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AN EVALUATION OF THE ARTIFICIAL NEURAL NETWORK APPROACH FOR COST ESTIMATION OF ENGINEERING SERVICES
Abstract
In a globally competitive world, with diminishing profit margins and decreasing market shares, the cost of a project is one of the major criteria in decision making at the early stages of a building design process in the construction industry. To remain competitive in the market, it is crucial for companies to have an accurate estimate of their projects. Nevertheless, given that very little is known about the scope and details of the project, the conventional cost estimation methods tend to be slow and inaccurate. With the rise of computing power, there is now a tendency to use Machine Learning (ML)-based methods, such as Artificial Neural Networks (ANNs), for more accurate cost estimation that can remain reliable in face of insufficient details during the tendering phase. While the use of ANN for cost estimation has been abundantly investigated from the perspective of contractors, there are very limited studies on the development and application of ML-based methods for engineering consultancy firms. Given that the nature of products/services offered by consultancy firms is inherently different from that of contractors (i.e. they are more abstract and less material-based) and also given that the type and level of detail of the available data at the tendering stage is dissimilar, it is important to investigate the applicability of ML-based methods for cost estimation in consultancy firms. To this end, this paper presents an artificial neural network approach for the cost estimation of engineering services. In developing the model, first, the influential factors that affect the costs of engineering services are identified. Thereafter, a model is developed using the data of 132 projects. Subsequently, a heuristic method is developed to systematically improve and fine-tune the performance of the model. Eventually, the findings show that artificial neural networks (ANNs) can obtain a fairly accurate cost estimate, even with small datasets. In fact, the model proposed in this paper performed better than those proposed in other similar works. The model developed in this study showed a 14.5% improvement in the accuracy of the model, considering MAPE.
TABLE OF CONTENTS
Title Page i
Declaration ii
Approval Page iii
Dedication iv
Abstract vi
Table of Contents vii
CHAPTER ONE: INTRODUCTION
1.1 Inroduction 1
1.2 Background of the study 3
1.3 Statement of the General Problem 4
1.4 Objective of the study 5
1.5 Significance of the study 5
1.6 Statement of hypothesis 6
1.7 Scope of the study 6
1.8 Limitation of the study 7
1.9 Definition of terms 7
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction 9
2.1 Review of related literature 9
2.2 Theoretical framework
2.3 Summary of review 33
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Introduction 35
3.2 Research design 35
3.3 Area of study 35
3.4 Population of the study 36
3.5 Sample size 36
3.6 Instrument for data collection 36
3.7 Reliability of the instrument 37
3.8 Validity of the Instrument 38
3.9 Method of data Collection 38
3.10 Method of Data Analysis 39
CHAPTER FOUR: DATA PRESENTATION AND ANALYSIS
4.1 Introduction 41
4.2 Characteristics of the respondents 41
4.3 Presentation of Data Analysis 43
4.4 Discussion of Findings 48
4.5 Summary of findings 49
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary 51
5.2 Conclusion 52
5.3 Recommendation 53
Biography 54
Appendix 56
AN EVALUATION OF THE ARTIFICIAL NEURAL NETWORK APPROACH FOR COST ESTIMATION OF ENGINEERING SERVICES. GET MORE
