VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY
--------------------

THONG THI KIM ANH

COST ESTIMATING FOR DESIGN AND BUILD (D&B)
PROJECTS ACCORDING TO CONDITION IN VIETNAM

Major:

Construction Management

Major code:

8580302

MASTER’S THESIS

HO CHI MINH CITY, JULY 2023


THIS RESEARCH IS COMPLETED AT:
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY – VNU-HCM
Supervisor: Assoc. Prof. Long Duc LUONG

Examiner 1:

Dr. Nhat Minh HUYNH

Examiner 2:

Dr. Ngoc Chau DANG

This master’s thesis is defended at HCM City University of Technology,
VNU- HCM City on 13th July 2023
Master’s Thesis Committee:
(Please write down full name and academic rank of each member of the
Master’s Thesis Committee)
1. Dr. Thu Anh NGUYEN

- Chairman

2. Assoc. Prof. Hoc Duc TRAN

- Member, Secretary

3. Dr. Minh Nhat HUYNH

- Reviewer 1

4. Dr. Chau Ngoc DANG

- Reviewer 2

5. Dr. Cuong Viet CHU

- Member

Approval of the Chairman of Master’s Thesis Committee and Dean of Faculty of
Civil Engineering after the thesis being corrected (If any).

CHAIRMAN OF THE COUNCIL

DEAN OF FACULTY OF CIVIL
ENGINEERING


i
VIETNAM NATIONAL UNIVERSITY - HO CHI MINH CITY

SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom - Happiness

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY

THE TASK SHEET OF MASTER’S THESIS
Full name: Thong Thi Kim Anh

Student ID: 217085

Date of birth: 18/04/1985
Major: Construction Management

Place of birth: Ninh Thuan
Major ID: 8580302

I.

THESIS TITLE:
COST ESTIMATING FOR DESIGN AND BUILD (D&B) PROJECTS
ACCORDING TO CONDITION IN VIETNAM

ƯỚC TÍNH CHI PHÍ CHO CÁC DỰ ÁN THIẾT KẾ VÀ THI CÔNG THEO ĐIỀU
KIỆN TẠI VIỆT NAM

II.

TASKS AND CONTENTS:
THE GOAL OF THE RESEARCH IS TO PROVIDE A MORE PRECISE
METHOD FOR COST ESTIMATION OF NEW PROJECTS BY USING THE
DATA ALREADY AVAILABLE FROM PREVIOUS PROJECTS.

III. THESIS START DAY: 6TH FEBRUARY 2023
IV. THESIS COMPLETION DAY: 11TH JUNE 2023
V.

SUPERVISOR: LUONG DUC LONG, ASSOCIATE PROFESSOR
Ho Chi Minh City, date ………

SUPERVISOR
(Full name and signature)

HEAD OF DEPARTMENT
(Full name and signature)

LUONG DUC LONG
DEAN OF FACULTY OF CIVIL ENGINEER
(Full name and signature)

Note: Student must pin this task sheet as the first page of the Master’s Thesis booklet



ii

ACKNOWLEDGEMENT

It is my pleasure to extend my appreciation to all those who helped me to
accomplish this master dissertation work.
Most importantly, I am greatly indebted to my supervisor Luong Duc Long,
Associate Professor for his excellent advice, encouragement, and support
throughout my master degree. He provided me with unlimited potential to become
researcher during the master program. His perceptive guidance, keen advice, support
have served as the source of my inspiration.
Next, I would like to thank the professors of the Department of Construction
Management, Faculty of Civil Engineering for their dedication in teaching and
imparting specialized knowledge during my study at the school.
And thank my colleagues and friends who have given their comments,
participated in surveys as well as shared valuable knowledge and experiences and
supported me to complete this research.
I also would like to thank my classmates for enjoying student life and studying
with me during the master course.
Finally, I wish to warmly and deeply thank my family for their prayer,
patience, and emotional support during my entire graduate career. Their love, beliefs,
and blessings made my task easy and comfortable.


