A Framework for Evaluating Cost Performance of Architectural Projects Using Unstructured Data and Random Forest Model Focusing on Korean Cases

Cost is a key performance indicator for evaluating the success of architectural construction projects. While previous studies have relied on quantitative data and statistical models to evaluate cost performance, recent advancements in methods have enabled analysis using unstructured data. Unstructured data, particularly in construction supervision reports, can be considered the significant variables for performance evaluation, as they represent independent third-party monitoring of the construction project’s execution. This study aims to present a framework that supports cost performance evaluation using unstructured data and random forests (RFs), a representative method of machine learning. Specifically, association rule analysis and social network analysis were used to identify the main keywords, and an RF model was applied to these data to evaluate cost performance. The tuning of hyper-parameters in the RF was implemented by the Bayesian optimization technique with the augmentation of the original dataset. The accuracy of cost performance evaluation was 59% for the traditional logistic regression (LR), 74% for the regularization-based logistic regression (BLR) designed to prevent overfitting, and 76% for the RF model utilizing augmented data. The complementary utility of the models consisting of the proposed framework can be useful for deriving various evaluation explanations about cost performance. The applicability is expected to increase as more data become available in the future.