A Correlation Network Model for Structural Health Monitoring and Analyzing Safety Issues in Civil Infrastructures

Date
2017-01-04
Authors
Fuchsberger, Alexander
Ali, Hesham
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Structural Health monitoring (SHM) is essential to analyze safety issues in civil infrastructures and bridges. With the recent advancements in sensor technology, SHM is moving from the occasional or periodic maintenance checks to continuous monitoring. While each technique, whether it is utilizing assessment or sensors, has their advantages and disadvantages, we propose a method to predict infrastructure health based on representing data streams from multiple sources into a graph model that is more scaleable, flexible and efficient than relational or unstructured databases. The proposed approach is centered on the idea of intelligently determining similarities among various structures based on population analysis that can then be visualized and carefully studied. If some “unhealthy” structures are identified through assessments or sensor readings, the model is capable of finding additional structures with similar parameters that need to be carefully inspected. This can save time, cost and effort in inspection cycles, provide increased readiness, help to prioritize inspections, and in general lead to safer, more reliable infrastructures.
Description
Keywords
Civil Infrastructures, Correlation Network, Dynamic Graph Clustering, Prediction Model, Structural Health Monitoring
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 50th Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.