iii

ABSTRACT
In the construction industry, particularly during the tender stage, accurate cost
estimates play a crucial role in the Design & Build procurement delivery process.
These estimates serve as essential data for decision-makers who must determine

whether to continue or stop the project. Additionally, they are vital for effectively
mobilizing capital to ensure successful project execution. Therefore, achieving high
accuracy in cost estimations at an early stage is of utmost importance.
Historically, traditional cost estimating techniques have not been able to
effectively leverage existing knowledge from previous projects and their associated
costs. Consequently, these methods often result in significant variance, are timeconsuming, and prone to errors, significantly impacting the financial aspects of
proposal drafting.
Fortunately, advancements in computer technology and mathematical programming
skills have paved the way for more sophisticated cost estimating strategies that rely
on large datasets and complex procedures. Artificial Intelligence (AI) methods have
emerged as a product of these breakthroughs.
This research aims to develop a precise AI-based approach to cost estimation,
utilizing data from previous projects to estimate costs for new ones. The main task is
to establish an AI methodology for cost estimation and compare its accuracy with
existing methods.
The study has identified that AI approaches have the potential to address the
limitations of conventional methods. Recent research has shown promising results
using Case-Based Reasoning (CBR), Random Forest (RF), and Artificial Neural
Networks (ANN) to overcome these challenges and successfully estimate project
costs.

Keywords: Design-Build; Parametric estimating; Comparative estimating; Artificial
Neural Network; Case Based Reasoning; Random Forest


iv

TÓM TẮT LUẬN VĂN THẠC SĨ
Trong ngành xây dựng, đặc biệt là trong giai đoạn thầu, việc ước tính chi phí chính
xác đóng một vai trị quan trọng, đặt biệt là cho hình thức hợp đồng là Thiết kế & Thi

cơng. Những ước tính này đóng vai trị quan trọng là dữ liệu cần thiết cho những người
quyết định phải xác định liệu có tiếp tục hoặc dừng dự án. Ngoài ra, chúng cũng rất quan
trọng để hiện định vốn một cách hiệu quả nhằm đảm bảo thực hiện dự án thành cơng. Do
đó, việc đạt được độ chính xác cao trong việc ước tính chi phí ở giai đoạn sớm là rất quan
trọng.
Lịch sử cho thấy các kỹ thuật ước tính chi phí kiểu truyền thống khơng thể tận
dụng hết hiệu quả của các kiến thức đã có từ các dự án trước đó và các chi phí liên quan.
Do đó, các phương pháp này thường dẫn đến sai biệt đáng kể, tốn thời gian và dễ gây lỗi,
ảnh hưởng đáng kể đến mặt tài chính của việc soạn thảo đề xuất.
May mắn thay, những tiến bộ trong công nghệ máy tính và kỹ năng lập trình tốn
học đã mở ra đường cho các chiến lược ước tính chi phí phức tạp hơn, dựa trên các bộ
dữ liệu lớn và các quy trình phức tạp. Phương pháp Trí tuệ nhân tạo (AI) đã nổi lên như
một sản phẩm của những đột phá này.
Nghiên cứu này nhằm phát triển một phương pháp tiếp cận AI chính xác để ước
tính chi phí, sử dụng dữ liệu từ các dự án trước để ước tính chi phí cho các dự án mới.
Nhiệm vụ chính là xây dựng một phương pháp tiếp cận AI cho việc ước tính chi phí và
so sánh độ chính xác của nó với các phương pháp hiện có.
Nghiên cứu đã xác định rằng các phương pháp AI có tiềm năng giải quyết những
hạn chế của các phương pháp truyền thống. Nghiên cứu gần đây đã cho thấy những kết
quả đáng hứa bằng cách sử dụng các phương pháp Học dựa trên trường hợp (CBR), Rừng
ngẫu nhiên (RF) và Mạng nơ-ron nhân tạo (ANN) để vượt qua những thách thức này và
ước tính chi phí dự án một cách thành cơng.

