and project management are listed above.
Where, take off refers to the quantities and amount of labour required to complete a construction
project.

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Chapter 3
COST PREDICITON
3.1 DEFINITION
Cost Prediction is used for the purpose of predictions to reduce time risk
assessment thatare indispensablesteps fortheprocess of decision-making of managers. Cost
prediction is a vitalprocess for every business in that it is a predecessorfor budget prices and
resourceallocation in a project life cycle.
The project scope is known there are more chances to generate estimates that are more
accurate in that more specifications of the project are defined. Construction industry due to
its characteristics andlargeamounts of capitalneeded to initiate and continue the project is
theproject types which need moreattentionbecausetheyarehigh risk. Either overestimating
or underestimating the cost of these projects will lead to future deviations in budget v/s
realizedcost.
However, the conventional methods have shown that they are not merely enough. Thereby
the lack of a systematic approach to reduce the error of the estimation process has entailed
in studies that most of all have tried to take advantage of mathematical models, machine
learning techniques, and soon to overcome inaccurate or may even erroneous predictions.
3.1.1OBJECTIVES
1. Investigating thecriteria for constructionprojects cost estimation.
2. Determine thecriteria of construction projects basedon application area, method
applied, techniques implemented, journals, and theyear of publication.
3. Reviewing the existing models of machine learning techniques in cost estimationof
constructionprojects.
4. Assessing themethods, techniques, andcriteria for construction project cost estimation.

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3.2 COST ANALYSIS APPROACHING METHODS IN
DIFFERENT AREAS OFCONSTRUCTION
Fig 3.4 Approach Applications Applied In Different Areas
In almost all the cases, estimating at theveryearly stageofthe project is of a
great concern. Most of theproposed estimationtechniques tried to meet the expectation
by generating modelstobe applied at even tendering level to help process of decision-
making of lead. Fundamentally, effective cost factors shallbe explored and scrutinized
exactly. Not only, the effective cost factors should be studied, but alsothe factors
affecting the cost model accuracymust be reviewed in deep. One of the cost factors that
have been noted repeatedly is theregional factor,whichshows the importance of
differentiating between projects with diverse geographicalorigin. Additionally, the ability
of themodel to expand generallyand theapplicability to novel cases has thehigh degree
of importance.
3.3 METHODSISCONSTRUCTIONCOSTESTIMATES
PARTICLE SWARMOPTIMIZATION(PSO):
The primary objective of PSO is to optimize the costand-orduration
values and exploration for an optimum set of unknown coefficients, as illustratedin the
proposed model sectionfrom within the solution space. PSO is considerablyfast and find
the best solution with high accuracyand it has been shown even that some problems

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have high effect, but it didn’t choose becausethe constrainshow high cost and longtime
therefor they become out of the selection’s proved to be powerful and accuratetoll in
solving the constructionproblems and it can be used as basefor other problems in other
area. Theother strengthof this model is that it is based on existing projects and is more
reliable than theprojects basedon judgement and experimentalcases. Theproposed
models aresimulated utilizing MATLAB to optimize the duration and cost amount model
for the construction projects.Theobjective function used in this study is theroot mean
squareerror and the expressionis givenby,
Where y’=forecast value and y= actualvalue and n= number of data samples
(Khalaf TZ et al (2020) Particle swarm optimization-based approachforestimation of
costs anddurationofconstructionprojects.Civ Eng J 6(2):384–401)
ARTIFICIAL NEURAL NETWORKS(ANN):
Computationalmechanisms that can acquire, represent and compute
function from one multivariatespaceof information to anothergivena set of data
representing that function. ANN's arefunctional abstractionof the biological neural
structureof the centralnervous systemthat aremore effective thantraditional methods
for solving complex qualitativeor quantitativeproblems where the parameters for
conventionalstatisticalandmathematicalmethods arehighly interdependent and data is
intrinsically noisy orincomplete or prone to error. The cost variables used in the study as
inputs arestructuralsystem, building function, exteriorfinishing, building height,
decorating class and site accessibility.
The neurons in the input layer are connected to thosein thehidden layers by the
synaptic weights. Thecommon transfer functions usedare the summationfunction
and the sigmoidsquashing function.
(Rumelhart 1986; Adeli2001; Aleksanderand Morton1993; Rudomin et al. 1993;
Arbib 1995;Geon 2005; Sivanandamand Deepa 2006; Bala et al. 2014).

