Machine learning informed knowledge driven framework supporting holistic bridge maintenance
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Date
2021
Authors
Jiang, Yali
Yang, Gang
Li, Haijiang
Zhao, Kai
Zhang, Tian
Journal Title
Journal ISSN
Volume Title
Publisher
Published Version
Abstract
Bridge maintenance is a complex task, which demands a wide spectrum of factors to achieve multi-objectives, multi-criteria optimum decisions. Physics-informed analysis can simulate complex and closely coupled problems, e.g., bridge structural analysis. However, it cannot account for some loosely coupled discrete factors, which complementarily could be addressed by ontological based semantic inference. This paper presents an overarching machine learning (ML) informed knowledge driven framework, which can enhance existing and static knowledge base via dynamically linking to real-time ML information for bridge structural safety as the governing consideration, to make accurate and holistic maintenance decisions. The framework includes semantic modelling, ML based numerical modelling and the Web OWL based reasoning mechanism for integration. The approach could contribute to some fundamental mindset changes towardsmaintenance decision making.
Description
Keywords
Bridge maintenance , Knowledge engineering , Ontology , Machine learning
Citation
Jiang, Y., Yang, G., Li, H., Zhao, K. and Zhang, T. (2021) 'Machine learning informed knowledge driven framework supporting holistic bridge maintenance', 28th EG-ICE International Workshop, 30 June - 2 July 2021, Hybrid.
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Copyright
© 2021, the Authors.