Real-Time Context-Aware Computing with Applications in Civil Infrastructure Systems.

This dissertation contributes a structured understanding of the fundamental processes involved in developing context-aware computing applications for the civil infrastructure industry. The civil infrastructure industry is characterized by mobile human and machine agents actively engaged in real-time decision-making tasks in a dynamic and unstructured workspace environment. This distinguishes context-aware computing from other computing technologies in three aspects: 1) it has the ability to perceive, interpret, and adapt to the agent’s evolving workspace; 2) It streamlines project data and presents the agent with information pertinent to its context, thus eliminating the agent’s tasks to accomplish the same; 3) By leveraging contextual information, it supplements decision-making tasks in real-time. This research has successfully investigated technical approaches to address fundamental aspects of introducing context-aware applications to civil engineering, including: the ubiquitous localization of mobile agents in dynamic, unstructured environments; abstraction of the spatial-context and identifying the objects of interest to the agent; and the suitability of using standard models to manage and organize data for context-aware computing applications. A computational framework for designing context-aware applications to support real-time decision-making has also been implemented. The framework allows researchers and other end users to leverage currently available context-sensing technology to design and implement innovative solutions to domain specific problems. The researched methods have been validated through several experiments conducted at the University of Michigan, the National Institute of Standards and Technology, and the Michigan Department of Transportation. These experiments have resulted in the implementation of several applications – to support real-life decision-making tasks – that not only serve to illustrate the usefulness of the framework, but also have significant social and economic implications. Among these applications are the controlled drilling system that warns drilling personnel when the drill bit tip is about to strike rebar or utility lines, thus helping preserve the structural integrity of concrete decks and preventing utility strike accidents; an automated fault detection system that diagnoses faulty components of an underperforming HVAC distribution network; and an innovative bridge inspection solution that supports condition assessment decision-making, thus introducing objectivity to visual condition assessment by providing concurrence with the Structural Health Monitoring data.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99816/1/akulaman_1.pd

Leveraging Structural Health Monitoring for Bridge Condition Assessment

ABSTRACT Highway bridge infrastructure is a key aspect of the US transportation system. Efficient maintenance is important for preserving the integrity and improving the condition of aging national bridge infrastructure. Highway bridge condition is typically assessed using visual inspections and accompanying non-destructive field tests. This paper presents a solution that leverages low-cost Structural Health Monitoring (SHM) sensor data and the cumulative knowledge of bridge inspectors to monitor bridge condition. The developed solution utilizes a central database to store infrastructure inventory, inspection, and SHM information. The paper presents the Bridge Inspection Toolkit (BIT)-an innovative software solution that provides condition assessment decision-making support to bridge inspection documentation. Condition assessment support is provided by integrating the BIT with functionality that allows inspectors access to intelligent interpretation of SHM data. The BIT also allows inspectors access to condition assessment data corresponding to equivalent components, recorded by other responders, thus allowing them to draw on the collective experience and judgment of peers. The implementation of the inspection solution developed in this research introduces a greater degree of objectivity into condition ratings assessed by bridge inspectors. Condition assessment data gathered using the presented methods helps in effectively rating bridge infrastructure capacity and efficiently allocating maintenance resources