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Fig 3.5 FeedFormal NeuralNetworks
3.4 CASE STUDY
COST PREDICTION MODEL FOR INSTITUTIONALBUILDINGS IN NIGERIA
(KABIR BALA & SHEHU AHMAD)
PURPOSE:
The purpose of this study was developing a computer-based cost prediction model for institutional
building projects in Nigeria using artificial neural network (ANN) technique. The back-propagation
network learns by exampleand provides good prediction to novel cases.
METHODOLOGY:
The input variables were derived from relatedworks with modification and advice from professionals
through a field survey. Two hundred and sixty completed project data were used for training and
development of theANNmodel. Back-propagationalgorithmusing thegradient descent delta learning
rule with a learning coefficient of 0.4 was used. The input layer of the model comprised of nine
variables: building height, compactness of building, construction duration, external wall area, gross
floor area, number of floors, proportion of opening on externalwalls, location index and timeindex.
VALUE:
The study thus provides a simple, yet effective means of predicting construction costs of institutional
building projects in Nigeria using an ANN model.

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APPROACH:
⚫ I/P variables werederived from relatedfield works through heuristicmethod.
⚫ 260 completed project data were usedfor developing the model
The I/P layer of the model had comprisedof 8 variables such as:
1. Building height
2. Compactness ofbuilding
3. Constructionduration
4. Externalwall area
5. Gross floor area
6. Number of floors
7. Proportion of opening on externalwalls
8. Locationindex and time index
RESEARCH METHOD:
To achieve the studyobjectives 3 set of data was collected and they are:
1. Theuseof structuredquestionnaires toestablishtheinput variables forestimating theconstruction
cost of institutionalbuilding projects
2. Expert interview for the information obtained throughthe questionnaire.
3. Project documents for training and testing of the model.
Based on the result of the input variables, a cost prediction model was developed using ANN
technique. Severalmultilayer perceptronnetworks were developed using theBP algorithm. Using the
BP method of analysis helps to minimize the total squared error of the output. The aim of this is to
train thenetwork to achievea balance betweenthe ability to respond correctlyto the input patterns
that areused for training and the ability to provide good responseto the inputs.
TRAINING & TESTING:
• 260 nos. of completed data were collectedfrom the North-westernpart of Nigeria
• 70% data was randomly collected to be used as training data set

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• 20% data was the testing set inwhich the performanceof the ANNmodel was tested
• 10% data wereused as holdout samples
The performance of the resulting model was checked basedon the errors betweenthe desired & the
computed output values for testing the data set The training flow is carried out in the following
flowchart.
MODEL VALIDATION
• Having developed the models through training and testing, the model was validated by
selecting 30 completed project data and predicting their final cost using the model.
• MS Excel was usedas aninterface for implementation.
• The predictedvalues and theactualvalues were comparedand the averageerrors, maximum
mean squareerrors and MAPE’s weredetermined.

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RESULTS
• I/P VARIABLES: The variables were reduced to nine in an expert interview with professionals
confirming thosethat couldbeusedbasedon theworking conditions of quantitativemethods.
• DEVELOPMENT OFANN: One of the first tasks indeveloping an ANN model is to determinean
acceptable threshold for error in output. An ANN model can be manipulated in ways to
improve its performance, internal architecture, learning paradigmorparameters
ERROR MODEL VALIDATION: outperformed all the other models with a MAPE of 5.4% and MSE of
0.1273.
TRAINING & TESTING: Thenetworkhas SSE andrelativeerrorofthetesting data set of 0.002and0.271

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Due to the flexibility of MS Excel, it was used as the interfacefor implementing the model. The step-
by-step procedure for implementation is described:
Input generalproject information:
Totalweighted input y ln(j):
First Hidden Layeryi:
Second Hidden Layer y2nj:
Sigmoid Transfer Function
Totaloutput value using biases & weight:
CONCLUSIONS:
The ANN model developed has yielded satisfactoryresults on the test samplewith
averageerrorof 2.32%& MAPE of 5.4%with a reasonablecomputation time. It alsoshows advantage
over other conventional methods of cost estimation that use the knowledge of the experts. Due to
the existence of nonlinear relationships and interactions between project costs and uncertain
engineering characteristics, it is recommended that practitioners should adopt the use of ANN.The
limitationof themodel is that it maynot besuitableforother building types becauseoftheuniqueness
of such facility even though significant difference is not anticipated for buildings such as commercial
and residential

