Performance evaluation of normalization-based CBR models for improving construction cost estimation

Abstract

Case-based reasoning (CBR) can be an effective approach to achieve reliable accuracy in cost estimation for construction projects, especially in the early design stages where only limited information is available. As CBR relies on historical data, it is important to perform data preprocessing to obtain high-quality of base cases. Normalization preprocessing gives all attributes standard scores so that they can be compared. This research examines the effects of normalization methods through performance evaluations of normalization-based CBR models to improve construction cost estimation in the early design stages. Multi-family housing complexes were used as case studies, and leave-one-out cross validation (LOOCV) was used for model validation. The perfor-mance of the CBR models was evaluated using the mean absolute error rate (MAER), mean squared deviation (MSD), mean absolute deviation (MAD), and standard deviation (SD) for accuracy and stability. The kernel density estimation (KDE) method was used to examine the appropriateness of the normalization methods. The results are expected to contribute to the enhancement of accuracy and stability of CBR-based cost estimation and to support decision-making. The suggested method could also be applied to other CBR areas such as energy prediction, noise management, bid decision-making, and scheduling, as well as other data-oriented methods, such as regression analysis and artificial neural networks.