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The importance of accurate construction cost estimation and the need to consider market indices in cost estimation models. The document also outlines the methodology of a thesis aimed at identifying key factors affecting cost estimation for building projects in the uae. The findings will benefit stakeholders by enabling them to determine parameters that affect cost estimation and prevent project cost overruns.
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Investigation of Factors Impacting Construction Cost Estimate to Develop Construction- Driven Artificial Neural Network (ANN) by Salem Al Saber A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science Approved July 2023 by the Graduate Supervisory Committee: Kenneth Sullivan, Chair Richard Standage Kristen Hurtado ARIZONA STATE UNIVERSITY August 2023
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Construction industry is the backbone of any country’s economy. It is a primary source of foreign investments, creates new jobs, and maintains the economy flowing in various trades. Accurate cost estimation is a critical aspect for the construction industry, directly impacting project success and profitability. This master's thesis focuses on comprehensively identifying the key factors that influence cost estimation and provides valuable recommendations for constructing an optimized Artificial Neural Network (ANN) model. Through an extensive research methodology encompassing literature review, surveys, and interviews with industry professionals, this study uncovers significant factors that exert a substantial impact on cost estimation practices. The findings emphasize the importance of seamlessly integrating project delivery systems, meticulously considering project duration, and incorporating diverse perspectives from global regions. By incorporating these insights, stakeholders can make informed decisions, enhance project planning, and elevate overall project performance. This study successfully bridges the gap between theory and practice, presenting invaluable insights for stakeholders within the construction industry. Keywords: cost estimation, construction industry, Artificial Neural Network, factors, project delivery systems, project duration, global perspectives, informed decision-making, project planning, project performance
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I would like to express my sincere gratitude to all those who have contributed to the completion of this thesis. First and foremost, I extend my heartfelt appreciation to my advisor, Dr. Sullivan, for his guidance, expertise, and unwavering support throughout this research journey. His invaluable insights, constructive feedback, and dedication to my academic development have been instrumental in shaping this thesis. I am truly grateful for the opportunity to work under his mentorship. I am also deeply grateful to the faculty and staff at Arizona State University for providing a stimulating academic environment and resources that have nurtured my intellectual growth. I extend my thanks to the faculty members in the Ira Fulton School of Engineering for their knowledge, guidance, and commitment to excellence in education. I would like to express my appreciation to my family for their unwavering support, love, and encouragement throughout my academic journey. I am especially grateful to my parents for instilling in me the values of perseverance, dedication, and the pursuit of knowledge. Finally, I would like to express my deepest appreciation to all the individuals who have contributed to this thesis in any way, whether through their encouragement, feedback, or constructive criticism. Specifically, I would like to thank all the people who assisted and supported throughout the data collection process. Thank you all for your contributions and for being a part of this important milestone in my academic and personal growth. Your involvement and support have been invaluable.
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Page LIST OF TABLES .................................................................................................................... v LIST OF FIGURES .................................................................................................................vi LIST OF ABBREVIATIONS .................................................................................................. x CHAPTER
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Figure Page Figure 1: Project Costs Classification ............................................................................... 17 Figure 2: The Structure of a Construction Company’s Overhead Costs. Extracted From (Apanavičienė & Daugėlienė, 2011)................................................................................. 20 Figure 3: Schematic Diagram of a Neuron. Extracted From (Najafi & Tiong, 2015) ...... 33 Figure 4: The Structure of Artificial Neural Network. Extracted From (Jinisha & Jothi,
vii Figure Page Figure 14: Recurrent Networks Diagram. Extracted From (Najafi & Tiong, 2015) ........ 44 Figure 15: Research Design. ............................................................................................. 63 Figure 16: Work Sector Distribution. (Total Sample of 87) ............................................. 68 Figure 17: Population Positions in the Construction Field. (Total Sample of 87) ............ 69 Figure 18: Population Years of Experience in the Construction Field by Market. (Total Sample of 87) .................................................................................................................... 70 Figure 19: Percentage Rating If Inaccurate Estimation Can Be Identified As Reason for Cost Overruns in Construction Projects (Total Sample of 87). ........................................ 71 Figure 20: Population Rating Percentage of Projects With Cost Overruns Issues Due to Inaccurate Estimation (Total Sample of 87). .................................................................... 71 Figure 21: Population Rating Impact of Type of Foundation Factor on Cost Estimation (Total Sample of 87). ........................................................................................................ 73 Figure 22: Population Rating Impact of Type of Building Structure Material Factor on Cost Estimation (Total Sample of 87). ...................................................................................... 74 Figure 23: Population Rating Impact of Floor Area Factor on Cost Estimation (Total Sample of 87). ................................................................................................................... 75 Figure 24: Population Rating Impact of Number of Floors Factor on Cost Estimation (Total Sample of 87). ................................................................................................................... 76 Figure 25: Population Rating Impact of Type of Slab Factor on Cost Estimation (Total Sample of 87). ................................................................................................................... 77 Figure 26: Population Rating Impact of Number of Staircases Factor on Cost Estimation (Total Sample of 87). ........................................................................................................ 77