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Chapter 4
CONCLUSION
A conclusion is drawn that in almost all the cases, estimating at the very early stage
of the project is of a great concern. Most of the proposed estimation techniques tried to meet the
expectation by generating models to be applied at even tendering level to help process of decision-
making of managers. Fundamentally, effective cost factors shall be explored and scrutinizedexactly.
Not only, the effective cost factors should be studied, but also the factors affecting the costmodel
accuracy must be reviewed in deep. One of the cost factors that have been noted repeatedly is the
regional factor, which shows the importance of differentiating between projects with diverse
geographicalorigin. Additionally, the ability of the model to expand generally and the applicability to
novel cases has thehigh degree of importance.
As shown by results, the present study explores the existing methods and techniques for the cost
estimationofprojects and extracts approaches components. Among thevarious methods (ANN, Fuzzy
NN, SVM, PSO, RBF, RA, CBR, PSO, Decision Tree, AHP, Monte Carlo, fuzzylogic) used by researchers,
the most popular machine learning techniques that used in the reviewed papers are ANN and RA
respectively. In contrast to other methods, the ANN and RA are the most popular and successful
methods implemented in these studies respectively. However, the hybridmodels of ANN with fuzzy
logic, CBR, GAand so forth have surpassedthe mere ANN applied. Thepoint that shall be considered
in ANN application is its sensitivityto input data.
Since this machine learning technique is data driven, it will perform more accurately, ifa large amount
of data and homogeneous data set exists to extract relations between available data. On the other
hand, the number of input neurons (known as cost factors), has a direct effect on systemmalfunction.
Accordingly, when the number of input cost factors increases, the complexity of the system will
increase and in case of construction cost estimation, it showed theaccuracy of the estimation will
decrease. This study finds out in the hidden layer, the number of neurons and the corresponding
weights have a direct effect on thegeneralizationability of the model. Indeed, the number of factors
is important rather than learning parameters, andit directlyaffects the estimationmodel accuracy.
LIMITATTIONSOFML
• Data is collected from Google Scholars and Science Direct scientificdatabase;
therefore,thearticles did not cite in thesetwo databases didnot consider in the

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study as well.
• The study had the limitation of exploring the English languagepapers in
the costestimationforconstruction projects domain only and not
considered the other languages.
Basedon this study, deep-learning techniques did not get attention of researchers inthe
field ofcost estimationfor constructionprojects; therefore, this systematicreview
suggests these techniques andmodels for future propose work and study.
In addition, ANN is known as a powerful model in tackling with nonlinear problems.
Tuning the ANN parameters, suchas thenumber of hidden factors and weights have also
been the concernof many studies, which have been overcome by combining it with GA
algorithm. Nevertheless, theexpert knowledge to select cost factors in the estimation
models has a valuable influence.
Furthermore, the building and highway projects assignthemost attentionof the
researchers tothemselves incost estimationstudies. Among thesestudies, themethods
have been categorizedbasedontheir approach, including intuitive, parametric, analogous,
and analytical, which the most studies belong to the analogous group. Furthermore, the
building and highway projects assignthemost attentionof the researchers tothemselves
in cost estimationstudies. Among thesestudies, themethods have been categorized
basedon their approach, including intuitive, parametric, analogous, andanalytical, which
the most studies belong to the analogous group.

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REFERENCES
(Chapter-1)
[A] Pamidimukkala, Apurva & Kermanshachi, Sharareh & Karthick, Sanjgna. (2020).
Impact of Natural Disasters on Construction Projects: Strategies to Prevent Cost and
Schedule Overruns in ReconstructionProjects. 10.3311/CCC2020-054.
B] Ugwu, Onuegbu & Wai, M. & Kumaraswamy, M.. (2005). Towards a Balanced
Scorecardfor Assessing theEnvironmentalImpact of ConstructionActivities.
[C] “Building MaterialPrices Climbing at RecordYear-to-DatePace”-Economics, 2020
[D]“Challenges OfIndian ConstructionIndustry”-WienerbergIndia, 2021
[E]” The future of cost estimation" (2017) builtworlds
[F] (Al-Bani 1994, Lopez 1993, Pantzeter 1993, Akeel 1989. Ellis 1989; and Uhlik 1984)
(Chapter-2)
[G] “Cost EstimationinProject Management”TIMSTOBIERSKI (2019)
(Chapter-3)
[H] Khalaf TZ et al (2020) PSO based approach for estimation of cost and
duration ofconstruction projects. CIV ENG J 6(2):384-401
[I] Jiang Q (2019) Estimation of Construction Project Building Cost by Neural
Network. JEng Des Technol 18(3):601-609
[J] Alpaydin E (2014) Introductionto Machine Learning. MIT PRESS, Cambridge.
[K] (Rumelhart 1986; Adeli 2001; Aleksander and Morton 1993; Rudomin et al. 1993;
Arbib 1995; Geon 2005; Sivanandam and Deepa 2006; Bala et al. 2014

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