Knowledge driven approach for smart bridge maintenance using big data mining

Jiang, Yali, Yang, Gang, Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133 and Zhang, Tian 2022. Knowledge driven approach for smart bridge maintenance using big data mining. Automation in Construction 146 , 104673. 10.1016/j.autcon.2022.104673

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Abstract

Life cycle bridge maintenance is highly complex and multi-disciplinary oriented. Advanced technologies have been widely adopted, but the generated data and information are often intensive, specific and isolated, it is very difficult to contribute effectively for holistic bridge maintenance decisions. This paper investigates state-of-the-art methods used in bridge maintenance, a total of 2732 papers were selected for visualisation analysis and 323 papers were pinpointed for critical review. The review informs that mindset shifting from traditional and pre-digital, through data driven to knowledge-based approach is required for bridge engineers to holistically understand multi-sources of data and information to enable systematic thinking. The review further reveals the need for a knowledge-driven approach that can leverage bridge maintenance big data to provide smart holistic decisions, a novel knowledge-oriented framework was proposed in the end with an aim to unify and streamline different sources of data to facilitate new developments towards smart bridge maintenance.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0926-5805
Date of First Compliant Deposit:	25 November 2022
Date of Acceptance:	15 November 2022
Last Modified:	25 Nov 2023 17:10
URI:	https://orca.cardiff.ac.uk/id/eprint/154490

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