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Diagnostic Structural Health Assessment Through Layered Integration of Non-destructive Imaging Data

  • Author(s): Hess, Michael Robert
  • Advisor(s): Kuester, Falko
  • et al.
Abstract

Much of the built infrastructure that exists today has outlived its designed life and the unknown health of these structures poses safety and life-time maintenance concerns. The lack of reliable understanding as to a structure’s current state of health, oftentimes results in the expensive demolition and replacement of entire structures. If professionals were instead equipped with a comprehensive structural health assessment that provides actionable data describing an existing structure, they could then make more informed decisions with potential for less expensive remedies. For historical structures the need for structural assessment methods is even more critical, because replacement is not an option due to their economic, social and cultural impacts.

This dissertation presents methods to non-destructively acquire data describing the existing state of a given structure to inform future analyses and structural health assessments. The first objective is to identify the types of information that should be documented, and the techniques utilized to acquire those data. Emphasis here is placed on collecting information pertaining to geometry (at the surface, subsurface and volumetric levels), appearance, and context. These data are then integrated into a holistic digital model, or an as-built information model, to enhance interpretation and understanding by correlating results from different modalities in time and space.

The culmination of this dissertation is a repeatable methodology for the creation of as-built information models which ultimately serve as a data repository and the core hub for processing, analysis, dissemination and decision-making processes. Professionals and stakeholders will make more informed decisions if equipped with a reliable digital documentation record that captures a structure’s history, flaws and damages both at the surface and internally. These sources are then used to inform diagnostic assessments of the structure’s state of health as well as informing future predictive analyses and lifecycle maintenance. Though the initial scope highlights applications for historical structures, the developed methods for assessment and analysis can be extended to any existing structure at any scale. The ability to generate these models efficiently can greatly improve the investigation and maintenance of built infrastructure all over the world.

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