Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement

Mengmeng Wang, Atilla Incecik, Shizhe Feng, M.K. Gupta, Królczyk, Grzegorz and Z Li

Reliability Engineering and System Safety, 2023, vol. 237, issue C

Abstract: A new digital twin (DT) framework with optimal sensor placement (OSP) is proposed to accurately calculate the modal responses and identify the damage ratios of the offshore jacket platforms. The proposed damage identification framework consists of two models (namely one OSP model and one damage identification model). The OSP model adopts the multi-objective Lichtenberg algorithm (MOLA) to perform the sensor number/location optimization to make a good balance between the sensor cost and the modal calculation accuracy. In the damage identification model, the Markov Chain Monte Carlo (MCMC)-Bayesian method is developed to calculate the structural damage ratios based on the modal information obtained from the sensory measurements, where the uncertainties of the structural parameters are quantified. The proposed method is validated using an offshore jacket platform, and the analysis results demonstrate efficient identification of the structural damage location and severity.

Keywords: Optimal sensor placement; Damage identification; Digital twin; Offshore jacket platform (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002508

DOI: 10.1016/j.ress.2023.109336

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