Abstract
dc:description.abstractBridges are a vitally important part of modern infrastructure. Their condition needs to be monitored on a continuous basis in order to ensure their safety and functionality. Teams of engineers visually inspect more than half a million bridges per year in the US and the EU. There is clear evidence to suggest that they are not able to meet all bridge inspection guideline requirements. In addition, the format and storage of inspection reports varies considerably across authorities because of the lack of standardisation. The availability of a comprehensive and open digital representation of the data involved in and required for bridge inspection is an indispensable necessity for exploiting the full potential of modern digital technologies like big data exploration, artificial intelligence and database technologies. A thorough understanding of bridge inspection information requirements for reinforced concrete bridges is needed as basis for overcoming the stated problem. This work starts with a bridge inspection guideline analysis, from which an information model and a candidate binding to Industry Foundation Classes (IFC) is developed. The resulting bridge model can fully store inspection information in a standardised way which makes it easily shareable and comparable between users and standards. Then, two inspection stages for locating and classifying visual concrete defects are devised, implemented and benchmarked to support the bridge inspection process: In a first stage, healthy concrete surfaces are located and disregarded for further inspection. In a second hierarchical classification stage, each of the remaining potentially unhealthy surface areas is classified into a specific defect type in accordance with bridge inspection guidelines. The first stage achieves a search space reduction for a subsequent defect type classification of over 90% with a risk of missing a defect patch of less than 10%. The second stage identifies the correct defect type to a potentially unhealthy surface area with a probability of 85%. A prototypical implementation serves as a proof of concept. This work closes the gap between requirements arising from established inspection guidelines, the demand for holistic data models which has recently become known as "digital twin", and methods for automatically identifying and measuring specific defect classes on small scale images. It is of great significance for bridge inspectors, bridge owners and authorities as they now have more suitable data models at hand to store, view and manage maintenance information on bridges including defect location and defect types which are being retrieved automatically. With these developments, a foundation is available for a complete revision of bridge inspection processes on a modern, digital basis.
Degree
thesis:*- Name dc:type.qualificationname
- Doctor of Philosophy (PhD)
- Level dc:type.qualificationlevel
- Doctoral
- Grantor dc:publisher.institution
- University of Cambridge
- Year dc:date.issued
- 2019
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Huethwohl, Philipp Karl
- Advisor dc:contributor.advisor
-
- Brilakis, Ioannis
Subjects
dc:subject × 3Rights
dc:rightsIdentifiers
dc:identifier.*- Author Identifier
-
0000-0002-8766-5472
0000-0003-1829-2083 - OAI identifier oai:identifier
- oai:www.repository.cam.ac.uk:1810/285562