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Physics-informed knowledge-driven decision-making framework for holistic bridge maintenance

Jiang, Yali, Yang, Gang, Li, Haijiang ORCID:, Zhang, Tian and Khudhair, Ali ORCID: 2024. Physics-informed knowledge-driven decision-making framework for holistic bridge maintenance. Journal of Construction Engineering and Management 150 (9) , 0402410. 10.1061/JCEMD4.COENG-13593

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Bridge maintenance is a highly intricate task that involves considering a wide range of factors in order to achieve optimal decisions that align with multiple objectives, criteria, and the entire lifecycle of the bridge. While physics-informed analysis, such as the finite element method (FEM), can simulate complex and closely coupled scenarios, such as bridge structural analysis, it cannot account for some loosely coupled discrete factors, which could be addressed by ontological reasoning. Therefore, this paper presents a knowledge-driven decision-making framework that combines static knowledge reasoning with dynamic FEM analysis results to support holistic bridge maintenance decisions. One significant contribution of this research is the development of a comprehensive bridge maintenance ontology that incorporates knowledge derived from bridge maintenance standards. Another key contribution is the ability to employ complex runtime rules-based reasoning to tackle intricate bridge maintenance scenarios. To enable automatic knowledge-driven reasoning, an integrated workflow is developed to orchestrate semantic modeling with numerical modeling through a Python-based Web Ontology Language application programming interface (OWL API). This integration facilitates the efficient orchestration of the framework. A case study is presented to demonstrate the potential for the developed framework in assisting with the complex holistic decisions required for bridge maintenance.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: American Society of Civil Engineers
ISSN: 0733-9364
Date of First Compliant Deposit: 24 April 2024
Date of Acceptance: 21 March 2024
Last Modified: 10 Jul 2024 00:32

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