Data-driven insights on the knowledge gaps of conceptual cost estimation modeling

Although data modeling methods for conceptual cost estimation are proven to be effective in academia, they are not adopted by construction practitioners as expected. To understand this fact and find solutions to the challenge of implementing modeling methods, a review of the modeling process is needed. Fifty-one most relevant studies were filtered out from the Web of Science and ASCE. Referencing two established data mining frameworks, namely, CRISP-DM and KDD, this paper identifies the key tasks of implementing conceptual cost estimation models. The results of reviewing key tasks show that the literature did not provide sufficient solutions to data preparation and model evaluation. Critical judgments on the accomplishment and deficiencies of the current conceptual cost estimation studies, from the perspective of data modeling process for the first time, is the main contribution of this paper. Other contributions include the elaboration of the body of knowledge to guide practitioners to implement advanced cost estimation, as well as recommendations on future studies of improving data quality and integration with data management systems to achieve the data models' best capacity.