Project Abstract

Abstract
The construction industry is known for its complexity and uncertainties, especially in cost estimation, which can significantly impact project success. This research project focuses on developing a cost prediction model for construction projects using artificial intelligence in the field of quantity surveying. The utilization of artificial intelligence techniques in cost prediction aims to improve the accuracy and efficiency of estimating project costs, leading to better decision-making and cost control throughout the project lifecycle. Chapter 1 of this research introduces the background of the study, highlighting the challenges faced in traditional cost estimation methods in construction projects. The problem statement underscores the need for more advanced tools and techniques to enhance cost prediction accuracy. The objectives of the study are outlined to guide the research towards developing a reliable cost prediction model. The limitations and scope of the study are discussed to provide clarity on the boundaries and constraints of the research. The significance of the study is emphasized in addressing the gaps in current cost estimation practices, and the structure of the research is outlined to guide the reader through the subsequent chapters. Chapter 2 presents a comprehensive literature review, covering ten key aspects related to cost estimation in construction projects, artificial intelligence applications, and the role of quantity surveying in project cost management. The review of existing literature provides a theoretical foundation for the research and highlights the current trends and challenges in cost prediction. Chapter 3 details the research methodology employed in developing the cost prediction model using artificial intelligence techniques. The methodology includes data collection methods, the selection of AI algorithms, model development processes, and validation techniques. Eight key components of the research methodology are discussed to ensure transparency and reproducibility of the study. Chapter 4 presents the findings of the research, discussing the performance and accuracy of the developed cost prediction model. Seven key findings are elaborated upon, highlighting the strengths and limitations of the model and its practical implications for the construction industry. The discussion provides insights into the potential benefits of integrating artificial intelligence in quantity surveying practices for cost estimation. In Chapter 5, the conclusion and summary of the research project are presented, emphasizing the key contributions, implications, and future research directions. The findings of the study are summarized, and the overall impact of the cost prediction model on construction project management is discussed. The conclusion encapsulates the significance of leveraging artificial intelligence in quantity surveying for enhancing cost prediction accuracy and project outcomes. In conclusion, this research project contributes to the advancement of cost estimation practices in construction projects by developing a novel cost prediction model using artificial intelligence in quantity surveying. The integration of AI techniques offers a promising approach to improving cost prediction accuracy and project cost management, ultimately enhancing the overall success of construction projects.

Project Overview