# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 2 Literature Review  
The objective of this paper is to review and synthesize prior research on construction cost prediction. The paper specifically focuses on identifying the direction and method of research related for predicting construction costs in response to changes in design content during the 'Schematic Design' stage of the architectural design process. Through the review of research cases, it is expected that the most effective and appropriate approach to predict construction costs according to changes in design contents in the 'Schematic Design' stage will be derived. In order to improve the accuracy of the estimate, [PERSON] et al. [2] analyzed the performance data of a high-rise office building project and presented a model for calculating construction closing costs based on the Object and Parameter based Schematic Estimation Model (OPSEM). In the study, necessary activities were defined based on the properties of the object, and construction costs were calculated by applying construction costs to these tasks. [PERSON] et al. [3] analyzed the efficiency of BIM estimation by comparing the working time in the 2D-based cost estimate and the BIM-based cost estimate for the same project. [PERSON] [4] developed a model for predicting asthma occurrence based on environmental data and frequency of asthma occurrence, and trained a deep learning model, showing higher accuracy than the existing model. [PERSON] et al. [5] developed a deep learning model to predict housing prices, in which features were extracted from input data by combining convolutional neural network (CNN) and long short-term memory (LSTM) networks, and predictive performance was improved using a joint self-attention mechanism. [PERSON] and [PERSON] [6] proposed an improved approximate cost prediction model for the 'Pre-Design' stage by identifying the optimal combination of influencing factors using regression analysis and the Pearson correlation coefficient. They found that the accuracy of the prediction model was significantly improved when using 'total area' and 'building area' as influencing factors. The study utilized the Pearson correlation coefficient to determine the degree of correlation between the influencing factors and construction costs. [PERSON] et al. [7] conducted a study on predicting construction costs for 'Smart Education Facilities' in the 'Schematic Design' stage, where the 'Unit cost of construction' technique was found to be inadequate due to the small construction samples involved. They used multiple regression analysis, DNN, and DBN models to predict construction costs, and compared and analyzed the error rates of each model. To ensure the reliability of the results, they employed the 'k-fold' technique. The study found that using the DBN model could reduce the problem of overfitting that could occur when less data are used. In the study, the authors used variables such as 'Total Area', 'Building Area', 'Land Area', 'Underground Floor Area', 'Order Method', 'School Classification', 'Number of Classrooms', and 'Construction Period'. However, in this study, DNN was used as an artificial intelligence model to check the possibility of using BIM properties, and the 'construction period' variable was excluded from the analysis as it was difficult to specify in the 'Schematic Design'. [PERSON] et al. [8] developed a construction cost prediction model for public apartment buildings in the 'Schematic Design' stage. They used regression analysis and selected factors that influence construction costs, such as 'Total Area', 'Building Area', 'Land Area', 'Construction Area', 'Ground Floor Area', 'Underground Floor Area', and 'Ground floor'. They analyzed the 'Pearson correlation coefficient' for 'Total Construction Cost' and found that variables such as 'Building Area', 'Ground Floor Area', 'Number of Households per Floor', 'Total Household', 'Perimeter of the Building', 'Exterior Finish Area', 'Land Area', 'Parking Lot Area', 'Underground Parking lot Floor', and 'Number of Parking spaces' had an overall coefficient higher than 0.9.