Từ khóa: Design-Build; Ước tính tham số; Ước tính so sánh; Mạng nơ-ron nhân tạo; Lý
luận dựa trên trường hợp cụ thể; Rừng ngẫu nhiên.


v

AUTHOR’S COMMITMENT


The undersigned below:
Name

: Thong Thi Kim Anh

Student ID

: 2170852

Place and date of born

: Ninh Thuan, April 18, 1985

Address

: Ho Chi Minh City

With this declaring that the master thesis entitled “Cost Estimating for Design and
Build Projects according to condition in Vietnam” is done by the author under
supervision of the instructor. All works, ideas, and material that was gain from other
references have been cited in the corrected way.
Ho Chi Minh City, June 10th 2023

Kim Anh THONG


vi

CONTENTS

THE TASK SHEET OF MASTER’S THESIS ............................................................ i
ACKNOWLEDGEMENT............................................................................................ ii
ABSTRACT ................................................................................................................ iii
TÓM TẮT LUẬN VĂN THẠC SĨ ............................................................................ iv
AUTHOR’S COMMITMENT ..................................................................................... v
CHAPTER 1................................................................................................................. 1
INTRODUCTION ....................................................................................................... 1
1. 1- Definitions: .................................................................................................................................... 1
1. 2- Research background .................................................................................................................... 3
1. 3- Problem statement ......................................................................................................................... 5
1. 4- Research Goal ................................................................................................................................ 6
1.5- Relevance ....................................................................................................................................... 6

CHAPTER 2................................................................................................................. 8
OVERVIEW ................................................................................................................ 8
2.1- Conventional cost estimation approaches ...................................................................................... 8
2.2- Artificial intelligence estimation methods.................................................................................... 13
2.3- The appropriate cost estimation method ....................................................................................... 16
2.4- Related researches ........................................................................................................................ 18

CHAPTER 3............................................................................................................... 22
RESEARCH METHODOLOGY ............................................................................... 22
3.1. - Machine Learning (ML)............................................................................................................. 22
3.2. - Research Steps ........................................................................................................................... 23
3.3. - Random Forest (RF) .................................................................................................................. 23
3.4. - Artificial neural networks (ANN) .............................................................................................. 25
3.5. - Case based reasoning (CBR)...................................................................................................... 36

CHAPTER 4............................................................................................................... 44
COST DATA AS AN EXAMPLE APPLICATION .................................................. 44

4.1- Problem Definition: ...................................................................................................................... 44
4.2- Data: ............................................................................................................................................. 45


vii
4.3- Identify the factors and profile of casebase: ................................................................................. 45
4.4- Selecting performance evaluation metrics: .................................................................................. 50
4.5- Performance evaluation: ............................................................................................................... 52
4.6- Result Comparison and Discussion: ............................................................................................. 65

CHAPTER 5............................................................................................................... 68
CONCLUSION .......................................................................................................... 68
5.1- Conclusion .................................................................................................................................... 68
5.2- Future research direction .............................................................................................................. 69

LIST OF PUBLICATIONS .................................................................................... 71
REFERENCES .......................................................................................................... 72
APPENDICES ........................................................................................................... 76


viii

LIST OF FIGURES

Figure 1.1- Design‐Bid‐Build…………………………………………………………...1
Figure 1.2- Design & Build………………………………………………………….…..2
Figure 1.3- Transition from early cost estimation to final cost……………………….…..6
Figure 3.1- Machine learning procedure……………………………………...……...…22
Figure 3.2- Different types of machine learning………………………………….….…23
Figure 3.3- Research steps…………………….……………………………………..…23

