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Published in:
2025 | OriginalPaper | Chapter
Expanding IE Model Applications with Real-World Case Studies of Bridge Structures
Authors : Connor Kent, Connor O’Higgins, David Hester, Daniel S. Brennan, Zou Zhu, Su Taylor, Roger Woods
Published in: Dynamics of Civil Structures, Vol. 2
Publisher: Springer Nature Switzerland
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Abstract
Structural health monitoring (SHM) provides insights into the health of large civil structures, such as bridges, using data obtained by sensors. Population-based structural health monitoring (PBSHM) takes this a step further, allowing engineers to gain additional insights into structural health by incorporating the sensor data obtained from a population of similar structures instead of individual structures.
To enable the transfer of knowledge between structures, population similarity scoring metrics are being used where structures that have a high similarity will get a high similarity score. The similarity scoring is being achieved through the development of irreducible element (IE) models and graph neural networks (GNNs) in addition to other methods of generating similarity scores. Whilst an initial schema has been developed to facilitate the creation of IE models for various structures, further work needs to be undertaken in order to facilitate the rapid modelling of structures of greater complexity to enable real-world utilisation of PBSHM technology.
This chapter presents work that expands upon the current IE model schema to allow the IE models to more readily represent real-world bridges. Two bridges are investigated with the aim of examining their construction, including their geometrical options, e.g. identifying some of the standard section types commonly found in structures. Following these case studies, expansion recommendations are proposed to the schema related to the geometrical options with the aim of evolving the current version of the IE model schema so that a greater variety of structures, such as bridges or high-guided masts, can be modelled effectively by these IE models.