Data-Driven Insights on the Knowledge Gaps of Conceptual Cost Estimation Modeling
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 2
Abstract
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.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
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Journal of Construction Engineering and Management
Volume 147 • Issue 2 • February 2021
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© 2020 American Society of Civil Engineers.
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Received: Oct 1, 2019
Accepted: Jun 8, 2020
Published online: Nov 28, 2020
Published in print: Feb 1, 2021
Discussion open until: Apr 28, 2021
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