Figure 3.4- Model of Random Forest…………………….……...……………….….…24
Figure 3.5- Structure of neural network…………………….……...………….…….…26
Figure 3.6- Processing Unit……………...………………….……...………….…….…26
Figure 3.7- Identity function……………...………………….……...………….………27
Figure 3.8- Sigmoid function……………...………………….…......…………………28
Figure 3.9- Hyperbolic Tan Function……………...………………..…………….……29
Figure 3.10- Feedforward propagation Network……………...….……………...…..…32
Figure 3.11- Recurrent neural network………..…………….....………………...…..…33
Figure 3.12- Back-propagation………………..………….…...…….…………..…..…33
Figure 3.13- CBR cycle process………………..……………...………………...…..…38
Figure 3.14- Formatting and Data Organization………………..…………...…..…..…39
Figure 3.15- Formula of similarity case for Textual symbol…………………….…...…40
Figure 3.16- Formula of similarity case for numerical symbol…………………….…...40
Figure 3.17- Calculation of the Weight………………………………………..…..…...41
Figure 3.18- GA Solver Program……….………………………………….…..…..…...42
Figure 3.19- Calculate the similarity of case……….……………………….……..…...43
Figure 4.1- Application flow Chart……….……………………………………….…...44


ix

Figure 4.2- Cronbach's Alpha coefficient……….………………………………....…...47
Figure 4.3-Cost Comparison between Actual cost and predicted cost in RF model.…..54
Figure 4.4-Category of variable………..……….……………………………….....…...56
Figure 4.5- Setup of location variable………..……….……………………..….....…...56
Figure 4.6- Setup of floor type variable………..……….………………..…...…....…...56
Figure 4.7- Setup of specification of material variable………..……….……………….56
Figure 4.8- Arrangement of variables..………..……….………………..…...…....…...57
Figure 4.9- Selection of Architecture...………..……….………………..…...…....…...58
Figure 4.10- Selection of training type...………..……….…………………...…....…...59

Figure 4.11- Selection of Output……....………..……….…………………...…....…...59
Figure 4.12- ANN architecture...……....………..……….…………………...…....…...60
Figure 4.13- The error of model...……....………..……….…………….…...…....…....61
Figure 4.14- The matrix of weight...……....………..……….………….…...…....…….61
Figure 4.15- Scatterplot chart of predicted value of each dependent variable………….62
Figure 4.16- Cost Comparison between Actual cost and predicted cost in ANN model..63
Figure 4.17-Cost Comparison between Actual cost and predicted cost in CBR model....65
Figure 4.18- Cost Comparison between Actual cost, predicted cost by CBR, RF and ANN
model……………………………..……....………..……….………….…...…....…….66


x

LIST OF TABLES

Table 1.1- Summary of advantages & disadvantages of Project Delivery Methods.……..2
Table 2.1- Summary of Strengths & weaknesses of AI estimation methods……….….16
Table 2.2- Cost variable sources…………………………………………………….… 21
Table 3.1- Summary of Strengths & weaknesses of Activation function of ANN
method……………………………………………………………………………....… 30
Table 4.1- Construction Index for Construction…………………………………….… 45
Table 4.2- Meaning of Cronbach’s Alpha coefficient values………………………… 46
Table 4.3- Factors affect the cost of faỗade works. 48
Table 4.4- Profile of cases for model validation 50
Table 4.5- RF model result……………………………………………………….…… 54
Table 4.6- ANN model result…………………………………………………..……… 63
Table 4.7- CBR model result……………………………………………………..…… 64
Table 4.8- Comparison of result of 6 cases testing by CBR, RF and ANN model.…....66



xi

LIST OF ABBREVIATIONS

AI

Artificial intelligence

ANN

Artificial neural networks

CBR

Case-based reasoning

RF

Random Forest

CER

Cost estimating relationships

ES

Evolutionary systems

HS


Hybrid systems

KBS

Knowledge-based systems

ML

Machine learning

MLR

Multiple linear regression

MRA

Multiple regression analysis

WBS

Work breakdown structure

SPSS

Statistical Package for the Social Sciences

D&B

Design & Build


BoQ

Bill of Quantities


1

CHAPTER 1

INTRODUCTION
---§---

1. 1- Definitions:
Project Delivery Methods:
- Traditional Approach: The design must be completed before construction
start the works. Design and construction are usually performed by two
different parties who interact directly and separately with the owner (Figure
1). (Hegazy, 2002)

