Jimenez-Roa, Lisandro Arturo (2020) Data-driven damage detection for bridges through vibration structural health monitoring. EngD thesis, University of Twente, Netherlands.
Abstract
Bridge structures are essential for social and economic development, but they are costly to maintain. More efficient planning and better investment of resources are needed. To this end, Structural Health Monitoring (SHM) techniques are used to collect data on the behavior of the bridge in terms of engineering and environmental variables to support decision making. However, without predefined objectives for the monitoring campaign, it often results in large, unwieldy databases from which little or no value can be derived. To tackle this problem, this PDEng project focuses on a methodology that translates vibration global SHM data into a damage indicator. To this end, (i) two types of damage sensitive features obtained from the vibration data were thoroughly explored; (ii) a process based on Principal Component Analysis (PCA) was used to address the high dimensionality space of the data. Besides, an approach to calibrate the reference period based on the PCA was proposed; (iii) a one-class support vector machine to perform damage detection using damage sensitive features was implemented; and (iv) the validation was carried out based on two case studies, the Z24 bridge, and the SMC bridge benchmarks. In particular, for the latter, anomalies were detected with respect to the reference period four months before the closing of the bridge when the damage was found through on-site inspection.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Halman, Johannes Innocentius Maria; Hartmann, Andreas; Stoelinga, Mariëlle I A; Wille, Sjoerd and Fennis, Sonja |
| Uncontrolled Keywords: | bridge monitoring; structural health monitoring; damage detection techniques; vibration monitoring systems; data driven model; bridge management strategies; machine learning; data analytics |
| Index terms: | validation, methodology, decision-making, vibration, machine learning, economic development, principal component analysis, monitoring, case study, management strategy, database, inspection |
| Subjects: | quality assurance, research methods, economic development, artificial intelligence, control systems, mechanical systems, data collection methods, statistical analysis, decision analysis, data management, professional development, management |
| Topics: | Digital Applications, Site Management, Business Strategy, Research Practice, Information Management, Quality Management, Engineering Principles, Risk Management, Sustainability |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here