Date of Award:

12-2018

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Marc Maguire

Committee

Paul Barr

Committee

Marvin W. Halling

Committee

Yan Sun

Committee

Calvin Coopmans

Abstract

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections.

Checksum

a8242e6b92ec78cba38e6ad9fcf7732d

Share

COinS