Figure 1.1- Design‐Bid‐Build

- Design and Build (D&B): In this approach, Contractor is in charge
Responsible for design and construction implementation. Because of taking
responsibility design and construction so that the contractor bears all the
important role of the project. One of the solutions that Client and Contractors
the current trend is to switch to the traditional form of transmission Design Bid - Build (DBB) to form D&B (Figure 2). (Hegazy, 2002)


2


Figure 1.2- Design & Build
- Turnkey: This approach is similar to the design-build approach, but the
Builder is responsible for design, construction and project financing. (Hegazy,
2002)
Advantages & Disadvantages of Project Delivery Methods:
Table 1.1- Summary of advantages & disadvantages of Project Delivery Methods
Methods
Traditional

Benefits

Shortcomings

- Competitive price

-The project time take longer.

- The Designer, engineering,

-Designer cannot get the benefit

and constructor is familiar with

from construction experience;

this method

- Conflict between the parties.

- Easy to use in all markets,


- Disputes and claims if have

including public and private

any Changes

Design-

- No conflict among the parties.

- Cost may not be known until

Build/

-Minimum owner involvement.

the end of design.

Turnkey

- Design can get benefit from

- High risk to contractor and

construction experience

more cost to owner.

- Time can be reduced since

overlaps
construction.

design

and


3

Role of Cost Estimating in construction project:
Cost estimation is probably the most crucial function to the success of
construction organizations, due to cost estimation serve several purposes, including
feasibility analysis, budgeting, preparing owner's funding, and a baseline for evaluating
contractors.
The primary goal of cost estimating is to offer a basis for controlling project costs through
the generation of cost estimates framed within the permitted budget and to give the
essential data for the development of projects decision-making process.
Bids Cost estimating needs to be done in different manners at different stages of
a project. The estimator, who often works on behalf of the owner or the designer, may
have to make such an estimate from concept, without dimensions, details, specification
and schedule of the owner's requirements
1. 2- Research background
In a fiercely competitive industry where market shares are declining and profit
margins are shrinking, the cost of delivering services or products emerges as a pivotal
factor in decision-making (Günaydın & Dogan, 2004). During the tendering phase of a
project, the cost estimate for capital expenditures significantly impacts planning,
bidding, design, construction management, and overall cost management (Arage &
Dharwadkar, 2017). Decisions based on these estimates often lead to critical
commitments, including resource allocation, with far-reaching consequences. Accurate

cost estimates enable project managers to assess project feasibility and exercise effective
cost control. Moreover, these estimates can influence the client's decision on whether to
proceed with the project or not (Ahiaga-Dagbui & Smith, 2012). Clients consistently
expect contractors to deliver projects within the approved budget, as it plays a vital role
in ensuring client satisfaction. Consequently, precise cost estimates can bolster a
contractor's reputation and foster stronger relationships with clients.
In recent years, Vietnam has witnessed numerous construction projects
experiencing frequent delays, cost overruns, and failures. These challenges have left
the stakeholders, particularly investors, disheartened by the project implementation
process. The prevailing approach for project implementation in Vietnam is the
traditional method, known as "design-bidding-build".


4

The traditional method is employed when the Bill of Quantity (BoQ) and design
drawings, prepared by experienced professionals in a specific field, are in regulatory
compliance and ready for execution. While this procurement approach provides
design certainty and clear risk allocation, it lacks cost-saving measures, speed, and
the seamless integration of design and construction, which has led to a shift in client
perception (Young, Seidu, Ponsford, Robinson, & Adamu, 2021). Unfortunately, the
BoQ used during the tender stage does not possess the capability to accurately predict
the final project cost. This inadequacy can be attributed to incomplete information in
the drawings and specifications used during the tender stage, resulting in a limited
understanding of the client's requirements.
Embracing a more efficient approach than the traditional one is poised to alleviate
the challenges confronting Vietnam. Design-build, acknowledged as an effective
project implementation method, has garnered widespread recognition and offers
numerous advantages to all stakeholders involved in the process. This progressive
approach is extensively adopted worldwide, including:

-

In the UK, D&B was already in use in the 1960s, and by the end of the 1990s, DB
had captured 23% of the market for new construction projects (Ling, Liu, &
Environment, 2004).

