Holistic Framework for Highway Construction Cost Index Development Based on Inconsistent Pay Items
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 7
Abstract
A construction cost index (CCI) measures the price changes of construction items over time. It allows owner agencies and contractors to monitor construction market fluctuations so that they can more accurately estimate construction costs and project long-term funding needs. However, various CCI calculation methods in the highway construction industry are limited in two respects: (1) inability to account for inconsistent pay items, primarily caused by changes in pay item catalogs, and (2) insufficiency in statistically cleaning pay item data, e.g., unbalanced bid prices. For this reason, the resulting highway construction cost index (HCCI) cannot accurately reflect the construction market. To address such limitations, this research develops a three-step holistic framework for automated HCCI development using inconsistent pay items. The three-step methodology encompasses (1) data cleaning, where a text analysis algorithm and outlier detection algorithms are developed to clean inconsistent pay items and unbalanced bid prices; (2) pay item sampling, which helps to select and edit pay items through various statistical analysis; and (3) HCCI calculation, where the chained Fisher index formula is applied to calculate HCCIs at the state level, as well as indexes for specific regions and item categories. A prototype application is developed to generate quarterly and annual HCCIs automatically using the Python programming language. Ten-year data, i.e., 251,033 records, were used to verify and validate the prototyped system. The resulting HCCI and sub-HCCIs provide reliable insights into construction market conditions with high granularity. This research contributes to the body of knowledge by offering a holistic framework for automated HCCI development that leverages the text analysis algorithm for accounting for inconsistent pay items and two-stage outlier treatments for cleaning pay item data.
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Data Availability Statement
Data analyzed during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The authors would like to thank the Michigan DOT (MDOT) for financial support (Grant File 2019-0313/Z3) and for providing all bid data, as well as personnel from MDOT for their advice. The authors would also like to thank Shengxian Tang at Western Michigan University for his support. This publication is disseminated in the interest of information exchange. MDOT expressly disclaims any liability, of any kind, or for any reason, that might otherwise arise out of any use of this publication or the information or data provided in the publication. MDOT further disclaims any responsibility for typographical errors or the accuracy of the information provided or contained within this information. MDOT makes no warranties or representations whatsoever regarding the quality, content, completeness, suitability, adequacy, sequence, accuracy, or timeliness of the information and data provided, or that the contents represent standards, specifications, or regulations.
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Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 7 • July 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Sep 4, 2020
Accepted: Jan 15, 2021
Published online: Apr 19, 2021
Published in print: Jul 1, 2021
Discussion open until: Sep 19, 2021
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