## 1 Introduction
### Scope and Methods  
The scope of this research is as follows: Based on a construction cost dataset developed for 907 cases of public office building projects with governmental agencies as the owners from 2015 to 2021 published by the Korean Public Procurement Service, we developed a construction cost estimation model for use in the architectural planning stage and validated the performance of this model through BIM. The reasons for using the construction cost data of public buildings ordered by public agencies in the prediction model are as follows. First, the data are easily accessible because the public building construction cost data collected by the Korean Public Procurement Service are publicly available. Second, public building construction cost data have been updated every year since 2015, facilitating convenient addition of datasets later. Third, public building construction cost data are larger in volume than private building construction cost data. Moreover, the reliability of construction cost data can be secured because the data are provided by public agencies. An outline of the dataset constructed and used in this study is presented in Table 1. The dataset includes 45 types of data fields in addition to the total construction costs of 907 construction projects and is composed based on the numbers collected and totaled after completion of the construction projects. The items in each data field include the area information, the number of floors and number of parking spaces related to the construction project, and the construction cost for each work type calculated during project execution. Among these, we utilized the data fields that could be factored into the architectural planning stage to develop the construction cost estimation model. We aimed to build a construction cost prediction model applying a parametric method, considering a wide range of parameters, as well as to perform model validation through BIM. The research methods adopted in this study to achieve the aforementioned objectives are discussed below. Firstly, the existing literature on the background and concepts related to the development of a construction cost estimation model is reviewed. In particular, we focus on literature pertaining to cost estimation with the application of a parametric method and selection of appropriate parameters required for modeling. Secondly, we employed data preprocessing and parameter selection to develop the cost prediction model. In this process, we utilized the scikit-learn and Pandas libraries, which are commonly used for data processing. In addition, for parameter selection, we performed correlation coefficient analysis and considered the LOD of the planning-stage BIM. Thirdly, we established a process for construction cost estimation in the early phase of a construction project based on BIM, assuming the architectural planning stage. Accordingly, the adequacy and conformity of the developed construction cost estimation model were tested and verified based on the standards presented by the American Association of Cost Engineering (AACE) International. Figure 1 illustrates the procedure for building the architectural planning stage construction cost estimation model based on the research methods outlined above. Firstly,