-

In the USA D&B started in the early 1900s (Ling et al., 2004) [9], in the mid1990s, more than one-third of construction projects in USA used the D&B
approach (Ling et al., 2004).

-

In Singapore, D&B has been employed for construction projects since 1992 (Ling
et al., 2004). From 1992 to 2000, the share of D&B projects climbed gradually,
reaching 16% for public sector projects and 34.5% for private sector projects.

Hence, adopting the design-build method for project implementation is likely to result
in high satisfaction among project participants in Vietnam, thanks to the numerous
benefits it offers.
In 2017, Vietnam witnessed a growing trend of design-build adoption, with
leading construction companies embracing this approach as a strategic bidding tactic.
Design-build, being a new global trend in the construction industry, streamlines the
process for investors by eliminating complexities associated with traditional
construction plans, such as managing multiple contractors and dividing work into


5

numerous small packages. The method has the potential to save up to 20-30% of

project progress, optimizing cash flow for all involved parties. In practice, some
projects have achieved savings of 10-15% of the total investment capital when
implementing design-build.
However, it is essential to recognize that design-build is not without its
challenges, especially when applied to large-scale and complex projects. Nonetheless,
embracing the design-build method provides construction companies with an
opportunity to showcase their capabilities while also encouraging the adoption of
modern technologies in Vietnam, making the country more attractive to foreign
investment.

1. 3- Problem statement
Accurate cost-estimating models play a crucial role in decision-making, particularly
when the model is based on concept drawings or at early stages of building design.
During this phase, project information is limited, and estimating costs rely on numerous
variables, making it challenging to combine different factors affecting construction
expenses. In Vietnam, the traditional technique for cost estimation of works, which relies
on concept drawings or early-stage information, is widely known as the floor area
method. This approach is commonly applied during the initial phases of construction
projects. The method involves multiplying the total gross floor area by an acceptable cost
per square meter, obtained from historical data (Castro Miranda, Del Rey Castillo,
Gonzalez, & Adafin, 2022). However, the accuracy of this conventional approach is
generally considered to be relatively low, ranging between 15% to 25% (Petroutsatou,
Georgopoulos, Lambropoulos, Pantouvakis, & Management-asce, 2012) (see figure 1.3
below). Furthermore, the available data for cost estimation in the initial stages is limited
(few projects available, and due to cost volatility, data collection faces difficulties).
Hence, specialized methods like Case-Based Reasoning are needed. The AI technique
promise surpasses the advantages of conventional methods.


6


Figure 1.3- Transition from early cost estimation to final cost (Petroutsatou et al.,
2012)
1. 4- Research Goal
With the limitations outlined in section 1.3 serving as the motivation, the author
introduces to the readers an advanced method based on AI platform, aiming to reduce
error percentage compared to traditional methods while being cost-effective. By
integrating with readily available tools like Excel or other existing computational
tools.
The target of this research is to create an accurate AI-based cost estimation
method and use the data already available about previous projects to estimate cost
performance using D&B procurement method for new projects. By employing this
technique, the predictor can accelerate the cost assessment process and make the cost
estimate more precise. To reach the goal, specific models are constructed to predict
each of the factor performance.
This research is important because the tender team will know the important variables
that they must pay closer attention to so they can be reached expected budget which
required at tender stage. Those significant variables factors that are controllable could
be properly managed to increase the chances of tender package success.

1.5- Relevance
Most cost estimation techniques rely on records of previous estimates and real
costs specific to the bidder, with only a few researchers contributing to this field.


