Context-aware information access and retrieval for rapid on-site decision making in construction, inspection and maintenance of constructed facilities

The spatial expanse and the dynamic and information intensive nature of typical engineering projects, in particular construction projects, require mobile users (engineers, managers, inspectors etc.) to have rapid and real-time access to a wealth of project information. However, during the course of their normal activities, field personnel typically spend a significant amount of time in manually accessing relevant information, thereby amounting to lost productivity and increased costs.;In an effort to remedy this situation, this dissertation takes the first steps and presents research that investigated methods for high-precision identification and automated retrieval of contextual information in mobile construction engineering applications. The primary contribution of the presented work is the design and implementation of a dynamic user-viewpoint tracking scheme in which mobile users' spatial context is defined not only by their position but also by their Three Degree-of-Freedom (3DOF) head orientation (i.e. line of sight). This allows the identification of objects and artifacts visible in a mobile user's field of view with much higher accuracy than was possible by tracking position alone.;Within this scheme, location-aware computing and head orientation sensing technologies were integrated. For outdoor applications, the Global Positioning System (GPS) and magnetic orientation tracker were used to track a user's dynamic viewpoint. For indoor applications, this research explored the applicability of wireless technologies, namely Wireless Local Area Networks (WLAN), Ultra-Wide Band (UWB), and Indoor GPS for dynamic user position tracking in situations where GPS is unavailable. The same orientation device was used indoors. Additionally, this research evaluated the capability of interoperable product models and project databases, such as the Computer Integrated Manufacturing for steel structures (CIMsteel) Integration Standards (CIS/2) product model and Microsoft Access databases for automated context-aware information access and retrieval.;The developed methods and the components of the proposed context-sensing information retrieval methodology have been validated through several experiments conducted at the University of Michigan, the National Institute of Standards and Technology (NIST), and the Disaster City urban search and rescue testbed in Texas.