| description abstract | This study presents a method for estimating construction costs, even when dealing with limited and unreliable data, to enhance decision-making in the early project stages. Owners, particularly in green building projects, often face challenges due to the scarcity of usable data, making cost estimation a complex task. They struggle to differentiate between costs associated with existing buildings and green buildings. To address this issue, we introduce a novel approach that leverages conditional tabular generative adversarial networks (CTGANs) for data augmentation, overcoming the limitations of relying solely on historical data. This involves training an artificial neural network (ANN)–based model using synthetic data, effectively addressing the scarcity and imbalance present in the original small data set. Compared to models trained exclusively on the original data set, our approach yielded a remarkable reduction of approximately 66% in root-mean-square error (RMSE), while increasing the validity from 0% to 15.09%. This study not only improves construction cost estimation but also facilitates more informed decision-making for owners, even in cases with limited and unreliable data, ultimately contributing to the efficiency of the construction project planning process. | |