7

The research aims to identify the best cost estimation method within the construction
industry, and AI methodology is considered the most promising approach, considered a
scientific innovation. The study's contributions to the scientific literature include:

✓ Providing a comprehensive overview of both AI-based and traditional cost
estimation methods.
✓ Exploring the potential of AI-based cost estimation methods.
✓ Improving accuracy and efficiency in cost estimation through the use of AI
techniques.
✓ Offering solutions for tender preparation in the construction industry.
From a commercial perspective, the research is highly relevant and useful due to the
following reasons:
➢ Addressing the issue of low accuracy and inefficiency in cost estimation at the
pricing proposal stage, which directly impacts profit margins.
➢ Increasing opportunities and competitive advantage for contractors by reducing
the costs associated with tenders.
➢ Potentially leading to faster cost estimation methods, which can reduce cost
overruns and overhead expenses.
➢ The developed cost estimation method in the construction industry can serve as a
basis for application in other industries.


8

CHAPTER 2

OVERVIEW
---§--2.1- Conventional cost estimation approaches
The literature offers comprehensive understanding of construction project cost
estimation techniques. There are many differences conventional cost estimation
techniques and it based on the project's goal, amount of planning and/or design, size,
complexity, conditions, timetable, and location.
Many of the techniques employed in cost estimate for construction projects can also
be applied to cost estimate in others field. These techniques can be classified as

parametric, historically based on bids, quantity-based on unit costs, within a range,
and based on probabilistic risk estimations (Geberemariam, 2018).
Below shows a general division of the identified traditional methods into parametric,
detailed, comparative, and probabilistic estimates. The advantages and disadvantages
of these various strategies will then be analyzed and listed.
Parametric estimating:
This approach is typically applied in the project's first stages. Using a model with a
mathematical representation of the cost estimating relationships (CER) that can
forecast and offer a logical correlation between the physical and functional aspects of
a project, one can generate a parametric estimation (Geberemariam, 2018). The
features could be functional requirements, performance requirements, or physical
characteristics. Cost-to-cost or cost-to-non-cost variables are used to show CER.
The cost of the independent variable, for instance, might be used to forecast the cost
of the dependent variable in a cost-to-cost relationship. For example, the cost of labor
hours for one component could be used to forecast the cost of labor hours for another
component.
The quantity of output items can be used to estimate the cost of labor hours in a costto-non-cost scenario. The relationships might be as simple as one-to-one or as
sophisticated as an algorithm. An estimating model is a collection of intricate
relationships.
The relevant linear and nonlinear cost estimation relationships (CER) are shown in


9

equations 1 and 2 below. The cost estimating relationships (CERs) framework is what
equations 1 are known as. To quantify the relationship between an independent
variable and contract price, the CER employs quantitative methods (Geberemariam,
2018).
Tc= ∑ni Pcr x PI


(E.q-1)

Where:
Tc = Total Cost
Pcr= Parameter Cost Ratio
PI = Parameter of an Interest

Equation 2 below for CER with associated nonlinear form cost estimation
relationships:
Tc =∑𝑛𝑖 𝑃𝑐𝑟 𝑥 𝑃 𝑛𝑖

(E.q-2)

Where:
PI = Parameter of independent variable of interest
ni = exponent used to transform 𝑷I
The temporal effects of cost, such as inflation, sharp rises in material costs, and for an
independent variable and other metrics, are transformed and normalized using the
exponential factors (E.q-2).
The project's cost drivers should be the variables considered in a parametric estimate.
The underlying presumption is which the factors that influenced costs in the previous
would continue to influence costs in the future. Access to historical data that can be
utilized to identify the cost drivers and the pertinent CER is necessary in order to
employ a parametric technique. Based on the unique characteristics of the project, the
parametric CER can be used to forecast costs for upcoming projects.
Pros:
-

Cost Estimating is usually quickly and easily


-

Actual observations assist to remove the reliance on opinion.


10

Cons:
-

To ensure that they are consistent with the present link between project qualities
and costs, they should be regularly reviewed.

