## 2 Theoretical Background
### Literature Review  
In addition to the parametric method, various technical approaches have been used in previous studies. Methods for project cost prediction and estimation across all industries can be largely categorized into intuitive methods, analogical methods (case-based reasoning, CBR), parametric methods, and analytical methods. Among these approaches, the parametric method and analogical method (CBR) are suitable for cost estimation in the early stage of a project [5]. In the intuitive methods, experts perform construction cost estimation based on their expertise and experience. These methods are generally used when information pertaining to the scope of the work is unclear, and the task involves qualitative and subjective estimation. When applying an analogical method, a solution to the current problem is obtained based on a similar past project. This approach is reliable when highly similar past project cases are available and experts have a high competence in estimation. In the parametric methods, a mathematical algorithm model that utilizes project characteristics--such as the unit area of a facility, number of parking spaces, and scale--is employed. A representative parametric method is regression analysis. Finally, the assembly method is commonly used in large-scale organizations that perform iterative and specialized construction projects, and it estimates the final product from detailed components [18]. As a result of reviewing previous studies on construction cost estimation conducted in South Korea and abroad, the cost estimation methods can be categorized into three approaches as follows: (1) methods based on unit price such as unit cost per square meter or unit cost per square foot [15; 22]; (2) probabilistic and statistical methods using multi-variate regression analysis and simulation [23; 24; 25]; (3) artificial intelligence or data mining-based methods such as genetic algorithm (GA), artificial neural network (ANN), or case-based reasoning (CBR) approaches [10; 19; 26]. In addition, techniques involving construction cost prediction based on the application of artificial intelligence and data mining through ANN, GA, CBR, and a hybrid model that combine the strengths of these methods, have recently mainly been used [19]. Despite the development of these technologies, ANNs may produce unintended results by repeated trial and errors that are interpreted as overfitting in the process of finding appropriate control variables. CBR has a disadvantage in that it requires a sufficient number of data, and the process of determining weights has not been clearly established yet [27]. In addition, in hybrid model-based methodologies including ANNs--despite their being able to perform quality analysis even when the number of data is not sufficient--the criteria for determining the number of hidden layers or the number of nodes in the hidden layer are not clear in the analysis process; thus, designing of a neural network model with excellent performance is highly challenging in practice [28]. Furthermore, the regression analysis-based approach that forms the foundation of probabilistic and statistical methods is the most widely and frequently used in construction cost estimation, where there is a linear correlation between the dependent variable and independent variables. This approach has the advantage of performing approximate construction cost estimation with relatively high accuracy, using limited simple input data such as construction project overview [29]. However, because there are numerous mathematical constraints and conditions, when there is not a sufficient number of datasets to resolve these constraints or conditions, or when the variety in the influencing factors of construction cost increases, construction cost estimation becomes more difficult [30]. Nevertheless, until now, the statistical regression-based approach has been widely used to explore the possibility of reliable solutions to new problems, as it is a highly potent method with excellent analysis and prediction capabilities [31].