Date of Award
Dissertation - Restricted
Doctor of Philosophy (PhD)
Civil, Construction, and Environmental Engineering
Construction companies are vast silos of information and data. The companies utilize this data for a host of processes within their organization. The ability to share this data and efficiently query across the variety of data formats and software platforms is still a complex problem that many construction companies are trying to unravel. The data can be in paper or digital forms. There is a tremendous amount of data that is accumulated and stored in the typical business enterprise of all construction companies. Estimates, construction schedules, marketing information, human resources, managerial decision-making, and a host of other functions rely on the accurate exchange of data for planning and forecasting business functions. The problem that exists is that much of this data is stored in multiple places in various formats and cannot be accessed easily when needed. This leaves a tremendous amount of information duplication and manual re-entry. It also limits the ability to query the information for use in decision analysis and forecasting. Construction operations is a key business function for any construction company and is central to completion of projects. An analysis of this function was performed for the purpose of identifying the data generated and development of data models for manipulation and storage of field data used in the process of estimating. A critical business process for a construction company is estimating. Estimating relies on experience and knowledge of past practices. The understanding and definition of the data required to produce estimates is important. This research will also explore the requirements for the estimating process and data collection and will examine how this data should be efficiently structured in a historical cost database to improve access, minimize paper forms, and be available for future estimating purposes.