A considerable amount of digital data is being collected by State Highway Agencies (SHAs) to aid project-planning activities, support various project level decision-making processes, and effectively maintain and operate constructed highway assets. However, the highway construction industry has been significantly lagging behind utilizing the growing digital data to support business decisions compared to other industry sectors such as health care and energy. The significant lack of understanding on the linkage between raw data collected and various decisions, proper computational methodologies, and effective guidance is considered as major barriers to the full utilization of the digital data.

This study uses digital datasets that are now commonly available in SHAs, to demonstrate the smart utilization of existing digital data to support and enhance decision-making processes using data analytics and visualization methods. This study will a) develop an advanced computational methodology to generate multidimensional highway construction cost indexes (HCCIs) using two new concepts of i) dynamic item basket and ii) multidimensional HCCI, b) develop an enhanced framework for collection and utilization of digital Daily work Report (DWR) data, c) develop an automated methodology to generate as-built schedules using data collected from existing DWR systems, and d) analyze as-built schedules to develop a knowledge base of frequent precedence relationships of activities. The study achieves those objectives by utilizing three digital datasets: bid data, DWR data, and project characteristics data. Further, two standalone prototype systems, namely, Dyna-Mu-HCCI and ABSS are developed to automate computational methodologies for multidimensional HCCI calculation and as-built schedule development respectively.

This study will aid SHAs to utilize currently unused datasets for informed budgeting and project control decisions. It 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.