-

Should be accurately and completely described because using the CER incorrectly
could result in substantial estimate errors (Geberemariam, 2018).

Detailed estimation:
The bottom-up or analytical estimation methods are other names for the thorough
estimation approach. With the establishment of a Work Breakdown Structure (WBS)
for each activity that is computed by elements, time, and scope that is carried out in a
project, this method generates a detailed project cost estimate.
A quantity surveyor or other technical person with extensive experience in a certain
activity often calculates and connects the costs per activity to the WBS parts.
The general mathematical formula is shown in equation 3 below. But each project
requires a different approach.

Tc= ∑𝑖 𝑞𝑖 𝑥 (Mi + Li + Ei) +∑𝑗 𝐼𝑗


(E.q- 3)

In which:
𝑻𝑪 = Cost in Total
𝒒𝒊 = Quantity
𝑴𝒊 = Material rates
E𝒊 = Equipment rates
𝑳𝒊 = Labor rates
I𝒋 = Indirect rate

Advantages:
-

Finding out exactly what the estimate includes and whether anything was missed
is one of the biggest benefits of the thorough estimation approach (Geberemariam,
2018).

-

Reveals information on the project's primary cost drivers. Additionally, the distinct
project activities are frequently repeated and can be employed again in subsequent
projects.


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Disadvantages:
-

Executing a thorough estimate might take a lot of time, which makes it expensive.


-

The requirement that every new project require a fresh estimate. Estimates of some
recurring tasks may obtain from earlier projects, however, they have to integrate
into the new estimate's condition.

-

To provide a trustworthy estimate, the project specs must be well-known. if the
specifications’ project always change, the estimate must continuously account for
these changes.

-

During the summation of the many WBS elements, small inaccuracies can become
huge errors.

-

and it take a lot of time to establish, especially in large, complex projects with
many components of the work breakdown structure.

Comparative estimating:
When a new project is similar to one that has just been finished, the comparative
estimating approach can be used to quickly compare the two. The main cost factors
and current expertise from similar projects in the past are required during this phase.
The project's size, complexity, performance requirements, length, location, and
available technology will all be adjusted according to their individual differences.
A comparative estimate is typically used to examine the project's viability and

provides guidance on whether to move forward with the project within the specified
parameters (Burke, 2013). In addition, the analogous method is employed when
attempting to estimate a generic system with few specifications available.
This method technique normally is used by unit method, cost indexes Cost Capacity
Equation 4 or power law and sizing model, and Factored Estimates (Geberemariam,
2018). Equations 4 of the generic mathematical cost estimation are used:
-

Unit approach
Tc = ∑𝑛𝑖 𝑈 𝑥 𝑁

(E.q- 4)


12

In which:
𝑻𝑪 = Cost in Total,
U= Unit Price
N= Quantity
-

Cost Index: The ratio of current costs to previous costs is known as the cost index
(CI). The CI is dimensionless and changes in cost over time to account for the
influence of inflation (Geberemariam, 2018).
𝐼

Tc = ∑𝑡 𝐶𝑜 ( 𝑡 )
𝐼𝑜


(E.q- 5)

Where:
𝑻𝑪 = Total cost estimation at present
𝑪𝟎 = Cost of previous
𝑰𝒕 = Index value at time t
𝑰𝟎 = Index value at base time

Merits:
-

This completes an estimate quite quickly.

-

The accuracy still remains same in case the data from earlier that use for reference
is slight changing.

-

Everyone involved can easily understand the determined estimate.

Demerits:
-

It is quite difficult to identify a project that is similar perspective with the new
project in order to compare.

-


The method relies on extrapolation and professional judgment for the factor
adjustments. As a result, the requirement for normalization may result in a
subjective evaluation of the data and may affect the estimate's accuracy.

-

The method relies on extrapolation and professional judgment for the factor
adjustments. As a result, the accuracy of estimate will be affected if the
requirement for normalization due to the result may in subjective evaluation of the
data.