Forensic analysis of DWR data for effective prediction of highway production rates

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Devaguptapu, Vijay
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Hyungseok D. Jeong
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Civil, Construction, and Environmental Engineering

Departments of Transportation (DOTs) are collecting a vast amount of digital data to support project-planning, crucial decisions like contract time, and effectively document progress of highway construction activities. Analyzing the digital data in highway construction industry supplements and reinforces managerial and business decisions.

This study uses Daily work report data (DWR data) that are now commonly available in all State DOTs to demonstrate the smart utilization of existing digital data to support and enhance decision-making processes using data analytics and visualization methods. This study aims at providing an estimation model for transportation agencies to quickly estimate production rates based on bid data, DWR data, contractor and equipment data. In addition, the study identified important factors to the production rate of major work items. The study also examined the performance of different categories of contractors. The data used for this study was obtained from Montana DOT. The data was cleaned before being utilized to shortlist thirty-five key controlling activities important to highway construction. The final dataset was used to develop a model that can predict dynamic production rates according to project specific parameters. The scope of the study also includes developing a dynamic production rate estimation tool that would predict production rates depending on project characteristics as well as parameters involving contractors.

This study will enable State DOTs to utilize the existing datasets for contractor evaluation. The study is also expected to enhance professionals’ understanding of production rates achieved in the past by contractors. The study demonstrates the importance of data analytics and visualization to obtain more value from the investment made in collecting construction data. Overall, this study serves as a step in making a transition from experience-driven to data-driven decision making in the construction industry.

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Tue May 01 00:00:00 UTC 2018