ix Figure Page Figure 38: Population Rating Impact of Weather on Cost Estimation (Total Sample of 87). ........................................................................................................................................... 87 Figure 39: Population Rating Impact of Management Factor on Cost Estimation (Total Sample of 87). ................................................................................................................... 88 Figure 40: Population Rating Impact of Availability of Labor Factor on Cost Estimation (Total Sample of 87). ........................................................................................................ 89 Figure 41: Population Rating Impact of Type of Client Factor on Cost Estimation (Total Sample of 87). ................................................................................................................... 90 Figure 42: Population Rating Impact of Market Trend Factor on Cost Estimation (Total Sample of 87). ................................................................................................................... 90 Figure 43: Population Rating Impact of External Social Conditions Factor on Cost Estimation (Total Sample of 87). ...................................................................................... 91 Figure 44: Population Rating Impact of Project Sustainability Level on Cost Estimation (Total Sample of 87). ........................................................................................................ 92 Figure 45: Population Rating Impact of Safety Requirements Factor on Cost Estimation (Total Sample of 87). ........................................................................................................ 93 Figure 46: Population Rating Impact of Labor Union Availability on Cost Estimation (Total Sample of 87). ........................................................................................................ 94
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ANN - Artificial Neural Network BIM - Building Information Modeling UAE - United Arab Emirates MLP - Multi-Layer Perceptron RNN - Recurrent Neural Network PMI - Project Management Institute AACE - Association for the Advancement of Cost Engineering EVM - Earned Value Management CBR – Case Base Resonance MAE – Mean Absolute Error MSE – Mean Squared Error SGD – Stochastic Gradient Descent MLP- Multilayer Perceptron
Wang et al. (2022) outlines that there is a general consensus in Hong Kong project cost estimation models where insufficient consideration is given to incorporating the trend and value of market indices in construction cost estimation, such as stock market index, construction indices, and daily wages. This oversight may contribute to significant disparities between the actual final cost and the initial construction projects estimates, despite the utilization of advanced estimation approaches by stakeholders and contractors. Incorporating the trend and value of these market indices into construction cost estimation is crucial to address the issue and improve the accuracy of cost projections (Wang et al., 2022). Furthermore, the accuracy of cost estimates can vary among estimators due to differences in their levels of experience (Matel et al., 2019). Acquiring the expertise needed to perform cost estimation is a time-intensive endeavor for professional engineers, often taking several years to develop the necessary skills. Moreover, this expertise is frequently not documented or formally validated, making it susceptible to subjectivity and variability (Elfaki et al., 2014). According to Juszczyk et al. (2018), a project failure maybe caused by both underestimation and overestimation. Underestimating the cost of a construction project often results in cost overruns, leading to financial losses for stakeholders (Wang et al., 2022). Additionally, Elfaki et al. (2014) outlined that inaccurate cost estimation can result in various problems, including change orders, construction delays, and even bankruptcy. In contrast, trained neural networks can leverage their knowledge and adaptability to produce accurate cost estimates within a shorter timeframe leading to more reliable decision-making in project management. This advantage is particularly valuable when
limited project information is available (Matel et al., 2019). The adoption of advanced computational methods has become indispensable in addressing the complexities and challenges encountered in construction projects. These challenges serve as strong motivation for the application of intelligent techniques to effectively manage them. Intelligent techniques can be employed to address various challenges, including selecting the most suitable prime contractor, predicting project performance at different stages, and estimating the risk of cost overruns. In the field of civil engineering, Artificial Intelligence techniques are increasingly recognized as a valuable approach to tackle problem areas characterized by uncertainty and ambiguous definition (Elfaki et al., 2014). Problem Statement Cost overruns pose a significant challenge, particularly in the current landscape of stringent budget constraints. Such overruns can have severe consequences, including the potential cancellation of the project (Elmousalami, 2020). During the tendering phase, project details and information are limited, whereas traditional estimation methods rely heavily on detailed information, resulting estimators to use their expertise and experience to make intuitive judgments. This approach can be time- consuming and costly (Matel et al., 2019). Furthermore, such qualitative approaches that rely on expert judgments may produce bias and yield inaccurate estimations. Many models have been proposed for construction cost estimation. However, various models primarily focus on project characteristics and overlook external economic factors (Wang et al., 2022). This outlines that traditional estimation techniques using judgement and experience are no longer effective compared to Artificial Neural Network (ANN) models. Factors
determine parameters that affect cost estimation and can cause project cost overruns. The ultimate goal of this study is to prepare input parameters that will enable to provide valuable insights for future development of ANN models in the field of construction. The Main objectives of this thesis can be defined under the two points below:
potential challenges associated with ANNs. The chapter concludes by depicting applications review of ANN in the construction field in recent years. Chapter 3: Research Methodology The third chapter describes the methodology adopted for developing the thesis, including the process of acquiring data on key factors affecting cost estimating in building projects. It then briefs the tools for data development. Chapter 4: Data Results In this chapter, survey results statistical analysis is presented. Then, tools for data development used during study are discussed to confirm the data reliability. Chapter 5 : Conclusion and Recommendations The final chapter summarizes the conclusions drawn from the study and provides recommendations for the next phase in which an ANN model will be build and cost estimation model at early stage will be the end result. It also outlines areas for further research and development.
