Data analytics and visualization for enhanced highway construction cost indexes and as-built schedules

dc.contributor.advisor H. David Jeong Shrestha, Krishna
dc.contributor.department Civil, Construction, and Environmental Engineering 2018-08-11T13:31:02.000 2020-06-30T03:06:36Z 2020-06-30T03:06:36Z Fri Jan 01 00:00:00 UTC 2016 2001-01-01 2016-01-01
dc.description.abstract <p>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.</p> <p>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.</p> <p>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.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 6817
dc.identifier.contextkey 11165371
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/15810
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 20:47:04 UTC 2022
dc.subject.disciplines Civil Engineering
dc.subject.keywords Bid data analysis
dc.subject.keywords Big data analytics
dc.subject.keywords Daily Work Report
dc.subject.keywords Highway Construction Cost Index
dc.subject.keywords Schedule
dc.subject.keywords Sequential Pattern Mining
dc.title Data analytics and visualization for enhanced highway construction cost indexes and as-built schedules
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication Civil Engineering dissertation Doctor of Philosophy
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